Author:
Liu Xiaoxiao,Chen Mengyuan
Abstract
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.
Funder
Innovative Research Group Project of the National Natural Science Foundation of China
Collaborative Innovation Project of Colleges and Universities of Anhui Province
Anhui University
Subject
Industrial and Manufacturing Engineering,Fluid Flow and Transfer Processes,Mechanical Engineering,Mechanics of Materials,Civil and Structural Engineering,Control and Systems Engineering