addressed all split notes. Citations file still needs updated and pdflatex is throwing fits about citations

1-goals-and-outcomes/goals.tex

@@ -3,12 +3,12 @@
 % GOAL PARAGRAPH
 The goal of this research is to develop a methodology for creating autonomous
 hybrid control systems with mathematical guarantees of safe and correct
-behavior.\splitnote{Clear thesis statement. Gets right to it.}
+behavior.
 
 % INTRODUCTORY PARAGRAPH Hook
 Nuclear power plants require the highest levels of control system reliability,
 where failures can result in significant economic losses, service interruptions,
-or radiological release.\splitnote{Stakes established immediately — good hook.}
+or radiological release.
 % Known information
 Currently, nuclear plant operations rely on extensively trained human operators
 who follow detailed written procedures and strict regulatory requirements to
@@ -16,22 +16,16 @@ manage reactor control. These operators make critical decisions about when to
 switch between different control modes based on their interpretation of plant
 conditions and procedural guidance.
 % Gap
-This reliance on human operators prevents autonomous control capabilities and
+This reliance on human operators not only prevents autonomous control
-creates a fundamental economic challenge for next-generation reactor 
+capabilities but creates a fundamental economic challenge for next-generation
-designs.\splitsuggest{The ``and'' here joins two distinct issues (autonomy 
+reactor designs. Small modular reactors, in particular, face per-megawatt
-barrier + economics). Consider making the causal link explicit: ``This reliance 
+staffing costs far exceeding those of conventional plants and threaten their
-on human operators not only prevents autonomous control capabilities but also 
+economic viability.
-creates...'' or split into two sentences.}
-Small modular reactors, in particular, face per-megawatt staffing costs far
-exceeding those of conventional plants and threaten their economic viability.
 
 % Critical Need
-What is needed is a method to create autonomous control systems that safely
+Autonomous control systems must safely manage complex operational sequences with
-manage complex operational sequences with the same assurance as human-operated
+the same assurance as human-operated systems, but without constant human
-systems, but without constant human supervision.\splitpolish{``What is needed 
+supervision.
-is'' — Gopen would call this a weak topic position. The sentence buries the 
-subject. Try: ``Autonomous control systems must safely manage complex 
-operational sequences...'' Puts the actor in the topic position.}
 % APPROACH PARAGRAPH Solution
 To address this need, we will combine formal methods with control theory to
 build hybrid control systems that are correct by construction.
@@ -41,33 +35,22 @@ mirroring how operators change control strategies. Existing formal methods can
 generate provably correct switching logic from written requirements, but they
 cannot handle the continuous dynamics that occur during transitions between
 modes. Meanwhile, traditional control theory can verify continuous behavior but
-lacks tools for proving correctness of discrete switching 
+lacks tools for proving correctness of discrete switching
-decisions.\splitnote{Excellent setup of the gap — shows why neither approach 
+decisions. This work is conducted within the University of Pittsburgh Cyber Energy Center,
-alone is sufficient.}
+which provides access to industry collaboration and Emerson control hardware,
+ensuring that developed solutions align with practical implementation
+requirements.
 % Hypothesis
 By synthesizing discrete mode transitions directly from written operating
 procedures and verifying continuous behavior between transitions, we can create
 hybrid control systems with end-to-end correctness guarantees. If existing
 procedures can be formalized into logical specifications and continuous dynamics
 verified against transition requirements, then autonomous controllers can be
-built that are provably free from design 
+built that are provably free from design defects.
-defects.\splitnote{Hypothesis is clear and testable.}
 % Pay-off
 This approach will enable autonomous control in nuclear power plants while
 maintaining the high safety standards required by the industry.
 
-% Qualifications
-This work is conducted within the University of Pittsburgh Cyber Energy Center,
-which provides access to industry collaboration and Emerson control hardware,
-ensuring that developed solutions align with practical implementation
-requirements.\splitsuggest{This qualifications paragraph feels orphaned here. 
-It's important context but reads as an afterthought. Consider integrating it 
-into the approach paragraph (``...demonstrated on Emerson hardware through our 
-partnership with the Cyber Energy Center'') or moving to a ``Why This Will 
-Succeed'' framing later.}
-
-
-
 % OUTCOMES PARAGRAPHS
 If this research is successful, we will be able to do the following:
 
@@ -107,8 +90,7 @@ If this research is successful, we will be able to do the following:
     reactor simulation using industry-standard control hardware. This
     demonstration will prove correctness across multiple coordinated control
     modes from cold shutdown through criticality to power 
-    operation.\splitnote{``cold shutdown through criticality to power 
+    operation.
-    operation'' — concrete and impressive scope.}
     % Outcome
     We will demonstrate that autonomous hybrid control can be realized in the
     nuclear industry with current equipment, establishing a path toward reduced
@@ -119,7 +101,7 @@ If this research is successful, we will be able to do the following:
 % IMPACT PARAGRAPH Innovation
 The innovation in this work is unifying discrete synthesis with continuous
 verification to enable end-to-end correctness guarantees for hybrid 
-systems.\splitnote{Clear ``what's new'' statement.}
+systems.
 % Outcome Impact
 If successful, control engineers will create autonomous controllers from
 existing procedures with mathematical proof of correct behavior. High-assurance
@@ -130,5 +112,4 @@ nuclear power. Small modular reactors offer a promising solution to growing
 energy demands, but their success depends on reducing per-megawatt operating
 costs through increased autonomy. This research will provide the tools to
 achieve that autonomy while maintaining the exceptional safety record the
-nuclear industry requires.\splitnote{Strong closing — ties technical work to 
+nuclear industry requires.
-real-world impact and economic necessity.}

2-state-of-the-art/state-of-art.tex

@@ -6,7 +6,7 @@ first understand how nuclear reactors are operated today. This section examines
 reactor operators and the operating procedures we aim to leverage, then
 investigates limitations of human-based operation, and concludes with current
 formal methods approaches to reactor control 
-systems.\splitnote{Good roadmap — tells reader exactly what's coming.}
+systems.
 
 \subsection{Current Reactor Procedures and Operation}
 
@@ -20,15 +20,7 @@ developed using guidance from NUREG-0899~\cite{NUREG-0899, 10CFR50.34}, but thei
 development relies fundamentally on expert judgment and simulator validation
 rather than formal verification. Procedures undergo technical evaluation,
 simulator validation testing, and biennial review as part of operator
-requalification under 10 CFR 55.59~\cite{10CFR55.59}. Despite this rigor,
+requalification under 10 CFR 55.59~\cite{10CFR55.59}. 
-procedures fundamentally lack formal verification of key safety properties. No
-mathematical proof exists that procedures cover all possible plant states, that
-required actions can be completed within available timeframes, or that
-transitions between procedure sets maintain safety 
-invariants.\splitsuggest{This paragraph is doing a lot. Consider splitting: 
-first paragraph on the hierarchy and compliance, second on the lack of formal 
-verification. The ``No mathematical proof exists...'' sentence is powerful and 
-deserves emphasis.}
 
 \textbf{LIMITATION:} \textit{Procedures lack formal verification of correctness
 and completeness.} Current procedure development relies on expert judgment and
@@ -37,9 +29,7 @@ possible plant states, that required actions can be completed within available
 timeframes, or that transitions between procedure sets maintain safety
 invariants. Paper-based procedures cannot ensure correct application, and even
 computer-based procedure systems lack the formal guarantees that automated
-reasoning could provide.\splitpolish{This repeats the ``No mathematical 
+reasoning could provide.
-proof exists...'' sentence almost verbatim from the paragraph above. Either 
-cut it from the paragraph or from the LIMITATION box.}
 
 Nuclear plants operate with multiple control modes: automatic control, where the
 reactor control system maintains target parameters through continuous reactivity
@@ -59,8 +49,7 @@ actuation, containment isolation, and basic process
 control~\cite{WRPS.Description, gentillon_westinghouse_1999}. Human operators,
 however, retain control of strategic decision-making: power level changes,
 startup/shutdown sequences, mode transitions, and procedure 
-implementation.\splitnote{This is the key insight — the hybrid nature is 
+implementation.
-already there, just not formally verified.}
 
 \subsection{Human Factors in Nuclear Accidents}
 
@@ -75,8 +64,7 @@ operator requires several years of training.
 The persistent role of human error in nuclear safety incidents---despite decades
 of improvements in training and procedures---provides the most compelling
 motivation for formal automated control with mathematical safety 
-guarantees.\splitnote{Strong thesis for this subsection.}
+guarantees. Operators hold legal authority under 10 CFR Part 55 to make critical decisions,
-Operators hold legal authority under 10 CFR Part 55 to make critical decisions,
 including departing from normal regulations during emergencies. The Three Mile
 Island (TMI) accident demonstrated how a combination of personnel error, design
 deficiencies, and component failures led to partial meltdown when operators
@@ -87,8 +75,7 @@ the licensee without formal verification that operators can fulfill this
 responsibility does not guarantee safety. This tension between operational
 flexibility and safety assurance remains unresolved: the person responsible for
 reactor safety is often the root cause of 
-failures.\splitnote{``the person responsible for reactor safety is often the 
+failures.
-root cause of failures'' — devastating summary. Very effective.}
 
 Multiple independent analyses converge on a striking statistic: 70--80\% of
 nuclear power plant events are attributed to human error, versus approximately
@@ -100,15 +87,14 @@ of 190 events at Chinese nuclear power plants from
 2007--2020~\cite{zhang_analysis_2025} found that 53\% of events involved active
 errors, while 92\% were associated with latent errors---organizational and
 systemic weaknesses that create conditions for 
-failure.\splitnote{Strong empirical grounding. The Chinese plant data is a 
+failure.
-nice addition — shows this isn't just a Western regulatory perspective.}
 
 
 \textbf{LIMITATION:} \textit{Human factors impose fundamental reliability limits
 that cannot be overcome through training alone.} The persistent human
 error contribution despite four decades of improvements demonstrates that these
 limitations are fundamental rather than a remediable part of human-driven 
-control.\splitnote{Well-stated. The ``four decades'' point drives it home.}
+control.
 
 \subsection{Formal Methods}
 \subsubsection{HARDENS}
@@ -138,7 +124,7 @@ models of sensors, actuators, and compute infrastructure. Automatic code
 synthesis generated verifiable C implementations and SystemVerilog hardware
 implementations directly from Cryptol models---eliminating the traditional gap
 between specification and implementation where errors commonly 
-arise.\splitnote{Good technical depth on HARDENS toolchain.}
+arise.
 
 Despite its accomplishments, HARDENS has a fundamental limitation directly
 relevant to hybrid control synthesis: the project addressed only discrete
@@ -151,7 +137,7 @@ reactor safety depends on the interaction between continuous
 processes---temperature, pressure, neutron flux---evolving in response to
 discrete control decisions. HARDENS verified the discrete controller in
 isolation but not the closed-loop hybrid system 
-behavior.\splitnote{Clear articulation of the gap your work fills.}
+behavior.
 
 \textbf{LIMITATION:} \textit{HARDENS addressed discrete control logic without
 continuous dynamics or hybrid system verification.} Verifying discrete control
@@ -189,13 +175,11 @@ dynamic logic (dL). dL introduces two additional operators
 into temporal logic: the box operator and the diamond operator. The box operator
 \([\alpha]\phi\) states that for some region \(\phi\), the hybrid system
 \(\alpha\) always remains within that region. In this way, it is a safety
-ivariant being enforced for the system.\splitfix{Typo: ``ivariant'' should be 
+invariant being enforced for the system. The second operator, the diamond
-``invariant''} The second operator, the diamond
 operator \(<\alpha>\phi\) says that for the region \(\phi\), there is at least
 one trajectory of \(\alpha\) that enters that region. This is a declaration of a
-liveness property. 
+liveness property\cite{platzer2018}. 
 
-%source: https://symbolaris.com/logic/dL.html
 
 While dL allows for the specification of these liveness and safety properties,
 actually proving them for a given hybrid system is quite difficult. Automated
@@ -210,12 +194,8 @@ readers a sense of the scaling problem.}
 %gyroscopes overloding and needing to dump speed all the time
 Approaches have been made to alleviate
 these issues for nuclear power contexts using contract and decomposition based
-methods, but are far from a complete methodology to design systems 
+methods, but do not yet constitute a complete design
-with.\splitpolish{``but are far from a complete methodology to design systems 
+methodology\cite{kapuria2025}.%source: Manyu's thesis. 
-with'' — awkward ending preposition. Try: ``but remain far from a complete 
-design methodology'' or ``but do not yet constitute a complete design 
-methodology.''}
-%source: Manyu's thesis. 
 Instead, these approaches have been used on systems that have been designed a
 priori, and require expert knowledge to create the system proofs. 
 
@@ -223,8 +203,9 @@ priori, and require expert knowledge to create the system proofs.
 %very much, so the limitation is that logic based hybrid system approaches have
 %not been used in the DESIGN of autonomous controllers, only in the analysis of
 %systems that already exist.
-\splitinline{Your comment here is spot-on. You should add a LIMITATION box: 
+\textbf{LIMITATION:} \textit{Differential dynamic logic has been used for
-\textit{Differential dynamic logic has been used for post-hoc analysis of 
+post-hoc analysis of existing systems, not for the constructive design of
-existing systems, not for the constructive design of autonomous controllers.} 
+autonomous controllers.} Current formal methods based approaches can in theory
-This is exactly the gap you're filling — you're doing synthesis, not just 
+completely describe the behavior of a hybrid autonomous control system, but in
-verification.}
+practice remain difficult to implement, and have no straightforward application
+to the design of a hybrid autonomous control system.

2-state-of-the-art/v1.tex

@@ -1,165 +0,0 @@
-\section{State of the Art and Limits of Current Practice}
-
-The principal aim of this research is to create autonomous reactor control
-systems that are tractably safe. To understand what is being automated, we must
-first understand how nuclear reactors are operated today. This section examines
-reactor operators and the operating procedures we aim to leverage, then
-investigates limitations of human-based operation, and concludes with current
-formal methods approaches to reactor control systems.
-
-\subsection{Current Reactor Procedures and Operation}
-
-Nuclear plant procedures exist in a hierarchy: normal operating procedures for
-routine operations, abnormal operating procedures for off-normal conditions,
-Emergency Operating Procedures (EOPs) for design-basis accidents, Severe
-Accident Management Guidelines (SAMGs) for beyond-design-basis events, and
-Extensive Damage Mitigation Guidelines (EDMGs) for catastrophic damage
-scenarios. These procedures must comply with 10 CFR 50.34(b)(6)(ii) and are
-developed using guidance from NUREG-0900~\cite{NUREG-0899, 10CFR50.34}, but their
-development relies fundamentally on expert judgment and simulator validation
-rather than formal verification. Procedures undergo technical evaluation,
-simulator validation testing, and biennial review as part of operator
-requalification under 10 CFR 55.59~\cite{10CFR55.59}. Despite this rigor,
-procedures fundamentally lack formal verification of key safety properties. No
-mathematical proof exists that procedures cover all possible plant states, that
-required actions can be completed within available timeframes, or that
-transitions between procedure sets maintain safety invariants.
-
-\textbf{LIMITATION:} \textit{Procedures lack formal verification of correctness
-and completeness.} Current procedure development relies on expert judgment and
-simulator validation. No mathematical proof exists that procedures cover all
-possible plant states, that required actions can be completed within available
-timeframes, or that transitions between procedure sets maintain safety
-invariants. Paper-based procedures cannot ensure correct application, and even
-computer-based procedure systems lack the formal guarantees that automated
-reasoning could provide.
-
-Nuclear plants operate with multiple control modes: automatic control, where the
-reactor control system maintains target parameters through continuous reactivity
-adjustment; manual control, where operators directly manipulate the reactor; and
-various intermediate modes. In typical pressurized water reactor operation, the
-reactor control system automatically maintains a floating average temperature
-and compensates for power demand changes through reactivity feedback loops
-alone. Safety systems, by contrast, operate with implemented automation. Reactor
-Protection Systems trip automatically on safety signals with millisecond
-response times, and engineered safety features actuate automatically on accident
-signals without operator action required.
-
-The division between automated and human-controlled functions reveals the
-fundamental challenge of hybrid control. Highly automated systems handle reactor
-protection---automatic trips on safety parameters, emergency core cooling
-actuation, containment isolation, and basic process
-control~\cite{WRPS.Description, gentillon_westinghouse_1999}. Human operators,
-however, retain control of strategic decision-making: power level changes,
-startup/shutdown sequences, mode transitions, and procedure implementation.
-
-\subsection{Human Factors in Nuclear Accidents}
-
-Current-generation nuclear power plants employ over 3,600 active NRC-licensed
-reactor operators in the United States~\cite{operator_statistics}. These
-operators divide into Reactor Operators (ROs), who manipulate reactor controls,
-and Senior Reactor Operators (SROs), who direct plant operations and serve as
-shift supervisors~\cite{10CFR55}. Staffing typically requires at least two ROs
-and one SRO for current-generation units~\cite{10CFR50.54}. Becoming a reactor
-operator requires several years of training.
-
-The persistent role of human error in nuclear safety incidents---despite decades
-of improvements in training and procedures---provides the most compelling
-motivation for formal automated control with mathematical safety guarantees.
-Operators hold legal authority under 10 CFR Part 55 to make critical decisions,
-including departing from normal regulations during emergencies. The Three Mile
-Island (TMI) accident demonstrated how a combination of personnel error, design
-deficiencies, and component failures led to partial meltdown when operators
-misread confusing and contradictory readings and shut off the emergency water
-system~\cite{Kemeny1979}. The President's Commission on TMI identified a
-fundamental ambiguity: placing responsibility for safe power plant operations on
-the licensee without formal verification that operators can fulfill this
-responsibility does not guarantee safety. This tension between operational
-flexibility and safety assurance remains unresolved: the person responsible for
-reactor safety is often the root cause of failures.
-
-Multiple independent analyses converge on a striking statistic: 70--80\% of
-nuclear power plant events are attributed to human error, versus approximately
-20\% to equipment failures~\cite{WNA2020}. More significantly, the root cause of
-all severe accidents at nuclear power plants---Three Mile Island, Chernobyl, and
-Fukushima Daiichi---has been identified as poor safety management and safety
-culture: primarily human factors~\cite{hogberg_root_2013}. A detailed analysis
-of 190 events at Chinese nuclear power plants from
-2007--2020~\cite{zhang_analysis_2025} found that 53\% of events involved active
-errors, while 92\% were associated with latent errors---organizational and
-systemic weaknesses that create conditions for failure.
-
-
-\textbf{LIMITATION:} \textit{Human factors impose fundamental reliability limits
-that cannot be overcome through training alone.} The persistent human
-error contribution despite four decades of improvements demonstrates that these
-limitations are fundamental rather than a remediable part of human-driven control.
-
-\subsection{HARDENS and Formal Methods}
-
-The High Assurance Rigorous Digital Engineering for Nuclear Safety (HARDENS)
-project represents the most advanced application of formal methods to nuclear
-reactor control systems to date~\cite{Kiniry2024}.
-
-HARDENS aimed to address a fundamental dilemma: existing U.S. nuclear control
-rooms rely on analog technologies from the 1950s--60s. This technology is
-obsolete compared to modern control systems and incurs significant risk and
-cost. The NRC contracted Galois, a formal methods firm, to demonstrate that
-Model-Based Systems Engineering and formal methods could design, verify, and
-implement a complex protection system meeting regulatory criteria at a fraction
-of typical cost. The project delivered a Reactor Trip System (RTS)
-implementation with full traceability from NRC Request for Proposals and IEEE
-standards through formal architecture specifications to verified software.
-
-HARDENS employed formal methods tools and techniques across the verification
-hierarchy. High-level specifications used Lando, SysMLv2, and FRET (NASA Formal
-Requirements Elicitation Tool) to capture stakeholder requirements, domain
-engineering, certification requirements, and safety requirements. Requirements
-were analyzed for consistency, completeness, and realizability using SAT and SMT
-solvers. Executable formal models used Cryptol to create a behavioral model of
-the entire RTS, including all subsystems, components, and limited digital twin
-models of sensors, actuators, and compute infrastructure. Automatic code
-synthesis generated verifiable C implementations and SystemVerilog hardware
-implementations directly from Cryptol models---eliminating the traditional gap
-between specification and implementation where errors commonly arise.
-
-Despite its accomplishments, HARDENS has a fundamental limitation directly
-relevant to hybrid control synthesis: the project addressed only discrete
-digital control logic without modeling or verifying continuous reactor dynamics.
-The Reactor Trip System specification and verification covered discrete state
-transitions (trip/no-trip decisions), digital sensor input processing through
-discrete logic, and discrete actuation outputs (reactor trip commands). The
-project did not address continuous dynamics of nuclear reactor physics. Real
-reactor safety depends on the interaction between continuous
-processes---temperature, pressure, neutron flux---evolving in response to
-discrete control decisions. HARDENS verified the discrete controller in
-isolation but not the closed-loop hybrid system behavior.
-
-\textbf{LIMITATION:} \textit{HARDENS addressed discrete control logic without
-continuous dynamics or hybrid system verification.} Verifying discrete control
-logic alone provides no guarantee that the closed-loop system exhibits desired
-continuous behavior such as stability, convergence to setpoints, or maintained
-safety margins.
-
-HARDENS produced a demonstrator system at Technology Readiness Level 2--3
-(analytical proof of concept with laboratory breadboard validation) rather than
-a deployment-ready system validated through extended operational testing. The
-NRC Final Report explicitly notes~\cite{Kiniry2024} that all material is
-considered in development, not a finalized product, and that ``The demonstration
-of its technical soundness was to be at a level consistent with satisfaction of
-the current regulatory criteria, although with no explicit demonstration of how
-regulatory requirements are met.'' The project did not include deployment in
-actual nuclear facilities, testing with real reactor systems under operational
-conditions, side-by-side validation with operational analog RTS systems,
-systematic failure mode testing (radiation effects, electromagnetic
-interference, temperature extremes), NRC licensing review, or human factors
-validation with licensed operators in realistic control room scenarios.
-
-\textbf{LIMITATION:} \textit{HARDENS achieved TRL 2--3 without experimental
-validation.} While formal verification provides mathematical correctness
-guarantees for the implemented discrete logic, the gap between formal
-verification and actual system deployment involves myriad practical
-considerations: integration with legacy systems, long-term reliability
-under harsh environments, human-system interaction in realistic
-operational contexts, and regulatory acceptance of formal methods as
-primary assurance evidence.

3-research-approach/approach.tex

@@ -30,7 +30,7 @@ The challenge of hybrid system verification lies in the interaction between
 discrete and continuous dynamics. Discrete transitions change the governing
 vector field, creating discontinuities in the system's behavior. Traditional
 verification techniques designed for purely discrete or purely continuous
-systems cannot handle this interaction directly.\splitpolish{Missing space before ``Our} Our methodology addresses this
+systems cannot handle this interaction directly. Our methodology addresses this
 challenge through decomposition. We verify discrete switching logic and
 continuous mode behavior separately, then compose these guarantees to reason
 about the complete hybrid system. This two-layer approach mirrors the structure
@@ -47,7 +47,7 @@ This means that the system does not have external input and that continuous
 states do not change instantaneously when discrete states change. For our
 systems of interest, the continuous states are physical quantities that are
 always Lipschitz continuous. This nomenclature is borrowed from the Handbook on
-Hybrid Systems Control \cite{HANDBOOK ON HYBRID SYSTEMS}, but is redefined here
+Hybrid Systems Control \cite{lunze2009}, but is redefined here
 for convenience:
 
 \begin{equation}
@@ -75,12 +75,13 @@ The creation of a HAHACS amounts to the construction of such a tuple together
 with proof artifacts demonstrating that the intended behavior of the control
 system is satisfied by its actual implementation. This approach is tractable now
 because the infrastructure for each component has matured. The novelty is not in
-the individual pieces, but in the architecture that connects them.\splitnote{This is your key insight — the novelty is compositional, not component-level.} By defining
+the individual pieces, but in the architecture that connects them. 
-entry, exit, and safety conditions at the discrete level first, we transform the
+
-intractable problem of global hybrid verification into a collection of local
+By defining entry, exit, and safety conditions at the discrete level first, we
-verification problems with clear interfaces. Verification is performed per mode
+transform the intractable problem of global hybrid verification into a
-rather than on the full hybrid system, keeping the analysis tractable even for
+collection of local verification problems with clear interfaces. Verification is
-complex reactor operations.
+performed per mode rather than on the full hybrid system, keeping the analysis
+tractable even for complex reactor operations.
 
 \begin{figure} 
   \centering
@@ -293,16 +294,8 @@ implementation. Second, it clearly demonstrates where natural language documents
 are insufficient. These procedures may still be used by human operators, so any
 room for interpretation is a weakness that must be addressed.
 
-(Some examples of where FRET has been used and why it will be successful here)
+\splitinline{Some examples of where FRET has been used and why it will be successful
-%%% NOTES (Section 2):
+here}
-% - Add concrete FRET example showing requirement → FRETish → LTL
-% - Discuss hysteresis and how to prevent mode chattering near boundaries
-% - Address sensor noise and measurement uncertainty in threshold definitions
-% - Consider numerical precision issues when creating discrete automata
-
-% ----------------------------------------------------------------------------
-% 3. DISCRETE CONTROLLER SYNTHESIS
-% ----------------------------------------------------------------------------
 
 Once system requirements are defined as temporal logic specifications, we use
 them to build the discrete control system. To do this, reactive synthesis tools
@@ -339,19 +332,10 @@ system using temporal logics and synthesizing the controller using deterministic
 algorithms, we are assured that strategic decisions will always be made
 according to operating procedures.
 
-(Talk about how one would go from a discrete automaton to actual code)
+\splitinline{Talk about how one would go from a discrete automaton to actual
+code}
 
-(Examples of reactive synthesis in the wild)
+\splitinline{Examples of reactive synthesis in the wild}
-
-%%% NOTES (Section 3):
-% - Mention computational complexity of synthesis (doubly exponential worst case)
-% - Discuss how specification structure affects synthesis tractability
-% - Reference GR(1) fragment as a tractable subset commonly used in practice
-% - May want to include an example automaton figure
-
-% ----------------------------------------------------------------------------
-% 4. CONTINUOUS CONTROLLERS
-% ----------------------------------------------------------------------------
 
 \subsection{Continuous Control Modes}
 
@@ -380,7 +364,7 @@ continuous state space among several discrete vector fields has traditionally
 been a difficult problem for validation and verification. The discontinuity of
 the vector fields at discrete state interfaces makes reachability analysis
 computationally expensive, and analytic solutions often become intractable
-\cite{MANYUS THESIS}.
+\cite{kapuria2025, lang2021}.
 
 We circumvent these issues by designing our hybrid system from the bottom up
 with verification in mind. Each continuous control mode has an input set and
@@ -404,18 +388,9 @@ $q_i$ must drive the system from any state in $\mathcal{X}_{entry,i}$ to some
 state in $\mathcal{X}_{exit,i}$ while remaining within $\mathcal{X}_{safe,i}$.
 
 We classify continuous controllers into three types based on their objectives:
-transitory, stabilizing, and expulsory.\splitnote{This three-mode taxonomy is elegant — maps verification tools to control objectives cleanly.} Each type has distinct verification
+transitory, stabilizing, and expulsory. Each type has distinct verification
 requirements that determine which formal methods tools are appropriate.
 
-%%% NOTES (Section 4):
-% - Add figure showing the relationship between entry/exit/safety sets
-% - Discuss how standard control techniques (LQR, MPC, PID) fit into this framework
-% - Mention assume-guarantee reasoning for compositional verification
-
-% ----------------------------------------------------------------------------
-% 4.1 TRANSITORY MODES
-% ----------------------------------------------------------------------------
-
 \subsubsection{Transitory Modes}
 
 Transitory modes are continuous controllers designed to move
@@ -466,16 +441,6 @@ systems require more conservative over-approximations using techniques such as
 Taylor models or polynomial zonotopes. For this work, we will select tools
 appropriate to the fidelity of the reactor models available.
 
-%%% NOTES (Section 4.1):
-% - Add timing constraints discussion: what if the transition takes too long?
-% - Consider timed reachability for systems with deadline requirements
-% - Mention that the Mealy machine perspective unifies this: continuous system
-%   IS the transition, entry/exit conditions are the discrete states
-
-% ----------------------------------------------------------------------------
-% 4.2 STABILIZING MODES
-% ----------------------------------------------------------------------------
-
 \subsubsection{Stabilizing Modes}
 
 Stabilizing modes are continuous controllers with an objective of maintaining a
@@ -523,16 +488,6 @@ For example, a lower fidelity model can be used for controller design, but a
 higher fidelity model can be used for the actual validation of that stabilizing
 controller.
 
-%%% NOTES (Section 4.2):
-% - Clarify relationship between barrier certificates and Lyapunov stability
-% - Discuss what happens at mode boundaries: barrier for this mode vs guard
-%   for transition
-% - Mention tools: SOSTOOLS, dReal, barrier function synthesis methods
-
-% ----------------------------------------------------------------------------
-% 4.3 EXPULSORY MODES
-% ----------------------------------------------------------------------------
-
 \subsubsection{Expulsory Modes}
 
 Expulsory modes are continuous controllers responsible for
@@ -582,16 +537,6 @@ plant dynamics. The expulsory mode must handle the worst-case dynamics within
 this envelope. This is where conservative controller design is appropriate as
 safety margins will matter more than performance during emergency shutdown.
 
-%%% NOTES (Section 4.3):
-% - Discuss sensor failures vs actual plant failures
-% - Address unmodeled disturbances that aren't failures
-% - How much parametric uncertainty is enough? Need methodology for bounds
-% - Mention graceful degradation: graded responses vs immediate SCRAM
-
-% ----------------------------------------------------------------------------
-% 5. INDUSTRIAL IMPLEMENTATION
-% ----------------------------------------------------------------------------
-
 \subsection{Industrial Implementation}
 
 The methodology described above must be validated on realistic
@@ -619,17 +564,9 @@ the success and impact of this work. We will directly address the gap of
 verification and validation methods for these systems and industry adoption by
 forming a two-way exchange of knowledge between the laboratory and commercial
 environments. This work stands to be successful with Emerson implementation
-because we will have access to system experts\splitfix{Typo: ``excess should be ``access} at Emerson to help with the fine
+because we will have access to system experts at Emerson to help with the fine
 details of using the Ovation system. At the same time, we will have the benefit
 of transferring technology directly to industry with a direct collaboration in
 this research, while getting an excellent perspective of how our research
 outcomes can align best with customer needs.
 
-%%% NOTES (Section 5):
-% - Get specific details on ARCADE interface from Emerson collaboration
-% - Mention what startup sequence will be demonstrated (cold shutdown →
-%   criticality → low power?)
-% - Discuss how off-nominal scenarios will be tested (sensor failures,
-%   simulated component degradation)
-% - Reference Westinghouse relationship if relevant
-

3-research-approach/v1.tex

@@ -1,285 +0,0 @@
-\section{Research Approach}
-
-This research will overcome the limitations of current practice to build
-high-assurance hybrid control systems for critical infrastructure. Building
-these systems with formal correctness guarantees requires three main thrusts:
-
-\begin{enumerate}
-  \item Translate operating procedures and requirements into temporal logic
-    formulae
-
-  \item Create the discrete half of a hybrid controller using reactive synthesis
-
-  \item Develop continuous controllers to operate between modes, and verify
-    their correctness
-
-\end{enumerate}
-
-Commercial nuclear power operations remain manually controlled by human
-operators, yet the procedures they follow are highly prescriptive and
-well-documented. This suggests that human operators may not be entirely
-necessary given current technology. Written procedures and requirements are
-sufficiently detailed that they may be translatable into logical formulae with
-minimal effort. If successful, this approach enables automation of existing
-procedures without system reengineering. To formalize these procedures, we will
-use temporal logic, which captures system behaviors through temporal relations.
-
-The most efficient path for this translation is NASA's Formal Requirements
-Elicitation Tool (FRET). FRET employs a specialized requirements language called
-FRETish that restricts requirements to easily understood components while
-eliminating ambiguity~\cite{katis_capture_2022}. FRETish bridges natural language
-and mathematical specifications through a structured English-like syntax
-automatically translatable to temporal logic.
-
-FRET enforces this structure by requiring all requirements to contain six
-components: %CITE FRET MANUAL
-
-\begin{enumerate}
-  \item Scope: \textit{What modes does this requirement apply to?}
-  \item Condition: \textit{Scope plus additional specificity}
-  \item Component: \textit{What system element does this requirement affect?}
-  \item Shall
-  \item Timing: \textit{When does the response occur?}
-  \item Response: \textit{What action should be taken?}
-\end{enumerate}
-
-FRET provides functionality to check system \textit{realizability}. Realizability
-analysis determines whether written requirements are complete by examining the
-six structural components. Complete requirements neither conflict with one
-another nor leave any behavior undefined. Systems that are not realizable from
-their procedure definitions and design requirements present problems beyond
-autonomous control implementation. Such systems contain behavioral
-inconsistencies---the physical equivalent of software bugs. Using FRET during
-autonomous controller development allows systematic identification and
-resolution of these errors.
-
-The second category of realizability issues involves undefined behaviors
-typically left to human judgment during operations. This ambiguity is
-undesirable for high-assurance systems, since even well-trained humans remain
-prone to errors. Addressing these specification gaps in FRET during development
-yields controllers free from these vulnerabilities.
-
-FRET exports requirements in temporal logic format compatible with reactive
-synthesis tools. Linear Temporal Logic (LTL) builds upon modal logic's
-foundational operators for necessity ($\Box$, ``box'') and possibility
-($\Diamond$, ``diamond''), extending them to reason about temporal
-behavior~\cite{baier_principles_2008}. The box operator $\Box$ expresses that a
-property holds at all future times (necessarily always), while the diamond
-operator $\Diamond$ expresses that a property holds at some future time
-(possibly eventually). These are complemented by the next operator ($X$) for the
-immediate successor state and the until operator ($U$) for expressing
-persistence conditions.
-
-Consider a nuclear reactor SCRAM requirement expressed in natural language:
-\textit{``If a high temperature alarm triggers, control rods must immediately
-insert and remain inserted until operator reset.''} This plain language
-requirement can be translated into a rigorous logical specification:
-
-\begin{equation}
-  \Box(HighTemp \rightarrow X(RodsInserted \wedge (\neg
-  RodsWithdrawn\ U\ OperatorReset)))
-\end{equation}
-
-This specification precisely captures the temporal relationship between the
-alarm condition, the required response, and the persistence requirement. The
-necessity operator $\Box$ ensures this safety property holds throughout all
-possible future system executions, while the next operator $X$ enforces
-immediate response. The until operator $U$ maintains the state constraint until
-the reset condition occurs. No ambiguity exists in this scenario because all
-decisions are represented by discrete variables. Formulating operating rules in
-this logic enforces finite, correct operation.
-
-Reactive synthesis is an active research field focused on generating discrete
-controllers from temporal logic specifications. The term ``reactive'' indicates
-that the system responds to environmental inputs to produce control outputs.
-These synthesized systems are finite, with each node representing a unique
-discrete state. The connections between nodes, called \textit{state
-transitions}, specify the conditions under which the discrete controller moves
-from state to state. This complete mapping of possible states and transitions
-constitutes a \textit{discrete automaton}. Discrete automata can be represented
-graphically as nodes (discrete states) with edges indicating transitions between
-them. From the automaton graph, one can fully describe discrete system dynamics
-and develop intuitive understanding of system behavior. Hybrid systems naturally
-exhibit discrete behavior amenable to formal analysis through these finite state
-representations.
-
-We will employ state-of-the-art reactive synthesis tools, particularly Strix,
-which has demonstrated superior performance in the Reactive Synthesis
-Competition (SYNTCOMP) through efficient parity game solving
-algorithms~\cite{meyer_strix_2018,jacobs_reactive_2024}. Strix translates linear
-temporal logic specifications into deterministic automata automatically while
-maximizing generated automata quality. Once constructed, the automaton can be
-implemented using standard programming control flow constructs. The graphical
-representation enables inspection and facilitates communication with controls
-programmers who lack formal methods expertise.
-
-We will use discrete automata to represent the switching behavior of our hybrid
-system. This approach yields an important theoretical guarantee: because the
-discrete automaton is synthesized entirely through automated tools from design
-requirements and operating procedures, the automaton---and therefore our hybrid
-switching behavior---is \textit{correct by construction}. Correctness of the
-switching controller is paramount. Mode switching represents the primary
-responsibility of human operators in control rooms today. Human operators
-possess the advantage of real-time judgment: when mistakes occur, they can
-correct them dynamically with capabilities extending beyond written procedures.
-Autonomous control lacks this adaptive advantage. Instead, autonomous
-controllers replacing human operators must not make switching errors between
-continuous modes. Synthesizing controllers from logical specifications with
-guaranteed correctness eliminates the possibility of switching errors.
-
-While discrete system components will be synthesized with correctness
-guarantees, they represent only half of the complete system. Autonomous
-controllers like those we are developing exhibit continuous dynamics within
-discrete states. These systems, called hybrid systems, combine continuous
-dynamics (flows) with discrete transitions (jumps). These dynamics can be
-formally expressed as~\cite{branicky_multiple_1998}:
-
-\begin{equation}
-\dot{x}(t) = f(x(t),q(t),u(t))
-\end{equation}
-
-\begin{equation}
-q(k+1) = \nu(x(k),q(k),u(k))
-\end{equation}
-
-Here, $f(\cdot)$ defines the continuous dynamics while $\nu(\cdot)$ governs
-discrete transitions. The continuous states $x$, discrete state $q$, and
-control input $u$ interact to produce hybrid behavior. The discrete state $q$
-defines which continuous dynamics mode is currently active. Our focus centers
-on continuous autonomous hybrid systems, where continuous states remain
-unchanged during jumps---a property naturally exhibited by physical systems. For
-example, a nuclear reactor switching from warm-up to load-following control
-cannot instantaneously change its temperature or control rod position, but can
-instantaneously change control laws.
-
-The approach described for producing discrete automata yields physics-agnostic
-specifications representing only half of a complete hybrid autonomous
-controller. These automata alone cannot define the full behavior of the control
-systems we aim to construct. The continuous modes will be developed after
-discrete automaton construction, leveraging the automaton structure and
-transitions to design multiple smaller, specialized continuous controllers.
-
-Notably, translation into linear temporal logic creates barriers between
-different control modes. Switching from one mode to another becomes a discrete
-boolean variable. \(RodsInserted\) or \(HighTemp\) in the temporal
-specifications are booleans, but in the real system they represent physical
-features in the state space. These features mark where continuous control modes
-end and begin; their definition is critical for determining which control mode
-is active at any given time. Information about where in the state space these
-conditions exist will be preserved from the original requirements and included
-in continuous control mode development, but will not appear as numeric values in
-discrete mode switching synthesis.
-
-The discrete automaton transitions are key to the supervisory behavior of the
-autonomous controller. These transitions mark decision points for switching
-between continuous control modes and define their strategic objectives. We
-will classify three types of high-level continuous controller objectives based
-on discrete mode transitions:
-
-\begin{enumerate}
-  \item \textbf{Stabilizing:} A stabilizing control mode has one primary
-    objective: maintaining the hybrid system within its current discrete mode.
-    This corresponds to steady-state normal operating modes, such as a
-    full-power load-following controller in a nuclear power plant. Stabilizing
-    modes can be identified from discrete automata as nodes with only incoming
-    transitions.
-
-  \item \textbf{Transitory:} A transitory control mode has the primary goal of
-    transitioning the hybrid system from one discrete state to another. In
-    nuclear applications, this might represent a controlled warm-up procedure.
-    Transitory modes ultimately drive the system toward a stabilizing
-    steady-state mode. These modes may have secondary objectives within a
-    discrete state, such as maintaining specific temperature ramp rates before
-    reaching full-power operation.
-
-  \item \textbf{Expulsory:} An expulsory mode is a specialized transitory mode
-    with additional safety constraints. Expulsory modes ensure the system is
-    directed to a safe stabilizing mode during failure conditions. For example,
-    if a transitory mode fails to achieve its intended transition, the
-    expulsory mode activates to immediately and irreversibly guide the system
-    toward a globally safe state. A reactor SCRAM exemplifies an expulsory
-    continuous mode: when initiated, it must reliably terminate the nuclear
-    reaction and direct the reactor toward stabilizing decay heat removal.
-
-\end{enumerate}
-
-Building continuous modes after constructing discrete automata enables local
-controller design focused on satisfying discrete transitions. The primary
-challenge in hybrid system verification is ensuring global stability across
-transitions~\cite{branicky_multiple_1998}. Current techniques struggle with this
-problem because dynamic discontinuities complicate
-verification~\cite{bansal_hamilton-jacobi_2017,guernic_reachability_2009}. This
-work alleviates these problems by designing continuous controllers specifically
-with transitions in mind. Decomposing continuous modes according to their
-required behavior at transition points avoids solving trajectories through the
-entire hybrid system. Instead, local behavior information at transition
-boundaries suffices. To ensure continuous modes satisfy their requirements, we
-employ three main techniques: reachability analysis, assume-guarantee contracts,
-and barrier certificates.
-
-Reachability analysis computes the reachable set of states for a given input
-set. While trivial for linear continuous systems, recent advances have extended
-reachability to complex nonlinear
-systems~\cite{frehse_spaceex_2011,mitchell_time-dependent_2005}. We use
-reachability to define continuous state ranges at discrete transition boundaries
-and verify that requirements are satisfied within continuous modes.
-Assume-guarantee contracts apply when continuous state boundaries are not
-explicitly defined. For any given mode, the input range for reachability
-analysis is defined by the output ranges of discrete modes that transition to
-it. This compositional approach ensures each continuous controller is prepared
-for its possible input range, enabling reachability analysis without global
-system analysis. Finally, barrier certificates prove that mode transitions are
-satisfied. Barrier certificates ensure that continuous modes on either side of a
-transition behave appropriately by preventing system trajectories from crossing
-a given barrier. Control barrier functions certify safety by establishing
-differential inequality conditions that guarantee forward invariance of safe
-sets~\cite{prajna_safety_2004}. For example, a barrier certificate can guarantee
-that a transitory mode transferring control to a stabilizing mode will always
-move away from the transition boundary, rather than destabilizing the target
-stabilizing mode.
-
-This compositional approach has several advantages. First, this approach breaks
-down autonomous controller design into smaller pieces. For designers of future
-autonomous control systems, the barrier to entry is low, and design milestones
-are clear due to the procedural nature of this research plan. Second, measurable
-design progress also enables measurement of regulatory adherence. Each step in
-this development procedure generates an artifact that can be independently
-evaluated as proof of safety and performance. Finally, the compositional nature
-of this development plan enables incremental refinement between control system
-layers. For example, difficulty developing a continuous mode may reflect a
-discrete automaton that is too restrictive, prompting refinement of system
-design requirements. This synthesis between levels promotes broader
-understanding of the autonomous controller.
-
-To demonstrate this methodology, we will develop an autonomous startup
-controller for a Small Modular Advanced High Temperature Reactor (SmAHTR). We
-have already developed a high-fidelity SmAHTR model in Simulink that captures
-the thermal-hydraulic and neutron kinetics behavior essential for verifying
-continuous controller performance under realistic plant dynamics. The
-synthesized hybrid controller will be implemented on an Emerson Ovation control
-system platform, representative of industry-standard control hardware deployed
-in modern nuclear facilities. The Advanced Reactor Cyber Analysis and
-Development Environment (ARCADE) suite will serve as the integration layer,
-managing real-time communication between the Simulink simulation and the Ovation
-controller. This hardware-in-the-loop configuration enables validation of the
-controller implementation on actual industrial control equipment interfacing
-with a realistic reactor simulation, assessing computational performance,
-real-time execution constraints, and communication latency effects.
-Demonstrating autonomous startup control on this representative platform will
-establish both the theoretical validity and practical feasibility of the
-synthesis methodology for deployment in actual small modular reactor systems.
-
-This unified approach addresses a fundamental gap in hybrid system design by
-bridging formal methods and control theory through a systematic, tool-supported
-methodology. Translating existing nuclear procedures into temporal logic,
-synthesizing provably correct discrete switching logic, and developing verified
-continuous controllers creates a complete framework for autonomous hybrid
-control with mathematical guarantees. The result is an autonomous controller
-that not only replicates human operator decision-making but does so with formal
-assurance that switching logic is correct by construction and continuous
-behavior satisfies safety requirements. This methodology transforms nuclear
-reactor control from a manually intensive operation requiring constant human
-oversight into a fully autonomous system with higher reliability than
-human-operated alternatives. More broadly, this approach establishes a
-replicable framework for developing high-assurance autonomous controllers in any
-domain where operating procedures are well-documented and safety is paramount.

3-research-approach/v2.tex

@@ -1,578 +0,0 @@
-\section{Research Approach}
-\iffalse
-
-  HACS: hybrid autonomous control system
-  HAHACS: High-Assurance Hybrid AUtonomous Control System
-
-
-The research approach here needs to clearly outline the solution the the problem
-and identify the actions taken that will advance knowledge and solve the
-problem.
-
-First, what is the problem? 
-
-\textit{ 
-
-  Inhibition to adopt hybrid autonomous control in critical infrastructure is
-  rooted in safety concerns of system stability. Without a human in the loop
-  with general intelligence, HACS have not been trusted where failure modes can
-  be unique and novel.
-
-}
-
-So, what's the solution?
-
-\textit{
-
-  This research approach develops a methodology to build HACS that are provably
-  safe. This methodology builds on existing technologies, and unifies different
-  research thrusts to build a complete hybrid control system. To do this, the
-  problem of a HAHCS is broken into three distinct pieces:
-
-  \begin{enumerate}
-
-    \item System specification: properties of the HAHaCS such as transition
-      between control modes and system invariants are specified using a formal
-      methods tool. 
-        - This provides exact behavior
-        - allows realizabillity checking of controller specs. Can a controller
-        actually be built from these specs?
-        - ?
-        - ?
-
-    \item Discrete Behavior Synthesis: The discrete component of the controller
-      is synthesized directly from system specifications using reactive
-      synthesis.
-        - This ELIMINATES wholesale the possibility of introducing logical bugs
-        in the creation of the strategic part of the HAHCS. Critical decisions
-        that are normally made by a human are automated directly from the
-        formal specifications.
-        - This does two critical things:
-          - It makes the creation of the controller tractable. The reasons the
-          controller changes between modes acn be traced back to the
-          specification (and thus any requirements), which is a trace for
-          liability and justification of system behavior
-          - Discrete control decisions made by humans are reliant on the human
-          operator operating correctly. Humans are intrinsically probabalistic
-          creatures who cannot eliminate human error. By defining the behavior
-          of this system using temporal logics and synthesizing the controller
-          using deterministic algorithims, we are assured that strategic
-          decisions will always be made as according to operating procedures.
-
-    \item Continuous Behavior Synthesis and Verification: The continuous
-      components of the controller are built using existing dynamics and control
-      theory but then verified using reachability and barrier certificats.
-        - It's very challenging (nigh impossible) to say for certain how to
-        build any continuous control mode. That is honestly going to be have to
-        left to the specific control system and its objectives. It's not really
-        the point of this PhD to say how to do that. For that reason, I'm going
-        to assume that controllers between modes are generally possible to
-        build. That is to say that there exists a controller that can transition
-        between modes, but it is a human hunt to find it.
-        - To check if a candidate controller does transition between discrete
-        modes, we do two things:
-          - Check invariants using reachability. Specifications will require
-          that control modes transiiton from one mode to the next, where
-          appropriate. When this is the case, these invariants are extracted to
-          be checked using reachability. The control mode is given the possible
-          entry conditions of the 'entry' mode, and the possible 'exit' states
-          are analyzed. A cont. controller passes this reachability test if
-          there is no reachable state that is not at the exit condition of the
-          state transition. 
-
-          --- This needs flushed out more. I think this can really be clarified
-          using entry and exit conditions of Mealy machines. The continuous
-          system IS the transition, and the reachabililty test is saying whether
-          or not the physical system actually satisfies the entry and exit
-          conditions. 
-
-          - Then, for systems that need to STAY within one mode, we will use
-          barrier certificates. These can let us define a continuous state
-          boundary, and define for a discrete controller state, the total
-          controller will NOT leave the continuous boundary.
-
-        - One thing that must be considered is the idea that this analysis is
-        predicated on the physical system being correct to the model. If this
-        isn't true, we must define continuous modes that catch failure states.
-        If transition invariants are violated, we must shut down the system, and
-        build safety oriented control modes that we can be sure with a much
-        broader set of entry conditions will safely shut down the plant.
-
-      -- Q for dan: is it critical to really have software to namedrop or is it
-      better to stay amorphous on the technology? Iirc Manyu did a little bit of
-      both.
-
-  \end{enumerate}
-
-
-  What's the intellectual merit?
-
-  \textit{
-
-    There is no outstanding way to build HAHACS. This methodology provides a
-    basis for systems engineers to think about the components of a HAHACS as
-    interlocking pieces whos verification interlinks into a broader system.
-    This will also motivate the adoption of temporal logic to define autonomous
-    control systems, by allowing a close connection and tracability between
-    requirements from regulations to system specifications.
-
-  }
-
-}
-
-Some thoughts on invariants, and how they fit here: There are several types of
-safety invariants that HAHACS might have. 
-
-1. Conditions that initiate a switch between control modes (reactiive synthesis
-relevant) 
-
-2. Invariants about the stability of discrete states (barrier certificates) 
-
-3. Invariants ensuring the transition between discrete states (reachability) 
-
-4. Invariants about the timeliness of discrete transitions (??? Reachability?)
-
-How do we reason about all of these invariants. Well, fundamentally they can
-all be reasoned about with temporal logic statements. Using next and eventually
-operators, we can get to the fundamental behavior of all of these modes. What's
-challenging is the fact that we ensure that all of these specifications are
-validated differs between the type of invariant. This is really the beauty of
-this approach, and the intellectual merit. This proposal provides a way for
-hybrid control systems to be verified for autonomous control systems by
-diversifying the way that the invariants are checked. 
-
-Reactive synthesis helps us build discrete controllers using specifications
-that have conditons that don't depend on time. These invariants generally are
-strategic decisions, such as changing between operating modes, initiating power
-level changes, or perhaps doing a refueling or shutdown routine. These
-specifications are able to be nearly directly drawn from operating procedures,
-and should be closely tied to instructions that would be used for human
-operators. They have checkpoints for the continuous system in between different
-control implements. An example is, raise power at a certain rate while ensure
-temperature remains between certain bounds. These conditions are physical
-states, but they are a binary result. The condition is really binary, desipite
-perhaps having units of celsius or %power. When we build discrete controllers
-from these specifications, we get the validation of the controller of these
-specs for free by nature of reactive synthesis tools. We get direct
-traceability from the operating procedure to the discrete controller
-implementation with minimal human effort.
-
-That being said, there are no free lunches here. Ultimately, we're controlling
-physical systems, and while we can automate the controller building between
-stratgic objectives, it is not trivial to do so for the controller of the
-physical process. These controllers are going to have to be built manually,
-with the continuous dynamics of the system in mind. Helpfully, if
-specifications are complete first, one can obtain discrete controller before
-building physical controllers. The result of this is a simplification of
-controller design, becuase the operational goals of each continuous controller
-is clearly outlined by the invariants that define the goal of each discrete
-mode. While for reactive synthesis purposes conditions such as a certain
-temperature being reached or power level attained are binary variables, the
-continuous physical meaning becomes important in the design and analysis of the
-physical controllers. The continuous value of these conditions becomes the goal
-of the continuous controller design, while also providing a basis to check
-controller performance.
-
-To check continuous controllers are valid, we can split continuous controller
-objectives into two types. First, we have continuous controllers that are
-designed to move the plant between two different discrete modes. These will be
-called 'transitory' controllers, because their entire purpose is to transition
-the plant betweeen between discrete control modes. Because of the specification
-of the hybrid control system a priori, we will have defined what the invariants
-of these transitions are in continuous state space. Then, once a continuosu
-controller design is developed, it can be validated using reachability
-analysis. The input set for the analysis is the possible states that enter this
-transitory mode, while the reachable states must be entirely contained within
-the exit invariant for the controller to pass. At the time of writing this
-proposal, it is not clear what the most efficient way to obetain this
-continuous controller is, but is generally beyond the scope of this work. It is
-assumed that they generally won't be so difficult to find for most systems, as
-the refinement of the discrete controller should simplify the control
-objectives of the physical controllers significantly.
-
-The second type of continuous controller that may be utilized in a HAHAHCS is a
-controller that tries to maintaine a continuous steady state, such that no
-discrete transitions are triggered. Reachability on these systems may not prove
-a prudent approach to validating this behavior for a candidate continuous
-controller, and instead, barrier certificates must be used. Barrier
-certificates analyze the dynamics of the system to say whether or not flux
-across a given boudnary exists. That is to say that they evaluate whether or
-not there is a trajectory or not that leaves a given boundary. This definition
-is exactly what defines the validity of a stabilizing continuous control mode.
-Once again, because the design of the discrete controller defines careful
-boundaries in continuous state space, the barrier is known a priori of which we
-must satisfy this condition. This will eliminate the search for such a barrier,
-and minimze complicatoin in validating stabilizing continuous control modes.
-
-Finally, consideration must be paid for when errors occur. The validation of
-these continuous control modes hinges upon having an assumption ofcorrect
-model, which in the case of a mechanical failure will almsot certainly be
-invalidated. Special continuous controllers for these conditions must be
-created, called 'explusory' control modes. These controllers will be
-responsible for ensuring safety in case of failure, and will be designed with
-reachability, but in this case, additional allocation for the allowing of
-physical parameters will be allowed in the analysis. Traditional safety
-analysis will also be used to identify potential failure modes, and the
-modelling of their worst case dynamics. The HAHCS will be able to idenfity why
-such a fault occors because an discrte boundary condition will be violated by
-the continuous physical controller. That is to say, since we will have
-validated the continuous control modes using reachability and barrier
-certificates a priori, we will know with certainty that the only room for
-dynamics to change is a shift in the plant dynamics, not that of the proven
-controller.
-
-\fi
-
-%%%%%%%%%% TABLESETTING
-
-% what is a hybrid system really for this proposal
-% Define: A hybrid system with continuous state space X ⊆ ℝⁿ and discrete modes Q = {q₁, q₂, ..., qₘ}
-% Each discrete mode qᵢ has an associated continuous state region Xᵢ ⊆ X
-% The discrete controller manages transitions between modes based on continuous state thresholds
-
-% what are requirements, anyways?
-
-% why do we care about defining the whole hybrid system into requirements?
-
-% How do different requirements line up into different parts of the system?
-% (operational vs strategic requirements and their relevance to different parts
-% of our system)
-
-Autonomous control systems are fundamentally different from automatic control
-systems. The difference between these systems is the level at which
-they operate. Automatic control systems are purely operational systems, 
-
-To build a high-assurance hybrid autonomous control system (HAHACS), a
-mathematical description of the system must be established. This work will make
-use of automata theory while including logical statements and control theory.
-The nomenclature and lexicon between these fields is far from homogenous, and
-the reviewer of this proposal is not expected to be an expert in all fields
-simultaneously. To present the research ideas as clearly as possible in this
-section, the following syntax is explained.
-
-A hybrid system is a dynamical system that has both continuous and discrete
-states. The specific type of system discussed in this proposal are continuous
-autonomous hybrid  systems. This means that these systems a) do not have
-external input \footnote{This is not strictly true in our case because we allow
-strategic inputs. For example, a remote powerplant may receive a start-up or
-shutdown command from a different location, but only this binary high level
-input is a strategic input.} and b) continuous states do not change
-instantaneously when discrete states change. For our systems of interest, the
-continuous states are physical, and are always Lipschitz continuous. This
-nomenclature is heavily borrowed from \cite{HANDBOOK ON HYBRID SYSTEMS CONTROL},
-but is redefined here for convenience:
-
-\begin{equation}
-  H = (\mathcal{Q}, \mathcal{X}, \mathbf{f}, Init, \mathcal{G}, \mathcal{R}, Inv)
-\end{equation}
-
-where:
-
-\begin{itemize}
-  \item \( \mathcal{Q}\): is the discrete states of the system
-  \item \( \mathcal{X}\): is the continuous states of the system
-  \item \(\mathbf{f}: \mathcal{Q} \times \mathbb{R} \rightarrow \mathbb{R} \), where
-    \(\mathbf{f}_i\) is a
-    vector field that defines the continuous dynamics for each \(q_i\)
-  \item \(Init\): the initial states of \(q\) and \(x\)
-  \item \( G\): guard
-    conditions that define when discrete state transitions occur
-  \item \(\delta: \mathcal{Q} \times G \rightarrow \mathcal{Q}\), are the
-    discrete state transition functions
-  \item \mathcal{R}: Reset maps that define state 'jumps'
-  \item \(Inv\): Safety invariants on the continuous dynamics
-\end{itemize}
-
-The creation of a HAHACS essentially boils down to the creation of such a tuple
-where there are proof artifacts that the intended behavior of the control system
-are satisfied by the actual implementation of the control systems. But to create
-such a HAHACS, we must first completely describe its behavior.
-
-%% Brief discussion on what each part of this tuple means for us
-
-\subsection{System Requirement and Specifications}
-
-Temporal logic is a powerful set of semantics to build systems that can have
-complex but deterministic behavior. 
-
-
-%%%%%%%%%%% Building discrete controllers
-
-% Buildout of requirements from written procedures (this is easy for critical
-% systems - we already have the requirements)
-
-% What happens to the invariants that specify a continuous space? Save em for
-% later. Here they become binary for our purposes
-% KEY POINT: We don't IMPOSE discrete abstraction - we FORMALIZE existing practice
-% Operating procedures (esp. nuclear) already define go/no-go conditions as discrete predicates
-% e.g., "WHEN coolant temp >315°C AND pressurizer level 30-60% THEN MAY initiate load following"
-% These thresholds come from design-basis safety analysis, validated over decades
-% Our methodology assumes this domain knowledge exists and provides formalization framework
-% The discrete predicates p₁, p₂, ... are Boolean functions over continuous state: pᵢ: X → {true, false}
-% Q: How do we rigorously set thresholds for continuous→discrete abstraction?
-% Q: How do we handle hysteresis to prevent mode chattering near boundaries?
-% Q: How do we account for sensor noise and measurement uncertainty?
-% Q: How do we handle numerical precision issues when creating discrete automata? (relates to task 36)
-
-% Discrete controller implementation can be realized with reactive synthesis. 
-% LTL specs to automata
-
-% talk a bit about tools here like FRET. Talk about previous attempts.
-
-\begin{figure}[htbp]
-  \centering
-  \framebox[0.8\textwidth]{\rule{0pt}{3cm}\textit{Strategic, operational,
-tactical placeholder}}
-  \caption{Breakdown of control scope}
-  \label{fig:strat_op_tact}
-\end{figure}
-
-
-Human control of nuclear power can be divided into three different scopes:
-strategic, operational, and tactical. Strategic control is the high-level and
-long term decision making for the plant. This level has objectives that are
-complex and economic in scale, such as managing labor needs and supply chains to
-optimize sheduled maintenence and downtime. The time scale on this level of
-control is long, often over months or years. The lowest level of control is the
-tactical level. This is the individual control of pumps, turbines, and
-chemistry of the plant. This level of control has already been somewhat
-automated today in nuclear power, and is generally considered 'automatic
-control' when autonomous. These controls are almost always continuous systems,
-and have a direct impact on the physical state of the plant. Tactical control
-objectives are things like maintaining a pressurizer level, maintaining a
-certain core temperature, or adjusting reactivity with a chemical shim. The level of
-control linking these two levels, then, is the operational control scope.
-Operational control is the primary responsibility of human operators today.
-Operational control takes the current strategic objective, and implements
-tactical control objectives to drive the plant towards strategic goals. In this
-way, it is the bridge between high and low level goals. A strategic goal may be
-to perform refueling at a certain time, while the tactical level of the plant
-currently is focused on mainting a certain core temperature. The operational
-level is what issues the shutdown procedure of the plant, using several smaller
-tactical goals along the way to achieve this objective. Thus, the combination of
-the operational and tactical level of the plant fundamentally forms a hybrid
-controller. The tactical level is the continuous evolution of the plant
-according to the control input and control law, while the operational level is a
-discrete state evolution which determines the tactical control law to reach
-different operational states.
-
-This operational control level is the main reason for the requirement of human
-opeartors in nuclear control today. The hybrid nature of this control system
-makes it difficult to prove that a controller will perform according to the
-strategic requirements, as the infrastructure to build hybrid systems today
-dooes not exist. Humans have been used for this layer because the general
-intelleigence of humans has be relied upon as a safe way to manage the hybrid
-nature of this system. But, these operators are using prescriptive operating
-manuals to perform their control with strict procedures on what control to
-implement at a given time. These procedures are the key to the operational
-control scope.
-
-The method of constructing a HAHACS in this proposal leverages two key points of
-the way this control scope is done today: first, the operational scope control
-is effectively discrete control. Second, the rules of implementing this control
-are described a priori to their implementation in operating procedures. We can
-make great use of these facts by formalizing the rules for transitioning between
-discrete states. To do this, we will use temporal logic to formalize discrete
-switching behavior. 
-
-Temporal logic is a rich syntax that allows for the definition of logical
-calculations including time related bounds. For this reason, we can make
-statements relating discrete control modes to one another. Using temporal logic,
-we can effectively describe all of the requirements of a HAHACS. The guard
-conditions \(G\) are easily defined by determining boundary conditions between
-discrete states and defining their behavior, while continuous mode invariants
-can be defined using temporal logic statements as well. These form the basis of
-any proofs about a HAHACS, and are the fundamental 'truth' statements about what
-the behavior of the system is designed to be.
-
-To build these temporal logic statements, an intermediary tool called FRET is
-planned to be used. FRET stands for Formal Requirements Elicitation Tool, and
-was designed by NASA to build high assurance timed systems. FRET is an
-intermediarly language between temporal logic and natural language that allows
-for rigid definitions of temporal behvarior while using a logic-novice friendly
-syntax. This benefit is crucial for the feasibility of this methodology for
-industry, as minimizing the barrier to formal methods is a critical component of
-their scucess. By reducing the expert knowledge required to use these tools,
-their adoption with current workforce becomes easier. 
-
-A key feature of FRET is the ability to start with logically imprecise
-statements and consecutively refine them into a well-posited specification. We
-can use this to our advantage by directly dumping in operating procedures and
-design requirements into FRET in natural language, and iteratively refining them
-into the specifications for a HAHACS. This has two distinct but important
-benefits. First, it allows us to draw a direct link from the design
-documentation to the digital system implementation. Second, it clearly
-demonstrates where the natural language documents are insufficient. These
-procedures may still be used by human operators, so any wiggle room for
-interpretation is a weakness that must be addressed.
-
-%Talk about how we go from temp logic to reactive synth. Metnion fret can
-%export, or naturlly support reactive synth solver ltlsynt, a sota react synth
-%solver
-
-Once system requirements we defined as temporal logic specifications we will use
-the specifications to build the discrete control system. To do this, reactive
-synthesis tools will be utilized. Reactive synthesis is a field in computer
-science that deals with the automated creation of reactive programs from
-temporal logic specifications. A reactive program is one that for a given state
-takes an input, and produces an output. Our systems, such as the discrete
-portion of the controller, fit exactly this this mold. The current discrete
-state, and status of guard conditions are the input to the system, while the
-output is the next discrete state. The output of a reactive synthesis algorithim
-is a discrete automata.
-
-Reactive synthesis' main advantage is the fact that at no point in the
-production of a discrete automata of the program is human engineering required.
-The resultant automata is correct by construction. This method of construction
-eliminates the possibility of human error outright at the implementation state.
-Instead, the effort on the human designer is directed at the specification of
-the system behavior itself. 
-
-% talk about what the benefits of reactive synth are. Proof chain, machine
-% checkable, blah blah blah
-
-%%%%% I NEED TO WRITE ABOUT HOW REQUIREMENTS ARE EXTRACTED AND WHAT BECOME
-CONTINUOUS CONTROLLER TRANSITIONS VS DISCRETE GUARD CONDITIONS
-
-%%%%%%%%%%%% Building continuous controllers
-\subsection{Continuous Controllers}
-
-% The whole point of a hybrid system is that there are continuous components
-% underneath the digital system. We built the discrete like the physical doesn't
-% exist, but it really does. So how do we capture the physical system too?
-
-% SCOPE FRAMING: This methodology VERIFIES continuous controllers, not SYNTHESIZES them
-% Compare to model checking: doesn't tell you HOW to design software, verifies if it satisfies specs
-% We assume controllers can be designed using standard control theory techniques
-% Our contribution: verification that candidate controllers compose correctly with discrete layer
-
-% What are the main different kinds of continuous modes we may see?
-% Mathematical structure: Each discrete mode qᵢ provides three key pieces of information:
-% 1. Entry conditions: X_entry,i ⊆ X (initial state set)
-% 2. Exit conditions: X_exit,i ⊆ X (target state set)
-% 3. Invariants: X_safe,i ⊆ X (safety envelope during operation)
-% These come from the discrete controller synthesis and define objectives for continuous control
-% Q: Who designs the continuous controllers and how? This methodology verifies
-% them, but doesn't synthesize them. Is this a scope problem?
-
-The synthesis of the discrete operational controller is only half of an
-autonomous controller. These control systems are hybrid, with both discrete and
-continuous components. In this section, we will talk about the continuous
-control modes that are the transitions between discrete modes, how they may be
-synthesized, and how we plan to verify them. 
-
-The operational control scope defines go/no-go decisions that themselves are
-deciding what kind of continuous control to implement. To this end, the entry or
-exit of a discrete state triggers are themselves the guard conditions \(G\) that
-define the barriers of the continuous controller. These continuous controllers
-all share a large state space, but each individual continuous control mode
-operates within it's own partition defined by the discrete state \(q_i\) and
-guard conditions \(G\). This partitioning of the continuous state space amongst
-several discrete vector fields controlled by the given \(q_i\) has traditionally
-been a difficult problem for validation and verification of systems properties.
-Typically, the discontinuity of the vector fields at discrete state interfaces
-make things like reachability analysis computationally expensive, and analytic
-solutions become intractable. 
-
-We circumnavigate these issues by designing our hybrid system from the bottom up
-with this verification in mind. Each continuous control mode has an input and
-output set clearly defined by our discrete transitions \textit{a priori}.
-Consider that we define the continuous state space as \(X\). Whenever we create
-guard functions from our design requirements for a given system, we are
-effectively creating subsets \(X_{entry,i}\) and \(X_{exit,i}\) for each
-discrete mode \(q_i\). These subsets define when the state transitions occur
-between discrete modes, but more importantly when building continuous control
-modes, they become control objectives.
-
-% Start talking about what it means to build controlelrs to the objectives
-% rahter than the other why around. ALso why it makes things much easier to
-% verify and validate
-
-%%%%%% Transitory modes
-
-% entry and exit conditions
-% the goal is getting from one physical state to another
-% MATHEMATICAL FORMULATION:
-% Control objective: reach(X_entry,i) → reach(X_exit,i) while maintaining x(t) ∈ X_safe,i
-% Standard control techniques (LQR, MPC, trajectory optimization) applied with these constraints
-%
-% VERIFICATION: Reachability analysis confirms ALL trajectories starting in X_entry,i
-% reach X_exit,i without violating X_safe,i
-% Formally: Reach(X_entry,i, f(x,u), T) ⊆ X_exit,i ∪ X_safe,i
-% where f(x,u) is the closed-loop continuous dynamics
-%
-% we have the physical requirements from earlier specifications. Here we use
-% them in a reachability analysis. This time, we use the actual physical values
-% instead of the binary yes/no we used for discrete
-% Q: How do we verify timing constraints? If a transitory controller eventually
-% reaches the exit condition but takes too long, that violates safety. Timed
-% automata? Timed reachability?
-% Q: Should formalize the Mealy machine perspective - continuous system IS the
-% transition, and entry/exit conditions are the discrete states. This could be
-% a unifying conceptual framework.
-
-%%%%%% stabilizing modes
-
-% these are control modes with an objective of KEEPING a certain discrete state
-% stable
-%
-% MATHEMATICAL FORMULATION:
-% Control objective: remain(X_target,i) where X_target,i ⊂ X_safe,i
-% Standard feedback control (PID, state feedback, LQG) applied to maintain equilibrium
-%
-% VERIFICATION: Barrier certificates prove closed-loop dynamics cannot escape X_safe,i
-% Formally: Find B(x) s.t. ∇B(x)·f(x,u) ≤ 0 for all x ∈ ∂X_safe,i
-% This proves no trajectory can cross the boundary (no flux out of safety region)
-%
-% we also have the physical requirements for this. These can be used for barrier
-% certificates. We can prove that our model won't leave a given area without
-% some disturbance. 
-
-%%%%%% expulsory modes
-% I've made an implicit assumption when talking about transitory and stabilizing
-% modes. That our model is correct. This might not be true
-
-% In the case of a failure, our model will almost certainly be incorrect. For
-% this, we have to build safe shutdown modes too, since a human won't be in the
-% loop to shut things down.
-%
-% MATHEMATICAL FORMULATION:
-% Control objective: reach(X_current) → reach(X_safe_shutdown) under parameter uncertainty
-% where X_current may be anywhere in X (worst-case entry conditions)
-% Dynamics have parametric uncertainty: f(x,u,θ) where θ ∈ Θ_failure
-%
-% VERIFICATION: Parametric reachability analysis with robustness margins
-% Reach(X_current, f(x,u,θ), T) ⊆ X_safe_shutdown for all θ ∈ Θ_failure
-% Conservative bounds on Θ_failure come from FMEA/traditional safety analysis
-
-% WE can detect physical failures exist because our physical controllers have
-% previously been proven as correct by reachability and barrier certificates. We
-% KNOW our controller cannot be incorrect for the plant, so if an invariant is
-% violated, we KNOW it's the plant that has changed.
-% Q: What about sensor failures (wrong readings vs actual plant failure)?
-% Q: What about unmodeled disturbances that aren't failures?
-% Q: What if model uncertainty was too optimistic to begin with?
-% Need to be more precise about what "model failure" means and detect-ability. 
-
-% We do this using continuous modes that shutdown the system, and using
-% reachability analysis with parametric uncertainty, we can prove for a range of
-% error conditions we can maintain safe shutdown.
-% Q: How much parametric uncertainty is enough? How do we determine bounds for
-% worst-case failure dynamics? Need methodology for this.
-
-%%%%%%%%%%%% Implementation with industrial partnerships
-%%%%%%% Emerson
-%talk about this
-% ovation system
-% scenic? Is that what they call it?
-% ripe partnership with Westinghouse
-% Likely build a model with a ccng plant. They already have sophisticated models
-% of them
-% build controller with simplified model, then test with high fidelity digital
-% twin
-
-
-
-
-%
-%%%%%%%%%% 

4-metrics-of-success/metrics.tex

@@ -4,9 +4,7 @@ This research will be measured by advancement through Technology Readiness
 Levels, progressing from fundamental concepts to validated prototype
 demonstration. This work begins at TRL 2--3 and aims to reach TRL 5, where
 system components operate successfully in a relevant laboratory 
-environment.\splitnote{TRL as primary metric is smart — speaks industry 
+environment. This section explains why TRL advancement provides the most appropriate success
-language.}
-This section explains why TRL advancement provides the most appropriate success
 metric and defines the specific criteria required to achieve TRL 5.
 
 Technology Readiness Levels provide the ideal success metric because they
@@ -15,9 +13,7 @@ deployment---precisely what this work aims to bridge. Academic metrics like
 papers published or theorems proved cannot capture practical feasibility.
 Empirical metrics like simulation accuracy or computational speed cannot
 demonstrate theoretical rigor. TRLs measure both dimensions 
-simultaneously.\splitnote{Good framing — explains why other metrics are 
+simultaneously. Advancing from TRL 3 to TRL 5 requires maintaining theoretical rigor while
-insufficient.}
-Advancing from TRL 3 to TRL 5 requires maintaining theoretical rigor while
 progressively demonstrating practical feasibility. Formal verification must
 remain valid as the system moves from individual components to integrated
 hardware testing.
@@ -72,9 +68,10 @@ across the full operational envelope. The controller must handle off-nominal
 scenarios to validate that expulsory modes function correctly. For example,
 simulated sensor failures must trigger appropriate fault detection and mode
 transitions, and loss-of-cooling scenarios must activate SCRAM procedures as
-specified. Graded responses to minor disturbances are outside this work's 
+specified. Graded responses to minor disturbances are outside this work's scope.
-scope.\splitsuggest{Consider noting why graded responses are out of scope — 
+If this work is successful, graded responses are an extension that requires
-is it time, complexity, or scope creep? Brief justification helps.}
+additional labor, but not new capability as the mechanics of a graded response
+are an addition of special discrete modes and continuous controllers to match.
 Formal verification results must remain valid, with discrete behavior matching
 specifications and continuous trajectories remaining within verified bounds.
 This proves that the methodology produces verified controllers implementable on
@@ -92,5 +89,4 @@ operating on industrial control hardware through hardware-in-the-loop testing in
 a relevant laboratory environment. This establishes both theoretical validity
 and practical feasibility, proving that the methodology produces verified
 controllers and that implementation is achievable with current 
-technology.\splitnote{Clear success criteria. Committee will know exactly 
+technology.
-what ``done'' looks like.}

5-risks-and-contingencies/risks.tex

@@ -1,7 +1,7 @@
 \section{Risks and Contingencies}
 
 This research relies on several critical assumptions that, if invalidated, would
-require scope adjustment or methodological revision.\splitnote{Honest acknowledgment of risks with clear contingencies — committee will appreciate this.} The primary risks to
+require scope adjustment or methodological revision. The primary risks to
 successful completion fall into four categories: computational tractability of
 synthesis and verification, complexity of the discrete-continuous interface,
 completeness of procedure formalization, and hardware-in-the-loop integration
@@ -49,10 +49,8 @@ rather than a failure.
 The second critical assumption concerns the mapping between boolean guard
 conditions in temporal logic and continuous state boundaries required for mode
 transitions. This interface represents the fundamental challenge of hybrid
-systems: relating discrete switching logic to continuous dynamics. Temporal
+systems: relating discrete switching logic to continuous dynamics. Guard conditions
-logic operates on boolean predicates, while continuous control requires
+requiring complex predicates may resist boolean abstraction, making
-reasoning about differential equations and reachable sets. Guard conditions
-requiring complex nonlinear predicates may resist boolean abstraction, making
 synthesis intractable. Continuous safety regions that cannot be expressed as
 conjunctions of verifiable constraints would similarly create insurmountable
 verification challenges. The risk extends beyond static interface definition to
@@ -88,18 +86,6 @@ structures the problem to minimize complex transitions, with critical safety
 properties concentrated in expulsory modes that can receive additional design
 attention.
 
-Mitigation strategies focus on designing continuous controllers with discrete
-transitions as primary objectives from the outset. Rather than designing
-continuous control laws independently and verifying transitions post-hoc, the
-approach uses transition requirements as design constraints. Control barrier
-functions provide a systematic method to synthesize controllers that guarantee
-forward invariance of safe sets and convergence to transition boundaries. This
-design-for-verification approach reduces the likelihood that interface
-complexity becomes insurmountable. Focusing verification effort on expulsory
-modes---where safety is most critical---allows more complex analysis to be
-applied selectively rather than uniformly across all modes, concentrating
-computational resources where they matter most for safety assurance.
-
 \subsection{Procedure Formalization Completeness}
 
 The third assumption is that existing startup procedures contain sufficient
@@ -143,16 +129,3 @@ valuable to both the nuclear industry and formal methods community, establishing
 clear requirements for next-generation procedure development and autonomous
 control specification languages.
 
-Early-stage procedure analysis with domain experts provides the primary
-mitigation strategy. Collaboration through the University of Pittsburgh Cyber
-Energy Center enables identification and resolution of ambiguities before
-synthesis attempts, rather than discovering them during failed synthesis runs.
-Iterative refinement with reactor operators and control engineers can clarify
-procedural intent before formalization begins, reducing the risk of discovering
-insurmountable specification gaps late in the project. Comparison with
-procedures from multiple reactor designs---pressurized water reactors, boiling
-water reactors, and advanced designs---may reveal common patterns and standard
-ambiguities amenable to systematic resolution. This cross-design analysis would
-strengthen the generalizability of any proposed specification language
-extensions, ensuring they address industry-wide practices rather than specific
-quirks.

6-broader-impacts/impacts.tex

@@ -66,6 +66,6 @@ applies to any hybrid system with documented operational requirements. Potential
 applications include chemical process control, aerospace systems, and autonomous
 transportation, where similar economic and safety considerations favor increased
 autonomy with provable correctness guarantees. Demonstrating this approach in
-nuclear power---one of the most regulated and safety-critical domains\splitnote{``If it works here, it works anywhere — strong closing argument.}---will
+nuclear power---one of the most regulated and safety-critical domains---will
 establish both the technical feasibility and regulatory pathway for broader
 adoption across critical infrastructure.

8-schedule/schedule.tex

@@ -93,4 +93,4 @@ methodology. M5 (Month 20) achieves TRL 5 by demonstrating practical
 implementability on industrial hardware. This milestone delivers a conference
 paper submission to NPIC\&HMIT or CDC documenting hardware implementation and
 experimental validation. M6 (Month 24) completes the dissertation documenting
-the entire methodology, experimental results, and research contributions.\splitnote{Clear timeline with publication targets — shows you have a plan.} 
+the entire methodology, experimental results, and research contributions.

main.aux

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main.tex

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 \begin{document}
 
-\pagenumbering{roman}
+% \pagenumbering{roman}
 \maketitle
-\input{1-goals-and-outcomes/research-statement.tex}
+% \input{1-goals-and-outcomes/research-statement.tex}
 \newpage
 \tableofcontents
 \newpage
 \pagenumbering{arabic}
 
-\input{1-goals-and-outcomes/goals}
+\input{1-goals-and-outcomes/goals.tex}
 \newpage
 
 \input{2-state-of-the-art/state-of-art}

main.toc

@@ -1,4 +1,4 @@
-\contentsline {section}{Contents}{iii}{}%
+\contentsline {section}{Contents}{1}{}%
 \contentsline {section}{\numberline {1}Goals and Outcomes}{1}{}%
 \contentsline {section}{\numberline {2}State of the Art and Limits of Current Practice}{3}{}%
 \contentsline {subsection}{\numberline {2.1}Current Reactor Procedures and Operation}{3}{}%