Modified State Observer based Two-Way ETNAC Design for Uncertain Linear Systems


In this study, a neural network (NN) based two-way event-triggered controller (ETC) design is presented for uncertain linear systems. Dynamic triggering conditions are developed for both state and control data transmission. These triggering conditions are based on real performance parameters including estimation/tracking errors which make control execution more relevant instead of the extended time sampling as seen in most ETC literature. The other unique feature of this ETNAC scheme is the online uncertainty approximation even during inter-event time which makes control robust and efficient. A Modified State Observer (MSO) used in this study to replicate the system to estimate the system uncertainty. In this way it not only saves data communication over network but also reduces computational efforts. System stability and event-triggering conditions are established using Lyapunov analysis. A benchmark numerical example is used to illustrate the effectiveness of the proposed method.

Meeting Name

2019 International Joint Conference on Neural Networks, IJCNN 2019 (2019: Jul. 14-19, Budapest, Hungary)


Mechanical and Aerospace Engineering

Keywords and Phrases

Linear systems; Numerical methods; State estimation; System stability, Computational effort; Data-communication; Dynamic triggering; Lyapunov analysis; Neural network (nn); Performance parameters; System uncertainties; Uncertain linear system, Uncertainty analysis

International Standard Book Number (ISBN)


Document Type

Article - Conference proceedings

Document Version


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© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

01 Jul 2019