Abstract
In this paper, the Bellman equation is used to solve the stochastic optimal control of unknown linear discrete-time system with communication imperfections including random delays, packet losses and quantization. a dynamic quantizer for the sensor measurements is proposed which essentially provides system states to the controller. to eliminate the effect of the quantization error, the dynamics of the quantization error bound and an update law for tuning its range are derived. Subsequently, by using adaptive dynamic programming technique, the infinite horizon optimal regulation of the uncertain NCS is solved in a forward-in-time manner without using value and/or policy iterations by using Q-function and reinforcement learning. the asymptotic stability of the closed-loop system is verified by standard Lyapunov stability theory. Finally, the effectiveness of the proposed method is verified by simulation results. © 2012 IEEE.
Recommended Citation
Q. Zhao et al., "Adaptive Dynamic Programming-Based State Quantized Networked Control System Without Value And/or Policy Iterations," Proceedings of the International Joint Conference on Neural Networks, article no. 6252525, Institute of Electrical and Electronics Engineers, Aug 2012.
The definitive version is available at https://doi.org/10.1109/IJCNN.2012.6252525
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
Adaptive Dynamic Programming; Networked Control System; Optimal Control; Quantization
International Standard Book Number (ISBN)
978-146731490-9
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
22 Aug 2012
