Feasibility Conditions-Free Prescribed Performance Decentralized Fault-Tolerant Neural Control of Constrained Large-Scale Systems

This article investigates the command filter-based decentralized prescribed performance adaptive fault-tolerant compensation control strategy for uncertain nonlinear large-scale systems subject to asymmetric time-varying full-state constraints. Via integrating the performance function with command f...

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Published inIEEE transactions on systems, man, and cybernetics. Systems Vol. 53; no. 5; pp. 3152 - 3164
Main Authors Yang, Wen, Jiang, Yulian, He, Xiao, Zhu, Yanzheng, Wang, Shenquan
Format Journal Article
LanguageEnglish
Published New York IEEE 01.05.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Actuator faults
Actuators
Adaptive control
Adaptive systems
Algorithms
Artificial neural networks
Asymmetry
Constraints
Control systems design
Control theory
decentralized control
Fault tolerance
Fault tolerant systems
Feasibility
full-state constraints
Function analysis
large-scale systems
Liapunov functions
Nonlinear control
Nonlinear systems
prescribed performance
Time-varying systems
Tracking errors
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Summary:This article investigates the command filter-based decentralized prescribed performance adaptive fault-tolerant compensation control strategy for uncertain nonlinear large-scale systems subject to asymmetric time-varying full-state constraints. Via integrating the performance function with command filter-based backstepping technique, the prescribed performance control problem is addressed, under which the complexity of controller design is reduced. Under the prescribed performance control framework, the nonlinear transformed function is constructed so as to ensure that the asymmetric time-varying full-state constraints free from feasibility conditions imposed on virtual control signals are not violated. Besides, the effect of infinite number of time-varying actuator faults is compensated with the aid of projection adaption design. Furthermore, based on the piecewise Lyapunov function analysis, it is rigorously testified that entire involved signals are bounded, desired constraints are not breached and tracking errors within the predefined domains. Finally, the effective performances of the developed control algorithm are confirmed by some simulation results.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2022.3222857