基于PDAG算法的工业机器人轨迹跟踪

PDAG Algorithm Based Trajectory Tracking for Industrial Robots

根据工业机器人执行的作业任务具有重复性的特点,提出一种PDAG算法。将学习矩阵作用于误差数据建立PDAG算法的数学模型,从理论上证明了PDAG算法的稳定性和收敛性。将重力补偿作为前馈控制量,用以消除动态过程中关节重力的影响,提高迭代学习控制的收敛速度和轨迹跟踪精度。进行了PDAG算法仿真,结果表明,PDAG算法的控制性能与传统的无补偿算法相比,迭代学习后轨迹跟踪误差趋于稳定,各关节最大位置跟踪误差和平均跟踪误差均比没有重力补偿时降低50%,且比有重力补偿的PD算法精度提高最大达81%。实验证明提出的PDAG算法可以获得更高精度的跟踪效果。

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 0.5time 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.