Time-varying non-singular fast terminal sliding-mode control based on improved variable power–power reaching law

A manipulator often cannot converge rapidly within finite time and has low tracking accuracy owing to factors such as manipulator model errors and external disturbances. To address these problems, this paper proposes a time-varying non-singular fast terminal sliding mode control scheme based on an improved variable power–power reaching law. First, three radial basis function neural networks (RBFNNs) are employed to approximate the dynamic parameters of the manipulator model and thus realize model-free control. Second, to achieve faster finite-time convergence of the system state, a time-varying non-singular fast terminal sliding-mode (NFTSM) surface is designed according to the system state change. In addition, an improved variable power–power reaching law is adopted to avoid chatter and eliminate approximation errors. Finally, comparative simulation experiments are conducted using a 2-DOF manipulator as the research object. The results show that the proposed control scheme facilitates fast convergence, high-precision trajectory tracking, and effective suppression of system chatter under complex uncertainties, thereby confirming its utility and superiority.