Abstract
A novel adaptive critic-Based multilayer neural network (NN) controller in discrete-time is designed to deliver a desired tracking performance for a class of nonlinear systems in the presence of magnitude constraints on the input. Reinforcement learning scheme in discrete time is proposed for the NN controller, where the action generating NN learning is performed based on a certain performance measure, which is supplied from a critic. using the Lyapunov approach and with a novel weight update, the uniform ultimately boundedness (UUB) of the closed loop tracking error and weight estimates are shown. the adaptive critic NN does not require an offline learning phase and the weights can be initialized at zero or randomly. It is shown via simulation that taking magnitude constraints on the input would help reduce transients.
Recommended Citation
P. He and S. Jagannathan, "Adaptive Critic Neural Network-Based Controller for Nonlinear Systems with Input Constraints," Proceedings of the IEEE Conference on Decision and Control, vol. 6, pp. 5709 - 5714, Institute of Electrical and Electronics Engineers, Jan 2003.
The definitive version is available at https://doi.org/10.1109/CDC.2003.1271914
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
International Standard Serial Number (ISSN)
2576-2370; 0743-1546
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 2003
