摘要
研究了去毛刺机器人的轨迹跟踪控制。考虑到在工业机器人去毛刺加工过程中,由于受非重复性扰动、毛刺大小不同引起切削力变化以及动态建模不确定性因素的影响,传统的迭代学习控制算法无法精确规划出去毛刺机器人的打磨路径,且很难实现高精度的跟踪控制,不能保证系统的稳定性,提出了一种鲁棒性迭代学习控制算法,旨在提高去毛刺机器人的跟踪性能和对环境的抗干扰能力。该算法由于引入了鲁棒项,与传统的迭代学习控制算法相比,抗干扰能力得到增强,跟踪示教轨迹的性能得到提高,跟踪轨迹精度得到改善,解决了传统迭代学习控制算法需要确定模型才能完成打磨轨迹的精度要求的问题。仿真试验验证了该鲁棒迭代学习算法的鲁棒性和轨迹跟踪精度。
The study investigated the path tracking control problem in the burring process of an industrial robot, and found that the traditional iterative learning control algorithm cannot precisely plan the polishing path of a burring ro- bot due to the influences of non-repetitive disturbances, cutting force variation caused by burr size difference, un- certainty in dynamic modeling and other uncertain factors, so the higher precise tracking control can not be a- chieved, and the stability of the burring system cannot be guaranteed. Then, a kind of robust iterative learning con- trol algorithm was put forward to improve burring robots＇ The adoption of a robustness item, makes this algorithm tracking performance and anti-interference ability. robust when under interference and more accurate when tracking path, and traditional iterative learning control＇ s problem that the accuracy requirements of polishing path can be completed only after determining the model can be solved. The simulation experiment verified the ro- bustness of the robust iterative learning algorithm and showed its little tracking error.
出处
《高技术通讯》
CAS
CSCD
北大核心
2015年第12期1062-1068,共7页
Chinese High Technology Letters
基金
863计划(2013AA040501)
重庆市科委151机器人工程课题(cstc2013jcsf-zdzxqqX0005)资助项目
关键词
去毛刺
鲁棒迭代学习控制
跟踪轨迹
burring, robust iterative learning control, tracking path
