Data-Driven Adaptive Dynamic Programming for Optimal Control of Continuous-Time Multicontroller Systems With Unknown Dynamics

This paper investigates the optimal control of continuous-time multi-controller systems with completely unknown dynamics using data-driven adaptive dynamic programming (DD-ADP). In this investigation, all controllers take actions together as a team, and they have precisely the same cost function, wh...

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Vydáno v:IEEE access Ročník 10; s. 41503 - 41511
Hlavní autor: Zhao, Jingang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:2169-3536, 2169-3536
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Abstract This paper investigates the optimal control of continuous-time multi-controller systems with completely unknown dynamics using data-driven adaptive dynamic programming (DD-ADP). In this investigation, all controllers take actions together as a team, and they have precisely the same cost function, which is actually a fully cooperative game. According to optimal control theory, the HJB equation corresponding to the fully cooperative game is derived. To obtain the solution to HJB equation, a model-based policy iteration (PI) algorithm is first presented. On the basis of the PI algorithm, a DD-ADP algorithm without requiring the system dynamics is developed, and the neural networks (NNs) implementation scheme of the developed DD-ADP algorithm is given. Stability and convergence analysis are derived by Lyapunov theory. Finally, numerical simulation examples on linear and nonlinear multi-controller systems demonstrate the effectiveness of the designed scheme.
AbstractList This paper investigates the optimal control of continuous-time multi-controller systems with completely unknown dynamics using data-driven adaptive dynamic programming (DD-ADP). In this investigation, all controllers take actions together as a team, and they have precisely the same cost function, which is actually a fully cooperative game. According to optimal control theory, the HJB equation corresponding to the fully cooperative game is derived. To obtain the solution to HJB equation, a model-based policy iteration (PI) algorithm is first presented. On the basis of the PI algorithm, a DD-ADP algorithm without requiring the system dynamics is developed, and the neural networks (NNs) implementation scheme of the developed DD-ADP algorithm is given. Stability and convergence analysis are derived by Lyapunov theory. Finally, numerical simulation examples on linear and nonlinear multi-controller systems demonstrate the effectiveness of the designed scheme.
Author Zhao, Jingang
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SubjectTerms Adaptive control
Adaptive dynamic programming
Algorithms
Control systems
Control theory
Controllers
Cost function
Dynamic programming
fully cooperative games
Game theory
Games
Heuristic algorithms
Iterative methods
Mathematical models
multi-controller systems
Neural networks
Nonlinear control
Nonlinear systems
Optimal control
Performance analysis
Stability analysis
System dynamics
System effectiveness
unknown dynamics
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Title Data-Driven Adaptive Dynamic Programming for Optimal Control of Continuous-Time Multicontroller Systems With Unknown Dynamics
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