Abstract
In this paper, the fixed final-time near optimal output regulation of affine nonlinear discrete-time systems with unknown system dynamics is considered. First, a neural network (NN)-based observer is proposed to reconstruct both the system state vector and control coefficient matrix. Next, actor-critic structure is utilized to approximate the time-varying solution of the Hamilton-Jacobi-Bellman (HJB) equation or value function. To satisfy the terminal constraint, a new error term is defined and incorporated in the NN update law so that the terminal constraint error is also minimized over time. A NN with constant weights and time-dependent activation function is employed to approximate the time-varying value function which subsequently is utilized to generate the fixed final time near optimal control policy due to NN reconstruction errors. The proposed scheme functions in a forward-in-time manner without offline training phase. The effectiveness of the proposed method is verified via simulation. © 2014 American Automatic Control Council.
Recommended Citation
Q. Zhao et al., "Fixed Final-time Near Optimal Regulation of Nonlinear Discrete-time Systems in Affine Form using Output Feedback," Proceedings of the American Control Conference, pp. 4643 - 4648, article no. 6858756, Institute of Electrical and Electronics Engineers, Jan 2014.
The definitive version is available at https://doi.org/10.1109/ACC.2014.6858756
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
finite-horizon; Hamilton-Jacobi-Bellman equation; neural network; optimal regulation
International Standard Book Number (ISBN)
978-147993272-6
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 2014
