Adaptive neural prescribed performance control for switched pure-feedback non-linear systems with input quantization
Zhongwen Cao, Liang Zhang, Adil M. Ahmad, Fawaz E. Alsaadi, Madini O. Alassafi
Abstract
Purpose This paper aims to investigate an adaptive prescribed performance control problem for switched pure-feedback non-linear systems with input quantization. Design/methodology/approach By using the semi-bounded continuous condition of non-affine functions, the controllability of the system can be guaranteed. Then, a constraint variable method is introduced to ensure that the tracking error satisfies the prescribed performance requirements. Meanwhile, to avoid the design difficulties caused by the input quantization, a non-linear decomposition method is adopted. Finally, the feasibility of the proposed control scheme is verified by a numerical simulation example. Findings Based on neural networks and prescribed performance control method, an adaptive neural control strategy for switched pure-feedback non-linear systems is proposed. Originality/value The complex deduction and non-differentiable problems of traditional prescribed performance control methods can be solved by using the proposed error transformation approach. Besides, to obtain more general results, the restrictive differentiability assumption on non-affine functions is removed.