Adaptive Neural Control of Pure-Feedback Nonlinear Systems With Event-Triggered Communications

This paper is concerned with the adaptive event-triggered control problem for a class of pure-feedback nonlinear systems. Unlike the existing results where the control execution is periodic, the new proposed scheme updates the controller and the neural network weights only when desired control speci...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 29; H. 12; S. 6242 - 6251
Hauptverfasser:	Li, Yuan-Xin, Yang, Guang-Hong
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.12.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Adaptive control Adaptive systems Algorithms Artificial neural networks backstepping Closed loop systems Communication Computer Simulation Control systems event-triggered control (ETC) Feedback Humans Models, Neurological Network control Neural networks Neural Networks (Computer) Nonlinear control Nonlinear Dynamics nonlinear pure-feedback systems Nonlinear systems Stability analysis System effectiveness
ISSN:	2162-237X, 2162-2388, 2162-2388
Online-Zugang:	Volltext
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