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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Bibliographic Details
Published in:IEEE access Vol. 10; pp. 41503 - 41511
Main Author: Zhao, Jingang
Format: Journal Article
Language:English
Published: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3168032