Paper Info
Title
Adaptive Dynamic Programming for Decentralized Stabilization of Uncertain Nonlinear Large-Scale Systems With Mismatched Interconnections
Abstract
This paper presents a novel decentralized control strategy for a class of uncertain nonlinear large-scale systems with mismatched interconnections. First, it is shown that the decentralized controller for the overall system can be represented by an array of optimal control policies of auxiliary subsystems. Then, within the framework of adaptive dynamic programming, a simultaneous policy iteration (SPI) algorithm is developed to solve the Hamilton-Jacobi-Bellman equations associated with auxiliary subsystem optimal control policies. The convergence of the SPI algorithm is guaranteed by an equivalence relationship. To implement the present SPI algorithm, actor and critic neural networks are applied to approximate the optimal control policies and the optimal value functions, respectively. Meanwhile, both the least squares method and the Monte Carlo integration technique are employed to derive the unknown weight parameters. Furthermore, by using Lyapunov's direct method, the overall system with the obtained decentralized controller is proved to be asymptotically stable. Finally, the effectiveness of the proposed decentralized control scheme is illustrated via simulations for nonlinear plants and unstable power systems.
Year
DOI
Venue
2020
10.1109/TSMC.2018.2837899
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Keywords
DocType
Volume
Adaptive dynamic programming (ADP),decentralized control,large-scale systems,mismatched interconnections,reinforcement learning (RL)
Journal
50
Issue
ISSN
Citations 
8
2168-2216
9
PageRank 
References 
Authors
0.44
12
2
Name
Order
Citations
PageRank
Xiong Yang1364.51
Haibo He23653213.96