Abstract
A novel neural network (NN) -Based output feedback controller with magnitude constraints is designed to deliver a desired tracking performance for a class of multi-input-multi-output (MIMO) discrete-time strict feedback nonlinear systems. Reinforcement learning in discrete time is proposed for the output feedback controller, which uses three NNs: 1) a NN observer to estimate the system states with the input-output data; 2) a critic NN to approximate certain strategic utility function; and 3) an action NN to minimize both the strategic utility function and the unknown dynamics estimation errors. the magnitude constraints are manifested as saturation nonlinearities in the output feedback controller design. using the Lyapunov approach, the uniformly ultimate boundedness (UUB) of the state estimation errors, the tracking errors and weight estimates is shown.
Recommended Citation
P. He and S. Jagannathan, "Reinforcement Learning-Based Output Feedback Control of Nonlinear Systems with Input Constraints," Proceedings of the American Control Conference, vol. 3, pp. 2563 - 2568, Institute of Electrical and Electronics Engineers, Jan 2004.
The definitive version is available at https://doi.org/10.23919/acc.2004.1383851
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
International Standard Serial Number (ISSN)
0743-1619
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 2004
