协作机器人参数辨识与误差补偿算法研究

Research on parameter identification and errorcompensation algorithms of collaborative robots

为探索协作机器人动态特性,提高末端定位精度,以库卡LBR轻型类人手臂协作机器人模型为例进行研究。基于RBF神经网络和滑模控制算法设计协作机器人动力学控制策略并分析动态特性和末端位置误差。基于Levenberg-Marquardt非线性阻尼最小二乘算法进行协作机器人参数辨识和误差补偿。ADAMS-Matlab联合仿真结果表明:基于RBF神经网络设计的滑模控制器动态控制效果较好,极限工况末端误差平均约为4.7 mm,主要是重力负载的影响。基于Levenberg-Marquardt非线性阻尼最小二乘算法进行变参数误差补偿后末端平均误差小于0.2 mm,有效提升了位置精度,为协作机器人的控制和误差补偿研究提供了理论基础。

As intelligent operating assistants,collaborative robots have opened up a wide range of application scenarios in fields of industry,service and medical treatment.In order to explore the dynamic characteristics of collaborative robots and improve the accuracy of terminal positioning,this paper takes the KUKA LBR lightweight humanoid arm collaborative robot model as an example for research.Based on the RBF neural network and the synovial control algorithm,the dynamic control strategy of the collaborative robot is designed and the dynamic characteristics and end position error are analyzed.Parameter identification and error compensation of collaborative robots are carried out based on the Levenberg-Marquardt nonlinear damping least squares algorithm.The ADAMS-Matlab joint simulation shows that the dynamic control effect of a sliding mode controller based on the RBF neural network is better.The average terminal error under an extreme working condition is about 4.7 mm,which is mainly due to the influence of gravity load.After variable parameter error compensation,the average terminal error is less than 0.2 mm,which effectively improves the position accuracy and provides a theoretical basis for the research of collaborative robot control and error compensation.