Thesis/3-research-approach/approach.tex

\section{Research Approach}

To build a high-assurance hybrid autonomous control system (HAHACS), we must
first establish a mathematical description of the system. This work draws on
automata theory, temporal logic, and control theory. A hybrid system is a
dynamical system that has both continuous and discrete states. The specific
type of system discussed in this proposal is a continuous autonomous hybrid
system. 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{lunze_handbook_2009}, but is
redefined here for convenience:
%
\begin{equation}
  H = (\mathcal{Q}, \mathcal{X}, \mathbf{f}, Init, \mathcal{G}, \delta,
  \mathcal{R}, Inv)
\end{equation}
%
where:
%
\begin{itemize}
  \item $\mathcal{Q}$: the set of discrete states (modes) of the system
  \item $\mathcal{X} \subseteq \mathbb{R}^n$: the continuous state space
  \item $\mathbf{f}: \mathcal{Q} \times \mathcal{X} \rightarrow
    \mathbb{R}^n$: vector fields defining the continuous dynamics for each
    discrete mode $q_i$
  \item $Init \subseteq \mathcal{Q} \times \mathcal{X}$: the set of initial
    states
  \item $\mathcal{G}$: guard conditions that define when discrete state
    transitions may occur
  \item $\delta: \mathcal{Q} \times \mathcal{G} \rightarrow \mathcal{Q}$:
    the discrete state transition function
  \item $\mathcal{R}$: reset maps that define any instantaneous changes to
    continuous state upon discrete transitions
  \item $Inv$: safety invariants on the continuous dynamics
\end{itemize}

HAHACS bridges the gap between discrete and continuous verification by composing
formal methods from computer science with control-theoretic verification,
formalizing reactor operations using the framework of hybrid
automata~\cite{alur_hybrid_1993}. The challenge of hybrid system verification
lies in the interaction between discrete and continuous dynamics. Discrete
transitions change the active continuous 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. 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
compositional strategy follows the assume-guarantee paradigm for hybrid systems,
where guarantees about individual modes compose into guarantees about the
overall system~\cite{lunze_handbook_2009, alur_hybrid_1993}. This two-layer
approach mirrors the structure of reactor operations themselves: discrete
supervisory logic determines which control mode is active, while continuous
controllers govern plant behavior within each mode.

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. In concrete terms, this means producing a
discrete automaton whose transitions are provably correct, continuous
controllers whose behavior is verified against transition requirements, and
formal evidence linking the two. 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. By defining entry, exit, and safety conditions at the
discrete level first, we transform the intractable problem of global hybrid
verification into a collection of local verification problems with clear
interfaces. Verification is performed per mode rather than on the full
hybrid system, keeping the analysis tractable even for complex reactor
operations. Figure~\ref{fig:hybrid_automaton} illustrates this
structure for a simplified reactor startup sequence, showing discrete modes
connected by guard-triggered transitions with continuous dynamics governing
behavior within each mode.

\begin{figure}
  \centering
  \begin{tikzpicture}[
    state/.style={
      circle, draw=black, thick, minimum size=2.2cm,
      fill=blue!10, align=center, font=\small
    },
    trans/.style={
      ->, thick, >=stealth
    },
    guard/.style={
      font=\scriptsize, align=center, fill=white, inner sep=2pt
    },
    dynamics/.style={
      font=\scriptsize\itshape, text=blue!70!black
    }
  ]
    % States
    \node[state] (q0) at (0,0) {$q_0$\\Cold\\Shutdown};
    \node[state] (q1) at (5,0) {$q_1$\\Heatup};
    \node[state] (q2) at (10,0) {$q_2$\\Power\\Operation};
    \node[state, fill=red!15] (q3) at (5,-3.5) {$q_3$\\SCRAM};

    % Normal transitions
    \draw[trans] (q0) -- node[guard, above]
      {$T_{avg} > T_{min}$} (q1);
    \draw[trans] (q1) -- node[guard, above]
      {$T_{avg} \in [T_{op} \pm \delta]$\\$P > P_{crit}$} (q2);

    % Fault transitions
    \draw[trans, red!70!black] (q1) -- node[guard, left,
      text=red!70!black] {$\neg Inv_1$} (q3);
    \draw[trans, red!70!black] (q2) to[bend left=20] node[guard,
      right, text=red!70!black] {$\neg Inv_2$} (q3);

    % Recovery transition
    \draw[trans, dashed] (q3) to[bend left=30] node[guard, below]
      {Manual reset} (q0);

    % Self-loops indicating staying in mode
    \draw[trans] (q2) to[loop right] node[guard, right] {$Inv_2$}
      (q2);

    % Dynamics labels below states
    \node[dynamics] at (0,-1.4) {$\dot{x} = f_0(x)$};
    \node[dynamics] at (6,-1.2) {$\dot{x} = f_1(x)$};
    \node[dynamics] at (10,-1.4) {$\dot{x} = f_2(x)$};
    \node[dynamics] at (5,-4.9) {$\dot{x} = f_3(x)$};

  \end{tikzpicture}
  \caption{Simplified hybrid automaton for reactor startup. Each discrete
    state $q_i$ has associated continuous dynamics $f_i$. Guard conditions
    on transitions (e.g., $T_{avg} > T_{min}$) are predicates over
    continuous state. Invariant violations ($\neg Inv_i$) trigger
    transitions to the SCRAM state. The operational level manages discrete
    transitions; the tactical level executes continuous control within each
    mode.}
  \label{fig:hybrid_automaton}
\end{figure}

\subsection{System Requirements, Specifications, and Discrete Controllers}

Human control of nuclear power can be divided into three different scopes:
strategic, operational, and tactical. Strategic control is 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 scheduled maintenance and downtime. The time scale at this level is
long, often spanning months or years. The lowest level of control is the
tactical level. This is the individual control of pumps, turbines, and
chemistry. Tactical control has already been somewhat automated in nuclear power
plants today, and is generally considered ``automatic control'' when autonomous.
These controls are almost always continuous systems with a direct impact on the
physical state of the plant. Tactical control objectives include, but are not
limited to, maintaining pressurizer level, maintaining core temperature, or
adjusting reactivity with a chemical shim.

The level of control linking these two extremes 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 bridges high-level and low-level goals. A strategic goal may be to
perform refueling at a certain time, while the tactical level of the plant is
currently focused on maintaining a certain core temperature. The operational
level issues the shutdown procedure, using several smaller tactical goals along
the way to achieve this strategic objective. This heiarchal division of control
scope and objectives is illustrated graphically in
figure~\ref{fig:strat_op_tact}. 

\begin{figure}
  \centering
  \begin{tikzpicture}[scale=0.8]
    % Pyramid layers
    \fill[blue!30!white] (0,4) -- (2,4) -- (1,5.) -- cycle;
    \fill[blue!20!white] (-1.5,2.5) -- (3.5,2.5) -- (2,4) -- (0,4)
      -- cycle;
    \fill[blue!10!white] (-3,1) -- (5,1) -- (3.5,2.5) -- (-1.5,2.5)
      -- cycle;

    % Labels inside pyramid
    \node[font=\small\bfseries] at (1,4.5) {Strategic};
    \node[font=\small\bfseries] at (1,3.1) {Operational};
    \node[font=\small\bfseries] at (1,1.6) {Tactical};

    % Descriptions to the right
    \node[anchor=west, font=\small, text width=8cm] at (5.5,4.6)
      {\textit{Long-term planning:} maintenance scheduling, capacity
      planning, economic dispatch};
    \node[anchor=west, font=\small, text width=8cm] at (5.5,3.1)
      {\textit{Discrete decisions:} startup/shutdown sequences, power
      level changes, mode transitions};
    \node[anchor=west, font=\small, text width=8cm] at (5.5,1.6)
      {\textit{Continuous control:} temperature regulation, pressure
      control, load following};

    % Bracket showing HAHACS scope (simple line with text)
    \draw[thick] (5.0,1.0) -- (-3.5,1) -- (-3.5,4) -- (2.0,4) --
      cycle;
    \node[font=\small, align=center, rotate=90] at (-4.2,2.5)
      {HAHACS scope};
  \end{tikzpicture}
  \caption{Control scope hierarchy in nuclear power operations.
    Strategic control (long-term planning) remains with human management.
    HAHACS addresses the operational level (discrete mode switching) and
    tactical level (continuous control within modes), which together form
    a hybrid control system.}
  \label{fig:strat_op_tact}
\end{figure}

This operational control level is the main reason for the requirement of human
operators in nuclear control today. The hybrid nature of this control system
makes it difficult to prove what the behavior of the combined hybrid system will
do across the entire state-space, so human operators have been used as a
stop-gap for safety. Humans have been used for this layer because their general
intelligence has been relied upon as a safe way to manage the hybrid nature of
this system---if a failure occured, it has been assumed a human operator can
figure out a solution to maintain plant performance and safety without
exhaustive knowledge of plant behavior. However, human factors research has
sought to minimize the need for general human reasoning by creating extremely
prescriptive operating manuals with strict procedures dictating what control to
implement at a given time. These operating manuals have minimized the role of
human operators today, are the key to the automating the operational control
scope.

The method of constructing a HAHACS in this proposal leverages two key
observations about current practice. First, the operational scope control is
effectively discrete control. Second, the rules for implementing this control
are described in operating procedures prior to their implementation. Instead of
implementing these procudures with a human controller, we rigorize the
instructions as a set of formal requirements. The behavior of any control system
originates in requirements: statements about what the system must do, must not
do, and under what conditions. For nuclear systems, these requirements derive
from multiple sources including regulatory mandates, design basis analyses, and
aforementioned operating procedures. The challenge is formalizing these requirements with
sufficient precision that they can serve as the foundation for autonomous
control system synthesis and verification. We can build these requirements using
temporal logic.

Temporal logic is a powerful set of semantics for building systems with
complex but deterministic behavior. Temporal logic extends classical
propositional logic with operators that express properties over time. Using
temporal logic, we can make statements relating discrete control modes to one
another and define all the requirements of a HAHACS. The guard conditions
$\mathcal{G}$ are defined by determining boundary conditions between discrete
states and specifying their behavior, while continuous mode invariants can
also be expressed as temporal logic statements. These specifications form the
basis of any proofs about a HAHACS and constitute the fundamental truth
statements about what the behavior of the system is designed to be.

Discrete mode transitions include predicates that are Boolean functions over the
continuous state space: $p_i: \mathcal{X} \rightarrow \{\text{true},
\text{false}\}$. These predicates formalize conditions like ``coolant
temperature exceeds 315\textdegree{}C'' or ``pressurizer level is between 30\%
and 60\%.'' Critically, we do not impose this discrete abstraction artificially.
Operating procedures for nuclear systems already define go/no-go conditions as
discrete predicates, but do so in natural language. These thresholds come from
design basis safety analysis and have been validated over decades of operational
experience. Our methodology assumes this domain knowledge exists and provides a
framework to formalize it. This is why the approach is feasible for nuclear
applications specifically: the work of defining safe operating boundaries has
already been done by generations of nuclear engineers. The work of translating
these requirements from interpretable natural language to a formal requirement is
what remains to be done.

Linear temporal logic (LTL) is particularly well-suited for specifying reactive
systems. LTL formulas are built from atomic propositions (our discrete
predicates) using Boolean connectives and temporal operators. These operators
allow us to express safety properties (``the reactor never enters an unsafe
configuration''), liveness properties (``the system eventually reaches operating
temperature''), and response properties (``if coolant pressure drops, the system
initiates shutdown within bounded time''). We note that FRET's realizability
checking currently supports safety and bounded response properties but not
general liveness properties~\cite{katis_realizability_2022}. Liveness
requirements such as ``eventually reaches operating temperature'' are instead
verified through the continuous mode analysis described in Section~3.2, where
reachability analysis can confirm that target states are attained within bounded
time.

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 developed by NASA to build high-assurance timed systems. FRET is an
intermediate language between temporal logic and natural language that allows
for rigid definitions of temporal behavior while using a syntax accessible to
engineers without formal methods expertise\cite{katis_realizability_2022}. This
benefit is crucial for the feasibility of this methodology in industry. By
reducing the expert knowledge required to use these tools, their adoption with
the current workforce becomes easier.

A key feature of FRET is the ability to start with logically imprecise
statements and consecutively refine them into well-posed
specifications\cite{katis_realizability_2022, pressburger_using_2023}. We
can use this to our advantage by directly importing operating procedures and
design requirements into FRET in natural language, then iteratively refining
them into specifications for a HAHACS. This has two distinct benefits.
First, it allows us to draw a direct link from design documentation to
digital system 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.

\dasinline{Maybe add more details about FRET case studies here. This would be
Pressburger and Katis.}

Once system requirements are defined as temporal logic specifications, we use
them to build the discrete control system. To do this, reactive synthesis tools
are employed. 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~\cite{jacobs_reactive_2024}. Our systems fit exactly this mold: the
current discrete state and status of guard conditions are the input, while the
output is the next discrete state.

Reactive synthesis solves the following problem: given an LTL formula $\varphi$
that specifies desired system behavior, automatically construct a finite-state
machine (strategy) that produces outputs in response to environment inputs such
that all resulting execution traces satisfy $\varphi$. If such a strategy
exists, the specification is called \emph{realizable}. The synthesis algorithm
either produces a correct-by-construction controller or reports that no such
controller can exist. This realizability check is itself valuable: an
unrealizable specification indicates conflicting or impossible requirements in
the original procedures. The current implementation and one of the main uses of
FRET today is for exactly this purpose---multiple case studies have used FRET
for the refinement of unrealizable specifications into realizable systems
\cite{katis_realizability_2022, pressburger_using_2023}. 

The main advantage of reactive synthesis is that at no point in the production
of the discrete automaton is human engineering of the implementation required.
The resultant automaton is correct to the specification by construction. This
method of construction eliminates the possibility of human error at the
implementation stage entirely. The effort shifts entirely to specifying correct
behavior rather than implementing it. This has two critical implications. First,
every mode transition can be traced back through the specification to its
originating requirement, providing a clear liability and justification chain.
Second, by defining system behavior in temporal logic and synthesizing the
controller using deterministic algorithms, discrete control decisions become
provably consistent with operating procedures. 

The output of reactive synthesis is a finite-state machine that can be directly
translated to executable code. Tools such as Strix~\cite{meyer_strix_2018}
accept full LTL specifications and produce Mealy machines via parity game
solving~\cite{katis_capture_2022}. For specifications within the GR(1)
fragment---which captures the reactive input-output structure typical of
supervisory control---synthesis is efficient and scales to specifications with
thousands of states. Nuclear operating procedures are well-suited to this
fragment: environment inputs correspond to plant state measurements and guard
conditions, while outputs are mode transition commands. The synthesized
automaton provides a correct-by-construction implementation that can be compiled
to run on industrial control hardware without manual translation of the control
logic.

\subsection{Continuous Control Modes}

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. This section describes the continuous control modes that
execute within each discrete state, and how we verify that they satisfy the
requirements imposed by the discrete layer. It is important to clarify the scope
of this methodology with respect to continuous controller design. This work will
verify continuous controllers; it does not synthesize them. The distinction
parallels model checking in software verification: model checking does not tell
engineers how to write correct software, but it verifies whether a given
implementation satisfies its specification. Similarly, we assume that continuous
controllers can be designed using standard control theory techniques, and to
that end, are not prohibitive to create. Our contribution is a verification
framework that confirms candidate controllers compose correctly with the
discrete layer to produce a safe hybrid system.

The operational control scope defines go/no-go decisions that determine what
kind of continuous control to implement. The entry or exit conditions of a
discrete state are themselves the guard conditions $\mathcal{G}$ that define
the boundaries for each continuous controller's allowed state-space region.
These continuous controllers all share a common state space, but each
individual continuous control mode operates within its own partition defined
by the discrete state $q_i$ and the associated guard conditions. This partitioning of
the continuous state space among several distinct 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{kapuria_using_2025, lang_formal_2021}.

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
output set clearly defined by our discrete transitions \textit{a priori}.
Consider that we define the continuous state space as $\mathcal{X}$. Each
discrete mode $q_i$ then provides three key pieces of information for continuous
controller design:
%
\begin{enumerate}
  \item \textbf{Entry conditions:} $\mathcal{X}_{entry,i} \subseteq
    \mathcal{X}$, the set of possible initial states when entering this mode
  \item \textbf{Exit conditions:} $\mathcal{X}_{exit,i} \subseteq
    \mathcal{X}$, the target states that trigger transition to the next mode,
    or is the region in the state space a stabilizing mode remains within.
  \item \textbf{Safety invariants:} $\mathcal{X}_{safe,i} \subseteq
    \mathcal{X}$, the envelope of safe states during operation in this mode.
    These are derived from invariants \(Inv\).
\end{enumerate}
%
These sets come directly from the discrete controller synthesis and define
precise objectives for continuous control.\dasnote{This SOUNDS like
assume-guarantee stuff. Maybe make that connection formal and cite it?} The
continuous controller for mode $q_i$ 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. Each
type has distinct verification requirements that determine which formal methods
tools are appropriate.

\dasinline{(1) Add figure showing the relationship between entry/exit/safety
sets. (2) Mention assume-guarantee compositional stuff and how that fits in
here.}

\subsubsection{Transitory Modes}

Transitory modes are continuous controllers designed to move the plant from one
discrete operating condition to another. Their purpose is to execute
transitions: starting from entry conditions, reach exit conditions, and maintain
safety invariants throughout. Examples include but are not limited to power
ramp-up sequences, cooldown procedures, and load-following maneuvers.

The control objective for a transitory mode can be stated formally. Given
entry conditions $\mathcal{X}_{entry}$, exit conditions
$\mathcal{X}_{exit}$, safety invariant $\mathcal{X}_{safe}$, and
closed-loop dynamics $\dot{x} = f(x)$, the controller must satisfy:
%
\[
\forall x_0 \in \mathcal{X}_{entry}: \exists T > 0: x(T) \in
\mathcal{X}_{exit} \land \forall t \in [0,T]: x(t) \in \mathcal{X}_{safe}
\]
%
That is, from any valid entry state, the trajectory must eventually reach the
exit condition without ever leaving the safe region.

Verification of transitory modes will use reachability analysis. Reachability
analysis computes the set of all states reachable from a given initial set under
the system dynamics~\cite{guernic_reachability_2009,
mitchell_time-dependent_2005, bansal_hamilton-jacobi_2017}. For a transitory
mode to be valid, the reachable set from $\mathcal{X}_{entry}$ must satisfy two
conditions:
%
\begin{enumerate}
  \item The reachable set eventually intersects $\mathcal{X}_{exit}$ (the
    mode achieves its objective)
  \item The reachable set never leaves $\mathcal{X}_{safe}$ (safety is
    maintained throughout the transition)
\end{enumerate}
%
Formally, if $\text{Reach}(\mathcal{X}_{entry}, f, [0,T])$ denotes the states
reachable within time horizon $T$:
%
\[
\text{Reach}(\mathcal{X}_{entry}, f_i, [0,T]) \subseteq \mathcal{X}_{safe}
\land \text{Reach}(\mathcal{X}_{entry}, f_i, [0,T]) \cap
\mathcal{X}_{exit} \neq \emptyset
\]
%
Because the discrete controller defines clear boundaries in
continuous state space, the verification problem for each transitory mode is
well-posed. We know the possible initial conditions, we know the target
conditions, and we know the safety envelope. The verification task is to
confirm that the candidate continuous controller achieves the objective from
all possible starting points.

Several tools exist for computing reachable sets of hybrid systems, including
CORA, Flow*, SpaceEx~\cite{frehse_spaceex_2011}, and JuliaReach. The choice of
tool depends on the structure of the continuous dynamics. Linear systems admit
efficient polyhedral or ellipsoidal reachability computations. Nonlinear 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.

\subsubsection{Stabilizing Modes}

Stabilizing modes are continuous controllers with an objective of maintaining
a particular discrete state indefinitely. Rather than driving the system
toward an exit state, they keep the system within a safe
operating region. Examples include steady-state power operation, hot standby,
and load-following at constant power level. Reachability analysis for
stabilizing modes may not be a suitable approach to validation. Instead, we
plan to use barrier certificates. Barrier certificates analyze the dynamics
of the system to determine whether flux across a given boundary
exists~\cite{prajna_safety_2004}. In other words, they
evaluate whether any trajectory leaves a given boundary. This definition is
exactly what defines the validity of a stabilizing continuous control mode.

A barrier certificate (or control barrier function) is a scalar function $B:
\mathcal{X} \rightarrow \mathbb{R}$ that certifies forward invariance of a safe
set. The idea is analogous to Lyapunov functions for
stability~\cite{branicky_multiple_1998}: rather than computing trajectories
explicitly, we find a certificate function whose properties guarantee the
desired behavior. For a safe set $\mathcal{C} = \{x : B(x) \geq 0\}$ and
dynamics $\dot{x} = f(x,u)$, the\dasinline{Should clarify that the safe set C is
not the entire continuous region. It's just the boundary of the region.} barrier
certificate condition requires:
%
\[
\forall x \in \partial\mathcal{C}: \dot{B}(x) = \nabla B(x) \cdot f(x,u(x))
\geq 0
\]
%
This condition states that on the boundary of the safe set (where $B(x) =
0$), the time derivative of $B$ is non-negative. Geometrically, this means
the vector field points inward or tangent to the boundary, never outward. If
this condition holds, no trajectory starting inside $\mathcal{C}$ can ever
leave.

Because the design of the discrete controller defines careful boundaries in
continuous state space, the barrier \(\mathcal{C}\) is known prior to designing
the continuous controller. This eliminates the search for an appropriate barrier
and minimizes complication in validating stabilizing continuous control modes.
The discrete specifications tell us what region must be invariant; the barrier
certificate confirms that the candidate controller achieves this invariance.

Finding barrier certificates can be formulated as a sum-of-squares (SOS)
optimization problem for polynomial systems, or solved using satisfiability
modulo theories (SMT) solvers for broader classes of
dynamics~\cite{prajna_safety_2004, kapuria_using_2025}. The key advantage is
that the verification is independent of how the controller was designed.
Standard control techniques can be used to build continuous controllers, and
barrier certificates provide a separate check that the result satisfies the
required invariants. This also allows for the checking of control modes with
different models than they are designed for. 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.\splitnote{SOS methods
  proven effective: Papachristodoulou 2021 (SOSTOOLS v4, pp.1-2) solves barrier
  certificate optimization via SOS constraints---tool integrates with MATLAB.
  Borrmann 2015 (pp.4-8) demonstrates control barrier certificates for
  multi-agent systems, showing how discrete boundaries (mode guards) can inform
  barrier design. Your claim that discrete specs eliminate barrier search is
  novel and well-supported by these foundations.}\splitnote{Hauswirth 2024
  (pp.1-3) shows optimization-based robust feedback controllers can serve as
alternative verification method---suggests barrier certificates + reachability
provide complementary guarantees for your stabilizing modes.}

\subsubsection{Expulsory Modes}

Expulsory modes are continuous controllers responsible for ensuring safety
when failures occur. They are designed for robustness rather than optimality.
The control objective is to drive the plant to a safe shutdown state from
potentially anywhere in the state space, under degraded or uncertain
dynamics. Examples include emergency core cooling, reactor SCRAM sequences,
and controlled depressurization procedures.

We can detect that physical failures exist because our physical controllers have
been previously proven correct by reachability and barrier certificates. We know
our controller cannot be incorrect for the nominal plant model, so if an
invariant is violated, we know the plant dynamics have changed. The mathematical
formulation for expulsory mode verification differs from transitory modes in two
key ways. First, the entry conditions may be the entire state space (or a large,
conservatively bounded region) rather than a well-defined entry set. The failure
may occur at any point during operation. Second, the dynamics include parametric
uncertainty representing failure modes:
%
\[
\dot{x} = f(x, u, \theta), \quad \theta \in \Theta_{failure}
\]
%
where $\Theta_{failure}$ captures the range of possible degraded plant
behaviors identified through failure mode and effects analysis (FMEA) or
traditional safety analysis. 

We verify expulsory modes using reachability analysis with parametric
uncertainty. While tools such as SpaceEx handle nondeterministic
inputs~\cite{frehse_spaceex_2011}, parametric plant uncertainty requires
conservative over-approximation of reachable sets across the full parameter
range. The verification condition requires that for all parameter values within
the uncertainty set, trajectories from the expanded entry region reach the safe
shutdown state:
%
\[
\forall \theta \in \Theta_{failure}:
\text{Reach}(\mathcal{X}_{current}, f_\theta, [0,T]) \subseteq
\mathcal{X}_{shutdown}
\]
%
This is more conservative than nominal reachability, accounting for the fact
that we cannot know exactly which failure mode is active.

Traditional safety analysis techniques inform the construction of
$\Theta_{failure}$. Probabilistic risk assessment, FMEA, and design basis
accident analysis identify credible failure scenarios and their effects on
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.\splitnote{Parametric uncertainty approach validated: Kapuria 2025
(pp.82-120, Sections 5) verifies SmAHTR resiliency against UCAs with
uncertain dynamics (e.g., PHX secondary flow shutdown, resonating turbine
flow). Uses reachability + Z3 SMT solver (pp.23-24, Section 2.5 on
$\delta$-SAT) to handle nonlinear uncertainty---demonstrates your expulsory
mode approach is sound for nuclear failures. Shows safety can be proven even
when controller deviates from nominal (pp.85-107, UCA 1
analysis).}

\subsection{Industrial Implementation}

The methodology described above must be validated on realistic systems using
industrial-grade hardware to demonstrate practical feasibility. This research
will leverage the University of Pittsburgh Cyber Energy Center's partnership
with Emerson to implement and test the HAHACS methodology on production
control equipment. Emerson's Ovation distributed control system is widely
deployed in power generation facilities, including nuclear plants. The
Ovation platform provides a realistic target for demonstrating that formally
synthesized controllers can execute on industrial hardware meeting timing and
reliability requirements. The discrete automaton produced by reactive
synthesis will be compiled to run on Ovation controllers, with verification
that the implemented behavior matches the synthesized specification exactly.

For the continuous dynamics, we will use a small modular reactor simulation. The
SmAHTR (Small modular Advanced High Temperature Reactor) model provides a
relevant testbed for startup and shutdown procedures. The ARCADE (Advanced
Reactor Control Architecture Development Environment) interface will establish
communication between the Emerson Ovation hardware and the reactor simulation,
enabling hardware-in-the-loop testing of the complete hybrid controller.

The Emerson collaboration strengthens this work in two ways. Access to
system experts at Emerson ensures that implementation details of the Ovation
platform are handled correctly. Direct industry collaboration also provides an
immediate pathway for technology transfer and alignment with practical
deployment requirements.