具有执行器故障的非线性系统指定性能控制

doi:10.12052/gdutxb.210110

Abstract

Abstract: In this paper, considering a class of nonlinear systems with full state constraints and actuator faults, an adaptive neural network output feedback fault-tolerant control algorithm with prescribed performance is proposed. A state observer is constructed to solve the unmeasurable states problem. Unknown nonlinear functions in the systems are approximated by radial basis function neural networks (RBF NNs) . By introducing the nonlinear mapping, the systems with state constraints are transformed into novel systems without state constraints. Moreover, a novel performance function is utilized to guarantee that the tracking error converges within a preset time. Meanwhile, the convergence speed can be adjusted through the parameter design. Finally, it is proved that the control algorithm ensures that all signals in the closed-loop systems are semi-globally uniformly ultimately bounded. The effectiveness of the algorithm is verified by a numerical simulation.

Key words: adaptive neural networks control, actuator faults, full state constraints, prescribed performance, output feedback, dynamic surface control

CLC Number:

TP13

Qiu Jun-hao, Cheng Zhi-jian, Lin Guo-huai, Ren Hong-ru, Lu Ren-quan. Prescribed Performance Control for a Class of Nonlinear Pure-feedback Systems with Actuator Faults[J].Journal of Guangdong University of Technology, 2023, 40(02): 55-63.

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Prescribed Performance Control for a Class of Nonlinear Pure-feedback Systems with Actuator Faults

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