Event Triggered Neuroadaptive Controller (ETNAC) Design for Uncertain Linear Systems
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.
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.
57th Conference on Decision and Control (2018: Dec. 17-19, Miami Beach, FL)
Electrical and Computer Engineering
Intelligent Systems Center
Article - Conference proceedings
© 2018 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
19 Dec 2018