Self-organizing feature selection fuzzy neural network-based terminal sliding mode control for uncertain nonlinear systems.

A composite terminal sliding mode controller (CTSMC) for a kind of uncertain nonlinear system (UNS) is developed in this study. The primary aim of the design is to enhance the control performance of the CTSMC by learning its unknown parameters using a newly fuzzy neural network (FNN). Firstly, the stability and convergence of CTSMC for UNS with known parameters are demonstrated. Secondly, since some parameters of actual UNS are unmeasurable, a self-organizing feature selection fuzzy neural network (SOFSFNN) is intended to approach these unknown parts. Finally, the CTSMC using SOFSFNN is applied to UNS. The outcomes demonstrate that it has minimal tracking error, good robustness, and the ability to dynamically modify the network structure. • An improved composite terminal sliding mode controller is designed for a class of uncertain nonlinear systems. • Self-organizing feature selection fuzzy neural network is newly developed. • Results confirm that the proposed controller provides superior tracking performance against uncertainties. [ABSTRACT FROM AUTHOR]