Robust Nonsingular Terminal Sliding Mode Control for SCARA Robots Using Wavelet Neural Networks

This study proposes a robust nonsingular terminal sliding mode control strategy for SCARA robots, incorporating adaptive wavelet neural networks to enhance system stability, precision, and robustness under time-varying uncertainties and external disturbances. The nonsingular terminal sliding mode controller ensures strong robustness and finite-time convergence. Meanwhile, integrating wavelet neural networks with adaptive control enables real-time approximation of unknown nonlinearities and external effects, significantly reducing the common chattering phenomenon in traditional sliding mode controllers. This approach maintains stable SCARA robot performance, particularly in high-precision applications subject to significant disturbances. Simulation outcomes confirm that the developed controller delivers good tracking accuracy, stability, and robust adaptability under uncertain conditions.