灰色预测在机器人力顺应控制中的应用

Application of gray prediction in robotic force compliance control

大部分机器人控制系统都存在时延性,而过长的时延势必对机器人力顺应控制产生不利的影响,利用灰色系统预测模型对机器人末端受到的力进行预测,提前将其输入到阻抗控制器中,驱动机器人提前响应,降低控制回路纯时延对系统实时性的影响。机器人试验平台是FANUCLRMate200ic六自由度工业机器人,采用在线预更新JOB的方法实现机器人的连续运动控制,造成控制系统存在较大的时延,所以需要对其受力进行多步预测才能达到预期的效果。试验表明,灰色预测模型将系统纯时延造成的位置误差降低到不加预测时的43.11%。

Most robot-control systems are time-delayed,and too long time-delay has negative impact on robot compliance control.The model of gray prediction is adopted to predict the force received at the end of the robot,and the force is inputted into the impedance controller in advance,which will drive the robot ahead of time and reduce the effect of time delay in the control loop.The robot experiment platform serves as a FANUC LR Mate 200ic 6-DOF industrial robot,which realizes the continuous motion control of the robot with the aid of online pre-updating JOB,thus causing a higher delay of control system.Thereby,the multi-step prediction of force is required to achieve the desired effect.The experimental results show that the model of gray prediction reduces the position error caused by the pure time delay to 43.11% without prediction.