Trajectory Tracking Closed-Loop Cooperative Control of Manipulator Neural Network and Terminal Sliding Model

To address the issue of low trajectory tracking accuracy in six-degree-of-freedom robotic arms, this study proposes a trajectory tracking control strategy that integrates a Radial Basis Function Neural Network (RBFNN) with non-singular fast terminal sliding mode (NFTSM) control. (1) The Lagrangian method is utilized to develop the dynamic model of the robotic arm. At the same time, a non-singular fast terminal sliding surface is designed to accelerate trajectory convergence and resolve the singularity problem commonly associated with traditional sliding mode control by integrating nonlinear and fast terminal terms. (2) The RBF neural network is employed to globally approximate and compensate for uncertainties in the model and variations in the parameters of the robotic arm. (3) To confirm the overall stability of the control system with the proposed NFTSM control strategy, the Lyapunov stability theory is applied to formulate a Lyapunov function. (4) The six-degree-of-freedom robotic manipulator is simulated in the MATLAB/Simulink environment to assess the effectiveness of the proposed control method. In addition, experimental validation is carried out on a real robotic manipulator to verify the effectiveness of the proposed method. The simulation and experimental results show that, compared with NFTSM and RBFNN-SMC, the proposed control strategy significantly enhances the trajectory tracking accuracy of the six-degree-of-freedom robotic manipulator, thereby offering an effective and practical solution for its trajectory tracking control.