Abstract

This paper presents a novel stochastic event-based near optimal control strategy to regulate a networked control system (NCS) represented as an uncertain nonlinear continuous time system. An online stochastic actor-critic neural network (NN) based approach is utilized to achieve the near optimal regulation in the presence of network constraints, such as, network induced time-varying delays and random packet losses under event-based transmission of the feedback signals. The transformed nonlinear NCS in discrete-time after the incorporation the delays and packet losses are utilized for the actor-critic NN based controller design. To relax the knowledge of the control coefficient matrix, a NN based identifier is used. Event sampled state vector is utilized as NN inputs and their respective weights are updated non-periodically at the occurrence of events. Further, an event-trigger condition is designed by using the Lyapunov technique to ensure ultimate boundedness of all the closed-loop signals and save network resources and computation. Moreover, policy and value iterations are not utilized for the stochastic optimal regulator design. Finally, the analytical design is verified by using a numerical example by carrying out Monte-Carlo simulations.

Department(s)

Electrical and Computer Engineering

Second Department

Computer Science

Keywords and Phrases

adaptive dynamic programming; Event-triggered control; networked control systems; neural networks; optimal control

International Standard Book Number (ISBN)

978-147994553-5

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

14 Jan 2014

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