Abstract: A neural network nonsingular terminal sliding mode (NTSM) control method with block approximation of dynamics model parameters is proposed to address the problem of model uncertainty and unknown perturbations in the robotic arm. First, a nonsingular terminal sliding mode surface is used in order to accelerate the convergence of the system tracking error and avoid the problem of singularity in the traditional terminal sliding mode. Second, multiple selfrecurrent wavelet neural network (SRWNN) chunks are utilized to approximate the unknown dynamics model parameters of the system, and the weights are adjusted by using the adaptive update law. Meanwhile, the approximation error of the SRWNN is compensated by designing a robust control term, and the system stability is proved using Lyapunov stability theory. Finally, the simulation analysis using MATLAB shows that the average steady state error of the joint angle tracking error is reduced by 31.9% and 76.5% for the chunked SRWNN sliding mode control compared with the sliding mode control and the overall SRWNN sliding mode control, respectively, which demonstrates that this method is a reliable and effective trajectory tracking control method.

Key words: selfrecurrent wavelet neural network, nonsingular terminal sliding mode, dynamical model, trajectory tracking