基于迭代学习控制的码垛机器人轨迹优化

Track Optimization for Stacking Robots Based on Iterative Learning Control

提出一种学习控制方法用于码垛机器人的速度优化.通过对系统跟随误差学习,实现在误差允许范围内运动速度最大化,从而实现码垛效率的优化.本方法对系统建模要求较低,易于在实际控制器上实现.系统仿真实验结果表明,在带入机械臂系统参数后,在对迭代算法改进后的最优轨迹与未改进的标准轨迹比较后,前者通过改进局部速度,使机器人运转时间缩短了10%～20%.

In this paper, an innovative method based on ILC（iterative learning control） could be used to optimize the speed of the industrial robots. Learning through the system following error, the robotic system could achieve the optimum speed within the system following error,and gets the optimization of stacking robotic speed system. Further more,ILC requires little to model the robotic system, so it＇s easy to achieve itself in real machine. The results of the system simulation indicate that,under the circumstances of computing robotic system parameter, and comparing the optimization with ILC to the original results,ILC could increase the stacking speed by 10-20% through improving local speed.