Abstract
To achieve high-performance trajectory tracking for a manipulator, this study proposes a novel sliding mode control strategy incorporating a nonlinear disturbance observer. The observer is designed to estimate unknown models in real-time, enabling feedforward compensation for various uncertainties such as modeling errors, joint friction, and external torque disturbances. The control law is formulated by integrating the Backstepping method, Lyapunov theory, and global fast terminal sliding mode theory, ensuring global convergence to zero within finite time and enhancing system robustness. To address the inherent chattering issue in sliding mode control, a hybrid reaching law is developed by combining the exponential and power reaching laws. Additionally, the improved-fal (Imp-fal) function replaces the sign function in the switching control law, improving system response speed, preventing overshoot, and optimizing gain beyond the threshold value. Through simulation and comparative experiments conducted using MATLAB/Simulink, the controller model exhibited a 16.4% average reduction in the mean square value of tracking errors compared to existing control strategies, with improvements observed in various performance indicators. When applied to a self-developed three-degree-of-freedom manipulator experimental platform, the controller demonstrated a roughly 55% increase in tracking accuracy and a decrease in response time by approximately 45% compared to existing strategies. The experimental results validate the effectiveness, superiority, and practicality of the control strategy, providing a feasible solution for high-performance trajectory tracking in robotic arm systems.