Event Triggered Neuroadaptive Controller (ETNAC) Design for Uncertain Linear Systems
Abstract
In this paper a novel event triggered neural network (NN) based adaptive controller is presented for linear systems with multiple uncertainties. Controller design is primarily based on an observer, called the modified state observer (MSO). MSO is used to approximate uncertainties online, with two tunable gains, which allow fast approximation without inducing high frequency oscillations. On the other hand state information is transmitted to the feedback loop only when required in an aperiodic fashion. This aperiodic update is triggered by a dynamic condition based on errors. Consequently, this event triggered control (ETC) not only reduces the control computations, but also bring down the communication cost. Lyapunov analysis is used to show the stability of the system as well as to develop the event sample triggering condition. Efficacy of the proposed controllers is demonstrated using a numerical example.
Recommended Citation
A. Gaffoor et al., "Event Triggered Neuroadaptive Controller (ETNAC) Design for Uncertain Linear Systems," Proceedings of the IEEE Conference on Decision and Control (2018, Miami Beach, FL), Institute of Electrical and Electronics Engineers (IEEE), Dec 2018.
Meeting Name
57th Conference on Decision and Control (2018: Dec. 17-19, Miami Beach, FL)
Department(s)
Electrical and Computer Engineering
Research Center/Lab(s)
Intelligent Systems Center
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
19 Dec 2018
