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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Zusammenfassung: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 specifications cannot be guaranteed. Clearly, this can largely reduce the amount of transmission data. Besides, since the event-trigger error is discontinuous because of the event-triggering mechanism, the stability analysis in the classical sense may not be guaranteed. To solve this problem, we formulate the event-triggered network control systems as a nonlinear impulsive dynamical system, and a novel Lyapunov theorem is used to show the stability properties of the closed-loop systems. Finally, two simulation examples are given to illustrate the effectiveness of the theoretical results.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2018.2828140