Command filtering‐based adaptive neural network control for uncertain switched nonlinear systems using event‐triggered communication

In this article, a command filtering‐based adaptive event‐triggered neural network control scheme is proposed for a class of uncertain switched nonlinear systems with unknown control coefficient and input saturation. First, radial basis function neural networks are used as function approximators to...

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Bibliographic Details
Published in:International journal of robust and nonlinear control Vol. 32; no. 11; pp. 6507 - 6522
Main Authors: Chen, Zhongyu, Niu, Ben, Zhang, Liang, Zhao, Jinfeng, Ahmad, Adil M., Alassafi, Madini O.
Format: Journal Article
Language:English
Published: Bognor Regis Wiley Subscription Services, Inc 25.07.2022
Subjects:
Adaptive control
artificial intelligence
Control systems design
Error compensation
event‐triggering mechanism
Filtration
Network control
Neural networks
Nonlinear systems
Nussbaum‐type function
Radial basis function
Saturation
Stability analysis
switched nonlinear systems
Tracking errors
ISSN:1049-8923, 1099-1239
Online Access:Get full text
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Summary:In this article, a command filtering‐based adaptive event‐triggered neural network control scheme is proposed for a class of uncertain switched nonlinear systems with unknown control coefficient and input saturation. First, radial basis function neural networks are used as function approximators to estimate unknown nonlinear functions. Then, an event‐triggering mechanism based on the tracking error is introduced to avoid the over‐consumption of communication resources. Furthermore, command filters are employed to solve the problem of complexity explosion that exists in conventional backstepping control design, and the error compensation signals are designed to reduce the errors caused by the filters. Considering that the unknown control gain and input saturation exist in many practical applications, a Nussbaum‐type function is thus introduced into the controller design to address these challenging issues. Finally, stability of the closed‐loop system is strictly proven under a standard Lyapunov stability analysis framework. The effectiveness of the proposed control scheme is illustrated by a simulation example.
Bibliography:Funding information
Education Committee Project of Liaoning Province, Grant/Award Number: LJ2019002
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.6154