Adaptive Neural Tracking Control for a Class of Pure-Feedback Systems With Output Constraints Based on Event-Triggered Strategy

Adaptive Neural Tracking Control for a Class of Pure-Feedback Systems With Output Constraints Based on Event-Triggered Strategy

In this paper, an adaptive event-triggered tracking control problem is considered for a class of pure-feedback nonlinear systems with output constraints. The mean value theorem is used to transform the pure-feedback system in non-affine form into a system in affine form. In addition, the radial basi...

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Main Authors: Chunlei Zhang, Lidong Wang, Chuang Gao, Qiang Qu, Xuebo Chen
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Pure-feedback nonlinear systems
event-triggered control
output constraints
RBF neural network
Online Access:https://ieeexplore.ieee.org/document/9050773/
id doaj-881c0baaa0144eb784c9bfcfc9edc74e
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spelling doaj-881c0baaa0144eb784c9bfcfc9edc74e2021-03-30T01:29:54ZengIEEEIEEE Access2169-35362020-01-018615936160310.1109/ACCESS.2020.29843449050773Adaptive Neural Tracking Control for a Class of Pure-Feedback Systems With Output Constraints Based on Event-Triggered StrategyChunlei Zhang0https://orcid.org/0000-0002-1350-481XLidong Wang1https://orcid.org/0000-0003-3923-849XChuang Gao2https://orcid.org/0000-0003-3838-5076Qiang Qu3https://orcid.org/0000-0003-4442-1133Xuebo Chen4https://orcid.org/0000-0001-6799-7667School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaSchool of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, ChinaIn this paper, an adaptive event-triggered tracking control problem is considered for a class of pure-feedback nonlinear systems with output constraints. The mean value theorem is used to transform the pure-feedback system in non-affine form into a system in affine form. In addition, the radial basis function neural network (RBF NN) control is used to approximate the unknown nonlinear function in the system and the tracking error of the controller is limited to a small constant boundary by using the positive obstacle Lyapunov function. An adaptive controller for a class of pure-feedback systems is established, which based on the backstepping control theory and event-triggered control theory, it can ensure all the closed-loop signals are bounded and avoid the Zeno-behavior. The simulation results prove the effectiveness of the controller design.https://ieeexplore.ieee.org/document/9050773/Pure-feedback nonlinear systemsevent-triggered controloutput constraintsRBF neural network
collection DOAJ
language English
format Article
sources DOAJ
author Chunlei Zhang
Lidong Wang
Chuang Gao
Qiang Qu
Xuebo Chen
spellingShingle Chunlei Zhang
Lidong Wang
Chuang Gao
Qiang Qu
Xuebo Chen
Adaptive Neural Tracking Control for a Class of Pure-Feedback Systems With Output Constraints Based on Event-Triggered Strategy
IEEE Access
Pure-feedback nonlinear systems
event-triggered control
output constraints
RBF neural network
author_facet Chunlei Zhang
Lidong Wang
Chuang Gao
Qiang Qu
Xuebo Chen
author_sort Chunlei Zhang
title Adaptive Neural Tracking Control for a Class of Pure-Feedback Systems With Output Constraints Based on Event-Triggered Strategy
title_short Adaptive Neural Tracking Control for a Class of Pure-Feedback Systems With Output Constraints Based on Event-Triggered Strategy
title_full Adaptive Neural Tracking Control for a Class of Pure-Feedback Systems With Output Constraints Based on Event-Triggered Strategy
title_fullStr Adaptive Neural Tracking Control for a Class of Pure-Feedback Systems With Output Constraints Based on Event-Triggered Strategy
title_full_unstemmed Adaptive Neural Tracking Control for a Class of Pure-Feedback Systems With Output Constraints Based on Event-Triggered Strategy
title_sort adaptive neural tracking control for a class of pure-feedback systems with output constraints based on event-triggered strategy
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2020-01-01
description In this paper, an adaptive event-triggered tracking control problem is considered for a class of pure-feedback nonlinear systems with output constraints. The mean value theorem is used to transform the pure-feedback system in non-affine form into a system in affine form. In addition, the radial basis function neural network (RBF NN) control is used to approximate the unknown nonlinear function in the system and the tracking error of the controller is limited to a small constant boundary by using the positive obstacle Lyapunov function. An adaptive controller for a class of pure-feedback systems is established, which based on the backstepping control theory and event-triggered control theory, it can ensure all the closed-loop signals are bounded and avoid the Zeno-behavior. The simulation results prove the effectiveness of the controller design.
topic Pure-feedback nonlinear systems
event-triggered control
output constraints
RBF neural network
url https://ieeexplore.ieee.org/document/9050773/
work_keys_str_mv AT chunleizhang adaptiveneuraltrackingcontrolforaclassofpurefeedbacksystemswithoutputconstraintsbasedoneventtriggeredstrategy
AT lidongwang adaptiveneuraltrackingcontrolforaclassofpurefeedbacksystemswithoutputconstraintsbasedoneventtriggeredstrategy
AT chuanggao adaptiveneuraltrackingcontrolforaclassofpurefeedbacksystemswithoutputconstraintsbasedoneventtriggeredstrategy
AT qiangqu adaptiveneuraltrackingcontrolforaclassofpurefeedbacksystemswithoutputconstraintsbasedoneventtriggeredstrategy
AT xuebochen adaptiveneuraltrackingcontrolforaclassofpurefeedbacksystemswithoutputconstraintsbasedoneventtriggeredstrategy
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