Disturbance Observer-Based Backstepping- Super Twisting Control for Robust Trajectory Tracking in Robot Manipulators

摘要

This article presents a robust adaptive control design for robot manipulators to track desired trajectories amid unknown disturbances and input saturation. The suggested controller integrates backstepping and super-twisting techniques, ensuring system stability and robustness. The backstepping method mitigates unmatched disturbances in a two-step process, while the super-twisting algorithm addresses matched perturbations and overshoot apparitions. A nonlinear observer enhances control efficacy against matched disturbances and input saturation, ensuring fast convergence via a quasi-nonsingular terminal sliding surface. This approach enables precise tracking with smooth control signals and avoids large feedback gains. An advanced adaptive reaching law dynamically adjusts the controller's behavior through a potential function, mimicking and enhancing various established reaching control laws. The designed method provides a flexible strategy with rapid convergence, minimal chattering, and adaptability to variation of system dynamics. Stability is confirmed using Lyapunov’s direct method, proving uniform boundedness of signals in the closed-loop system. The proposed controller was validated through simulations, experiments, and comparative analysis, demonstrating its superior performance.