Adaptive Fixed-Time Neural Network Tracking Control of Nonlinear Interconnected Systems

In this article, a novel adaptive fixed-time neural network tracking control scheme for nonlinear interconnected systems is proposed. An adaptive backstepping technique is used to address unknown system uncertainties in the fixed-time settings. Neural networks are used to identify the unknown uncert...

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Bibliographic Details
Published inEntropy (Basel, Switzerland) Vol. 23; no. 9; p. 1152
Main Authors Li, Yang, Zhang, Jianhua, Xu, Xinli, Chin, Cheng Siong
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2021
MDPI
Subjects
Adaptive control
adaptive fixed-time
Closed loop systems
Control theory
Controllers
Convergence
Design
Explosions
neural network
Neural networks
Nonlinear control
nonlinear interconnected systems
Nonlinear systems
Process controls
Stability analysis
Systems design
Tracking control
Uncertainty
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Summary:In this article, a novel adaptive fixed-time neural network tracking control scheme for nonlinear interconnected systems is proposed. An adaptive backstepping technique is used to address unknown system uncertainties in the fixed-time settings. Neural networks are used to identify the unknown uncertainties. The study shows that, under the proposed control scheme, each state in the system can converge into small regions near zero with fixed-time convergence time via Lyapunov stability analysis. Finally, the simulation example is presented to demonstrate the effectiveness of the proposed approach. A step-by-step procedure for engineers in industry process applications is proposed.
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ISSN:1099-4300
1099-4300
DOI:10.3390/e23091152