Abstract
In this paper, an adaptive model-based event-triggered control of an uncertain linear discrete time system is developed. Measured input and output vectors and their history are utilized to express the unknown linear discrete-time system as an autoregressive Markov representation (ARMarkov). A novel adaptive model in the form of AR Markov is proposed and an update law is derived in order to estimate parameters of the ARMarkov model at triggered instants unlike periodic updates in standard adaptive control. Lyapunov method is used to derive the event trigger condition, prove boundedness of the parameter vector and asymptotic convergence of the outputs and states. A simulation example is utilized to verify theoretical claims and a comparison of the proposed with zero order hold (ZOH) and fixed model-based schemes is also discussed as part of simulation. © 2013 AACC American Automatic Control Council.
Recommended Citation
A. Sahoo et al., "Adaptive Event-triggered Control of a Uncertain Linear Discrete Time System using Measured Input and Output Data," Proceedings of the American Control Conference, pp. 5672 - 5677, article no. 6580726, Institute of Electrical and Electronics Engineers, Jan 2013.
The definitive version is available at https://doi.org/10.1109/acc.2013.6580726
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Keywords and Phrases
adaptive control; event-triggered; output feedback
International Standard Book Number (ISBN)
978-147990177-7
International Standard Serial Number (ISSN)
0743-1619
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 2013
