Abstract
In this paper, a novel neural network (NN) adaptive dynamic programming (ADP) control scheme for distributed parameter systems (DPS) governed by parabolic partial differential equations (PDE) is introduced in the presence of control constraints and unknown system dynamics. First, Galerkin method is utilized to develop a relevant reduced order system which captures the dominant dynamics of the DPS. Subsequently, a novel control scheme is proposed over finite horizon by using NN ADP. To relax the requirement of system dynamics, a novel NN identifier is developed. More-over, a second NN is proposed to estimate online the time-varying non-quadratic value function from the Hamilton-Jacobi-Bellman (HJB) equation. Subsequently, by using the identifier and the value function estimator, the optimal control input that inherently lies in actuation limits is obtained. A local uniform ultimate boundedness (UUB) of the closed-loop system is verified by using standard Lyapunov theory. The performance of proposed control scheme and effects of its design parameters are successfully verified by simulation on a diffusion reaction process.
Recommended Citation
B. Talaei et al., "Neural Network Dynamic Progamming Constrained Control of Distributed Parameter Systems Governed by Parabolic Partial Differential Equations with Application to Diffusion-reaction Processes," 2014 IEEE International Symposium on Intelligent Control, ISIC 2014, pp. 1861 - 1866, article no. 6967634, Institute of Electrical and Electronics Engineers, Nov 2014.
The definitive version is available at https://doi.org/10.1109/ISIC.2014.6967634
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
International Standard Book Number (ISBN)
978-147997406-1
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
25 Nov 2014
Comments
National Stroke Foundation, Grant None