Tunable Predefined Time Nonsingular Terminal Sliding Mode Control Based on Neural Network for Nonlinear Systems

This paper proposes a novel predefined time nonsingular terminal sliding mode control (TSMC) based on radial basis function neural network (RBFNN) for nonlinear systems. Firstly, a new lemma of tunable predefined time stability (PTS) is proposed, where the introduction of an adjustable parameter can adjust the stability time of the system and makes the design of the controller more flexible. Secondly, based on the proposed lemma, a new control method is proposed, which not only guarantees the PTS of the system, but also solves the singularity problem of the traditional TSMC and the problem of unknown model information. Finally, through comparative simulation, it is verified that the proposed method has good control performance.