Abstract
This paper develops a novel neural network (NN) based near optimal boundary control scheme for distributed parameter systems (DPS) governed by semi linear parabolic partial differential equations (PDE) in the presence of control constraints and unknown system dynamics. First, finite difference method (FDM) is utilized to develop a reduced order system which represents the discretized dynamics of PDE system. Subsequently, a near optimal control scheme is proposed for the discretized system by using NN based approximate dynamic programming (ADP). To relax the requirement of system dynamics, a NN identifier is utilized. Moreover, a second NN is proposed to estimate a non-quadratic value function online. Subsequently, by using the identifier and the value function estimator, the optimal control input that inherently falls within actuator limits is obtained. A local uniformly ultimately boundedness (UUB) of the closed-loop system is verified by using standard Lyapunov theory. The performance of the proposed control scheme is successfully verified by simulation on a diffusion reaction process.
Recommended Citation
B. Talaei et al., "Near Optimal Boundary Control of Distributed Parameter Systems Modeled as Parabolic Pdes by using Finite Difference Neural Network Approximation," Proceedings of the IEEE Conference on Decision and Control, pp. 6776 - 6781, article no. 7040453, Institute of Electrical and Electronics Engineers, Jan 2014.
The definitive version is available at https://doi.org/10.1109/CDC.2014.7040453
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
International Standard Book Number (ISBN)
978-147997746-8
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 2014
