Abstract

In this paper, the direct neural dynamic programming technique is utilized to solve the Hamilton-Jacobi-Bellman equation forward-in-time for the decentralized near optimal regulation of a class of nonlinear interconnected discrete-time systems with unknown internal subsystem and interconnection dynamics, while the input gain matrix is considered known. Even though the unknown interconnection terms are considered weak and functions of the entire state vector, the decentralized control is attempted under the assumption that only the local state vector is measurable. the decentralized nearly optimal controller design for each subsystem consists of two neural networks (NNs), an action NN that is aimed to provide a nearly optimal control signal, and a critic NN which evaluates the performance of the overall system. All NN parameters are tuned online for both the NNs. by using Lyapunov techniques it is shown that all subsystems' signals are uniformly ultimately bounded and that the synthesized subsystems inputs approach their corresponding nearly optimal control inputs with bounded error. Simulation results are included to show the effectiveness of the approach. © 2011 IEEE.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Comments

Natural Science Foundation of Jiangsu Province, Grant ECCS0624644

Keywords and Phrases

Decentralized control; Hamilton-Jacobi-Bellman equation; neural networks; optimal control

International Standard Serial Number (ISSN)

1045-9227

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Nov 2011

PubMed ID

21965197

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