Fixed-time composite neural learning control of state-constrained nonlinear uncertain systems

Wu, Zhonghua; Zheng, Shuaipeng; Zhao, Yunji; Liu, Bojun; Ni, Junkang

doi:10.1016/j.jfranklin.2021.08.013

Cited by 11 publications

(1 citation statement)

References 75 publications

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“…The article [30] presented a switching control strategy between a prediction error-based intelligent control law and a robust controller to achieve globally FnT stability, where the prediction error is extracted from a modified SPEM. The authors in [31] designed a neural adaptive FxT backstepping algorithm with composite learning laws to enhance NNs' performance. Nevertheless, the computational burden of the composite adaptive law updating is heavy, since the number of composite update weights is closely related to the number of neurons in the RBFNN.…”

Section: Introductionmentioning

confidence: 99%

Globally Intelligent Adaptive Finite-/Fixed- Time Tracking Control for Strict-Feedback Nonlinear Systems via Composite Learning Approaches

Wang¹

2023

Preprint

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This article focuses on the globally composite adaptive law-based intelligent finite-/fixed-time (FnT/FxT) tracking control issue for a family of uncertain strictfeedback nonlinear systems. First, intelligent approximators with new composite updating laws are developed to model uncertain nonlinear terms, which encompass prediction errors to enhance intelligent approximators' learning behaviors and fewer online learning parameters to diminish computational burden. Then, a novel smooth switching function coupled with robust controllers is designed to pull system states back when the transients are out of the approximators' active domain. After that, a modified FnT/FxT backstepping technique is constructed to render output to follow the reference trajectory, and an adaptive law is employed to alleviate the impact of external disturbances. It is theoretically confirmed that the proposed control strategies ensure globally FnT/FxT boundedness of all the closedloop variables. Finally, the validity of theoretical results is testified via a simulation case.

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Section: Introductionmentioning

confidence: 99%

Globally Intelligent Adaptive Finite-/Fixed- Time Tracking Control for Strict-Feedback Nonlinear Systems via Composite Learning Approaches

Wang¹

2023

Preprint

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A composite neural network-based adaptive sliding mode control method for reluctance actuator maglev system

Shu

et al. 2023

Neural Comput & Applic

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Improved Adaptive Fuzzy Control for Non-Strict Feedback Nonlinear Systems: a Dynamic Compensation System Approach

Sun

Xia

et al. 2022

Applied Mathematics and Computation

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Fixed-time composite neural learning control of state-constrained nonlinear uncertain systems

Cited by 11 publications

References 75 publications

Globally Intelligent Adaptive Finite-/Fixed- Time Tracking Control for Strict-Feedback Nonlinear Systems via Composite Learning Approaches

Globally Intelligent Adaptive Finite-/Fixed- Time Tracking Control for Strict-Feedback Nonlinear Systems via Composite Learning Approaches

A composite neural network-based adaptive sliding mode control method for reluctance actuator maglev system

Improved Adaptive Fuzzy Control for Non-Strict Feedback Nonlinear Systems: a Dynamic Compensation System Approach

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