Robust trajectory tracking control for collaborative robots based on learning feedback gain self-adjustment

Abstract. A robust position control algorithm with learning feedback gain automatic adjustment for collaborative robots under uncertainty is proposed, aiming to compensate for the disturbance effects of the system. First, inside the proportional-derivative (PD) control framework, the robust controller is designed based on model and error. All of the model's uncertainties are represented by functions with upper bounds in order to surmount the uncertainties induced by parameter changes and unmodeled dynamics. Secondly, the feedback gain is automatically adjusted by learning, so that the control feedback gain is automatically adjusted iteratively to optimize the desired performance of the system. Thirdly, the Lyapunov minimax method is used to demonstrate that the proposed controller is both uniformly bounded and uniformly ultimately bounded. The simulations and experimental results of the robot experimental platform demonstrate that the proposed control achieves outstanding performance in both transient and steady-state tracking. Also, the proposed control has a simple structure with few parameters requiring adjustment, and no manual setting is required during parameter setting. Moreover, the robustness and efficacy of the robot's trajectory tracking with uncertainty are significantly enhanced.