Intelligent robust control design for a 3-DoF industrial manipulator

Precise control of multi-degree-of-freedom (DoF) robotic manipulators is essential in industrial settings, where high tracking accuracy, fast convergence, and robustness to uncertainty are critical. However, many existing methods continue to suffer from high chattering, slow response, and limited adaptability to disturbances or abrupt input variations. This paper presents a control framework for a 3-DoF industrial manipulator that integrates nonsingular terminal sliding mode control (NTSMC), backstepping design, and a clustering-enhanced radial basis function neural network (RBFNN). The NTSMC component provides finite-time convergence and strong robustness to matched uncertainties, leading to superior tracking performance with a root mean squared error (RMSE) of 0.000202 under ideal conditions and 0.002634 under disturbances, both significantly better than those achieved by Fuzzy-sliding mode control (SMC) and SMC-neural network (NN) baselines. Backstepping enables smooth coordination of the control input across dynamic subsystems, which reduces settling time to 0.0375 seconds in the ideal case and 0.2808 seconds under disturbance. The clustering-enhanced RBFNN allows structured and efficient initialization of network parameters, identifying effective centers and widths in fewer than 500 iterations and improving generalization to both periodic and abrupt input signals. The combined design eliminates the common trade-off between chattering suppression and trajectory smoothness and demonstrates practical potential for real-time, high-precision control in uncertain industrial environments.