A Control System Test Bed for Demonstration of Distributed Computational Intelligence Applied to Reconfiguring Heterogeneous Systems

Srivastava, Sanjeev; Cartes, D.A.; Maturana, Francisco; Ferrese, Frank; Zink, Margo R.; Meeker, Rick; Carnahan, Dan; Staron, Raymond J.; Scheidt, David; Huang, Kuo-Chung

doi:10.1109/systems.2007.374668

Cited by 11 publications

(7 citation statements)

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“…1) is considered that captures the scalability, controllability, redundancy, and interdependencies that make the control of hydronic systems difficult. This testbed is based on the work of (Srivastava et al 2008)…”

Section: Hydronic Network Demonstratormentioning

confidence: 99%

“…Nuclear power, military, and other high-risk applications are demanding ever greater reliability, yielding hydronic networks with redundant flow paths and damage detection systems (Srivastava et al 2008). Lastly, flexibility in HVAC systems can be leveraged to provide value-added resources to electric grid operators, yielding dynamic interdependencies between these two largescale systems (Hao et al 2014).…”

Section: Introductionmentioning

confidence: 99%

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Development of a Scalable Distributed Model Predictive Control System for Hydronic Networks with Bilinear and Hybrid Dynamics

Kane

Lynch

Scruggs

2018

J. Comput. Civ. Eng.

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Hydronic heating and cooling systems are growing in complexity as the buildings and other infrastructure systems they serve demand greater efficiency and become larger. Increasing the scale and interconnectivity of these systems yields higher probabilities of component failure along with computational tractability issues. This paper presents a distributed hydronic control architecture featuring scalable computations and resiliency to component failure in both the cyberand physical-domains. In this agent-based control system, an agent is defined as a set of co-located components in the cyber-and physical-domains which together have sensations, actions, and/or goals. The proposed architecture consists of three types of agents: pumps, valves, and loads. Respectively, these agents solve a convex constraint satisfaction problem, a steady-state hybrid control problem, and a scalar bilinear model predictive control problem. The network on which these agents transfer thermal energy and on which they communicate are co-designed to share the same graph representation. The resulting coordinated behavior achieves the global control objective: maintain the thermal loads at safe temperatures while minimizing pump power. Analytical analysis, simulations, and laboratory experiments are employed to investigate the controller. The results demonstrate steady-state robustness to component failures in the cyber-2-physical domains, superiority to benchmarks, and suitable although not guaranteed to be optimal real-time performance.

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Section: Hydronic Network Demonstratormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Development of a Scalable Distributed Model Predictive Control System for Hydronic Networks with Bilinear and Hybrid Dynamics

Kane

Lynch

Scruggs

2018

J. Comput. Civ. Eng.

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“…We tested our reconfiguration strategy using a scaled-down version of a ship's coupled power and water subsystems [14]. Both power and chilled water are necessary for consumers to function, and every system needs sufficient power and must be kept cool enough to operate.…”

Section: A Test Systemmentioning

confidence: 99%

Reconfiguring connected resource distribution systems

Rosendall

Scheidt

2011

Computational Intelligence in Control and Automation (CICA)

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In this paper we consider complex mechanical systems that operate within harsh, uncertain environments where mission success depends critically upon the system's ability to monitor and maintain its own internal health. In particular, we focus on the problem of reconfiguring a system's internal resource distribution subsystems in response to one or more damage events. We present a graph-based approach that guarantees the resulting states adhere to a desirable resource segregation constraint. We also provide results using this approach in conjunction with optimization techniques to reconfigure a representative hardware test platform.

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“…For example, consider electrical and fluid systems on naval ships (Srivastava, Cartes, Maturana, Ferrese, Pekala, Zink, Meeker, Carnahan, Staron, Scheidt, & Huang, 2008). Such systems consist of components such as valves, pipes, pumps, and electrical relays.…”

Section: Introductionmentioning

confidence: 99%

Best-First Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation

Timmons

Williams

2020

jair

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There is an increasing desire for autonomous systems to have high levels of robustness and safety, attained through continuously planning and self-repairing online. Underlying this is the need to accurately estimate the system state and diagnose subtle failures. Estimation methods based on hybrid discrete and continuous state models have emerged as a method of precisely computing these estimates. However, existing methods have difficulty scaling to systems with more than a handful of components. Discrete, consistency based state estimation capabilities can scale to this level by combining best-first enumeration and conflict-directed search. While best-first methods have been developed for hybrid estimation, conflict-directed methods have thus far been elusive as conflicts learn inconsistencies from constraint violation, but probabilistic hybrid estimation is relatively unconstrained. In this paper we present an approach to hybrid estimation that unifies best-first enumeration and conflict-directed search through the concept of "bounding" conflicts, an extension of conflicts that represent tighter bounds on the cost of regions of the search space. This paper presents a general best-first enumeration algorithm based on bounding conflicts (A*BC) and a hybrid estimation method using this enumeration algorithm. Experiments show that an A*BC powered state estimator produces estimates up to an order of magnitude faster than the current state of the art, particularly on large systems.

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A Control System Test Bed for Demonstration of Distributed Computational Intelligence Applied to Reconfiguring Heterogeneous Systems

Cited by 11 publications

References 0 publications

Development of a Scalable Distributed Model Predictive Control System for Hydronic Networks with Bilinear and Hybrid Dynamics

Development of a Scalable Distributed Model Predictive Control System for Hydronic Networks with Bilinear and Hybrid Dynamics

Reconfiguring connected resource distribution systems

Best-First Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation

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