TY  - JOUR
AU  - Qiu, Jun-hao
AU  - Cheng, Zhi-jian
AU  - Lin, Guo-huai
AU  - Ren, Hong-ru
AU  - Lu, Ren-quan
PY  - 2023
TI  - Prescribed Performance Control for a Class of Nonlinear Pure-feedback Systems with Actuator Faults
JO  - Journal of Guangdong University of Technology
SN  - 1007-7162
SP  - 55
EP  - 63
VL  - 40
IS  - 2
AB  - 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.
UR  - https://doi.org/10.12052/gdutxb.210110
DO  - 10.12052/gdutxb.210110