Abstract
In this paper, the direct neural dynamic programming technique is utilized to solve the Hamilton Jacobi-Bellman (HJB) equation online and forward-in-time for the decentralized nearly optimal control of nonlinear interconnected discrete-time systems in affine form with unknown internal subsystem and interconnection dynamics. Only the state vector of the local subsystem is considered measurable. the decentralized optimal controller design for each subsystem consists of an action neural network (NN) that is aimed to provide a nearly optimal control signal, and a critic NN which approximates the cost function. the NN weights are tuned online for both the NNs. It is shown that all subsystems' signals are uniformly ultimately bounded (UUB) and that the subsystem inputs approach their corresponding nearly optimal control inputs with bounded error. © 2010 IEEE.
Recommended Citation
S. Mehraeen and S. Jagannathan, "Decentralized Nearly Optimal Control of a Class of Interconnected Nonlinear Discrete-time Systems by using Online Hamilton-Bellman-Jacobi Formulation," Proceedings of the International Joint Conference on Neural Networks, article no. 5596704, Institute of Electrical and Electronics Engineers, Jan 2010.
The definitive version is available at https://doi.org/10.1109/IJCNN.2010.5596704
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Decentralized Control; Hamilton-Jacobi-Bellman Equation; Neural Network; Nonlinear Discrete-time Systems; Optimal Control
International Standard Book Number (ISBN)
978-142446917-8
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2010
