Title

Optimal Event-triggered Control of Uncertain Linear Networked Control Systems: A Co-Design Approach

Abstract

In this paper, a co-design approach for event-based optimal state regulation of an uncertain linear networked control system is presented. Both the transmission intervals and the control policy are optimized by introducing a novel performance index such that the error in the control policy due to event-based transmission can be maximized. The event-triggering mechanism uses the worst case control input error as threshold to decide the optimal transmission instants. Stochastic Q-learning approach is used to design both the control policy and event-triggering condition without explicit knowledge of the system dynamics. The event-based Q-function parameters are updated using a hybrid scheme both at triggering instants and during inter-event times to accelerate the parameter convergence. The asymptotic stability in the mean square of the closed-loop system is demonstrated using Lyapunov analysis with the assumptions of persistence of excitation of regression vector. Finally, numerical results are included to substantiate the analytical design.

Meeting Name

2017 IEEE Symposium Series on Computational Intelligence, SSCI (2017: Nov. 27-Dec. 1, Honolulu, HI)

Department(s)

Electrical and Computer Engineering

Comments

This research is supported by NSF grant 1406533 and Intelligent Systems Center, Rolla and Startup fund Oklahoma State University, Stillwater, OK.

Keywords and Phrases

Artificial intelligence; Asymptotic stability; Closed loop systems; Linear networks; Stochastic systems; Uncertainty analysis; Event-triggered controls; Optimal transmission; Parameter convergence; Performance indices; Persistence of excitation; Q-learning approach; Stability in the mean; Transmission intervals; Networked control systems

International Standard Book Number (ISBN)

978-1-5386-2726-6

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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