Abstract

In this paper, finite horizon stochastic optimal control issue has been studied for linear networked control system (LNCS) in the presence of network imperfections such as network-induced delays and packet losses by using adaptive dynamic programming (ADP) approach. Due to an uncertainty in system dynamics resulting from network imperfections, the stochastic optimal control design uses a novel adaptive estimator (AE) to solve the optimal regulation of uncertain LNCS in a forward-in-time manner in contrast with backward-in-time Riccati equation-based optimal control with known system dynamics. Tuning law for unknown parameters of AE has been derived. Lyapunov theory is used to show that all the signals are uniformly ultimately bounded (UUB) with ultimate bounds being a function of initial values and final time. In addition, the estimated control input converges to optimal control input within finite horizon. Simulation results are included to show the effectiveness of the proposed scheme. © 2013 IEEE.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Comments

National Science Foundation, Grant ECCS #1128281

Keywords and Phrases

Adaptive Dynamics Programming and Reinforcement learning; Adaptive Estimator; Finite horizon; Networked Control System; Stochastic Optimal Control

International Standard Book Number (ISBN)

978-146735925-2

International Standard Serial Number (ISSN)

2325-1867; 2325-1824

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 Dec 2013

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