---

authors:

  - "Wood, Richard T."
  
  - "Upadhyaya, Belle R."
  
  - "Floyd, Dan C."
  

citekey: "woodAutonomousControlFramework2017"

alias: "woodAutonomousControlFramework2017"

publish_date: 2017-08-01

journal: "Nuclear Engineering and Technology"
volume: 49
issue: 5
pages: 896-904
last_import: 2025-07-30

---

# An autonomous control framework for advanced reactors

## Indexing Information

Published: 2017-08

**DOI**
[10.1016/j.net.2017.07.001](https://doi.org/10.1016/j.net.2017.07.001)
#Autonomous-Control, #Instrumentation-and-Control-System,
#Small-Modular-Reactor


#InSecondPass



>[!Abstract] Several Generation IV nuclear reactor concepts
>have goals for optimizing investment recovery through
>phased introduction of multiple units on a common site with
>shared facilities and/or reconfigurable energy conversion
>systems. Additionally, small modular reactors are suitable
>for remote deployment to support highly localized
>microgrids in isolated, underdeveloped regions. The
>long-term economic viability of these advanced reactor
>plants depends on significant reductions in plant
>operations and maintenance costs. To accomplish these
>goals, intelligent control and diagnostic capabilities are
>needed to provide nearly autonomous operations with
>anticipatory maintenance. A nearly autonomous control
>system should enable automatic operation of a nuclear power
>plant while adapting to equipment faults and other upsets.
>It needs to have many intelligent capabilities, such as
>diagnosis, simulation, analysis, planning,
>reconfigurability, self-validation, and decision. These
>capabilities have been the subject of research for many
>years, but an autonomous control system for nuclear power
>generation remains as-yet an unrealized goal. This article
>describes a functional framework for intelligent,
>autonomous control that can facilitate the integration of
>control, diagnostic, and decision-making capabilities to
>satisfy the operational and performance goals of power
>plants based on multimodular advanced reactors.>[!seealso]
>Related Papers
>

## Annotations

### Notes

[[An-autonomous-control-framework-for-advanced-reactors-notes]]

### Highlights From Zotero
>[!tip] Brilliant
>
> The long-term economic viability of these advanced reactor
> plants depends on significant reductions in plant
> operations and maintenance costs. To accomplish these
> goals, intelligent control and diagnostic capabilities are
> needed to provide nearly autonomous operations with
> anticipatory maintenance. 2025-07-06 12:44 pm
>

>[!done] Important
>
> An SMR is generally characterized by: (1) an electrical
> generating capacity of less than 300 MWe (megawatt
> electric), (2) a  primary system that is entirely or
> substantially fabricated within a factory, and (3) a
> primary system that can be transported by truck or rail to
> the plant site. 2025-07-06 12:47 pm
>

>[!tip] Brilliant
>
> he current US nuclear industry average for O&M staff is
> roughly one person per every 2 megawatts of generated
> power. 2025-07-06 12:48 pm
>

>[!done] Important
>
> Current automated control technologies for nuclear power
> plants are reasonably mature, and highly  automated
> control for an SMR is clearly feasible under optimum
> circumstances. Autonomous control is primarily intended to
> account for the nonoptimum circumstances when degradation,
> failure, and other off-normal events challenge the
> performance of the reactor, and the capability for
> immediate human intervention is constrained. There are
> clear gaps in the development and demonstration of
> autonomous control capabilities for the specific domain of
> nuclear power operations. 2025-07-06 1:04 pm
>


>[!done] Important
>
> Autonomy extends the scope of primary control functions.
> Such capabilities can consist of automated control during
> all operating modes, process performance optimization
> (e.g., self-tuning), continuous monitoring, and diagnosis
> of performance indicators as well as trends for
> operational and safety-related parameters, diagnosis of
> component health, flexible control to address both
> anticipated and unanticipated events and to provide
> protection of  life-limited components (such as batteries
> and actuators), adaptation to changing or degrading
> conditions, and validation and maintenance of control
> system performance. Key characteristics of autonomy
> include intelligence, robustness, optimization,
> flexibility, and adaptability. Intelligence facilitates
> minimal or no reliance on human intervention and can
> accommodate an integrated, whole system approach to
> control. It implies embedded decision-making and
> management/planning authority. Intelligence in control
> provides for anticipatory action based on system knowledge
> and event prediction. 2025-07-06 6:03 pm
>

>[!highlight] Highlight 
>
> As a minimum requirement of autonomy, the SMR plant
> control system must be able to switch between normal
> operational modes automatically (i.e., automatic control).
> Additionally, reactor protective action must be available
> if the desired operational conditions cannot be achieved.
> 2025-07-06 6:07 pm
>

>[!done] Important
>
> Unlike conventional reactor operational concepts, in which
> the primary defense against potentially adverse conditions
> resulting from off-normal events is to scram the reactor,
> the objective of autonomous control is to limit the
> progression of off-normal events and minimize the need for
> shutdown. This is especially true in situations where the
> nuclear power plant is the stabilizing generation source
> on a small electric grid. 2025-07-06 6:10 pm
>

>[!tip] Brilliant
>
> To illustrate the autonomous functionality that can be
> provided for the SMR plant control system, two fault
> management scenarios are considered in which detection and
> response are described. The first scenario relates to
> fault adaptation in the case of sensor failure. The
> indicators from surveillance and diagnostic functions that
> the plant control system can employ include divergence of
> redundant measurements, conflict between predicted (based
> on analytical or relational estimation) and measured
> values, and detection and isolation of a confirmed fault.
> The prospective response can include substitution of a
> redundant measurement or utilization of a diverse
> measurement. An example of the latter would be using
> neutron flux instead of temperature (i.e., core thermal
> power) as a power measurement. Switching to an alternate
> control algorithm may prove necessary for faulted or
> suspect measurements. The second scenario relates to fault
> avoidance in the case of a degrading actuator. The
> indicators of an incipient failure can be prediction of
> actuator failure based on prognostic modeling (e.g., fault
> forecasting) or detection of sluggish response to
> commands. The prospective response can be to switch to an
> alternate control strategy to avoid incipient failure by
> reducing stress on the suspect component. An example would
> be utilizing manipulation of core heat removal (e.g.,
> coolant density change) instead of direct reactivity
> insertion (e.g., control element movement) to control
> reactor power. 2025-07-06 6:15 pm
>

>[!done] Important
>
> Although having a highly reliable plant control system is
> important, that fact is of limited value if the control
> system cannot accommodate plant degradation without
> immediate human intervention or scram. In such a case, the
> result is a highly reliable control system that becomes
> ineffective because the plant has changed. 2025-07-06 6:16
> pm
>

>[!highlight] Highlight 
>
> A three-level hierarchy is typical for robotic
> applications [8,30,31]. The three layers in top-to-bottom
> hierarchical order are the planner layer, the executive
> layer, and the functional layer. The general concept of
> the hierarchy is that commands are issued by higher levels
> to lower levels, and response data flows from lower levels
> to higher levels in the multi-tiered framework.
> Intelligence increases with increasing level within the
> hierarchy. Each of the three interacting tiers has a
> principal role. Basically, the functional layer provides
> direct control, the executive layer provides sequencing of
> action, and the planner layer provides deliberative
> planning. 2025-07-06 6:19 pm
>
> *Kinda mimmics the Purdue model? Application layer, scada
> layer, then what would be enterprise layer?*



>[!tip] Brilliant
>
> Key characteristics that are feasible through autonomous
> control include    Intelligence to confirm system
> performance and detect degraded or failed conditions
> Optimization to minimize stress on SMR components and
> efficiently react to operational events without
> compromising system integrity   Robustness to accommodate
> uncertainties and changing conditions   Flexibility and
> adaptability to accommodate failures through
> reconfiguration among available control system elements or
> adjustment of control system strategies, algorithms, or
> parameters 2025-07-06 12:51 pm
>

>[!done] Important
>
> Given anticipated operational imperatives to utilize
> technology with demonstrated (or at least high
> probability) readiness, it is not practical to strive for
> the high-end extreme of autonomy in first-generation SMRs.
> Instead, modest advancement beyond fully automatic control
> to allow extended fault tolerance for anticipated events
> or degraded conditions and some predefined
> reconfigurability is the most realistic goal for an
> initial application of SMR plant autonomous control.
> 2025-07-06 1:00 pm
>

>[!tip] Brilliant
>
> The primary technical gap relates to decision capabilities
> (e.g., strategic, interpretive, adaptive, predictive).
> Technology development and demonstration activities are
> needed to provide the desired technical readiness for
> implementation of an SMR autonomous control system. In
> particular, the capabilities to monitor, trend, detect,
> diagnose, decide, and self-adjust must be established
> within an integrated functional architecture to enable
> control system autonomy. 2025-07-06 1:00 pm
>


### Follow-Ups

>[!example] One of the most fully digital plants currently
>in operation in the United States is the Oconee Nuclear
>Station [14]. The three units at Oconee have digital
>reactor protection systems and a digital integrated control
>system (ICS). The digital ICS coordinates the main control
>actions of multiple control loops through an integrated
>master controller that establishes feedforward control
>demands based on desired overall core thermal power. The
>ICS also has provisions for supplementary support actions
>among control loops to facilitate optimized performance.
>- [ ]  #Follow-Up
  
>[!example] There is an architectural approach for nearly
>autonomous control systems that has been developed through
>simulated nuclear power applications (see Fig. 2). As part
>of research into advanced multimodular nuclear reactor
>concepts, such as the ALMR, the International Reactor
>Innovative and Secure (IRIS), and representative advanced
>SMR concepts, a supervisory control system architecture was
>devised [24e26]. This approach provides a framework for
>autonomous control while supporting a high-level interface
>with operations staff, who can act as plant supervisors.
>The final authority for decisions and goal setting remains
>with the human, but the control system assumes expanded
>responsibilities for normal control action, abnormal event
>response, and system fault tolerance.
>- [ ]  #Follow-Up