Adaptive dynamic programming-based decentralised control for large-scale nonlinear systems subject to mismatched interconnections with unknown time-delay

This paper addresses decentralised optimal control problems for large-scale nonlinear systems subject to mismatched interconnections with unknown time-delay by adaptive dynamic programming. To eliminate the effects of mismatched interconnections with unknown time-delay, a novel local value function...

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Bibliographic Details
Published in:International journal of systems science Vol. 51; no. 15; pp. 2883 - 2898
Main Authors: Wu, Qiuye, Zhao, Bo, Liu, Derong
Format: Journal Article
Language:English
Published: London Taylor & Francis 17.11.2020
Taylor & Francis Ltd
Subjects:
Adaptive control
Adaptive dynamic programming
decentralised control
Decentralized control
Delay
Dynamic programming
Interconnections
Iterative algorithms
large-scale nonlinear systems
Nonlinear control
Nonlinear systems
Optimal control
reinforcement learning
Robust control
unknown mismatched interconnection
unknown time-delay
ISSN:0020-7721, 1464-5319
Online Access:Get full text
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Summary:This paper addresses decentralised optimal control problems for large-scale nonlinear systems subject to mismatched interconnections with unknown time-delay by adaptive dynamic programming. To eliminate the effects of mismatched interconnections with unknown time-delay, a novel local value function is constructed to transform the decentralised control problem into an optimal control problem. A local robust observer is established to identify the bound of the unknown interconnections. Then, based on the observer-critic architecture, the decentralised optimal control policy is achieved by solving local Hamiltonian-Jacobi-Bellman equation via local policy iteration algorithm. The stability of the closed-loop large-scale nonlinear system is guaranteed to be uniformly ultimately bounded by implementing a set of decentralised control policies. Simulation examples demonstrate the effectiveness of the proposed scheme.
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ISSN:0020-7721
1464-5319
DOI:10.1080/00207721.2020.1803439