工业机器人, 轨迹跟踪, 自适应控制, 迭代学习, PDAG算法, 重力补偿

A PDAG algorithm to form the feedback controller was proposed according to the characteristics that industrial robots often executed tasks repeatedly. The establishment of mathematical model of PDAG algorithm, with the application of a learning matrix to the error data, proved the stability and convergence of PDAG algorithm in theory. The gravity compensation was regarded as feed-forward control quantity to eliminate the effect of articulation gravity in dynamic process, to improve the convergence rate and the trajectory tracking precision of iterative learning control, and to conduct PDAG algorithm simulation as well. The experimental results show that with the PDAG algorithm control performance, compared to traditional non-compensation algorithm, the trajectory tracking error tends to be stable after iterative learning. Both the maximum positioning tracking error and the average tracking error of each articulation drops by 0.5 time compared to the condition without gravity compensation. Compared to PD algorithm with gravity compensation, the PDAG algorithm increases accuracy by max 81%, which means that the proposed PDAG algorithm is capable of achieving trajectory tracking effect with higher precision.