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基于模型预测控制的工业机器人曲面跟踪方法研究
引用本文:杨真真,李明富,张黎明,邓旭康.基于模型预测控制的工业机器人曲面跟踪方法研究[J].机械工程学报,2022,58(19):24-33.
作者姓名:杨真真  李明富  张黎明  邓旭康
作者单位:1. 湘潭大学机械工程学院 湘潭 411105;2. 复杂轨迹加工工艺及装备教育部工程研究中心 湘潭 411105;3. 焊接机器人与应用技术湖南省重点实验室 湘潭 411105
基金项目:国家自然科学基金(51775470, 52075465)、湖南省战略性新兴产业科技攻关与重大科技成果转化项目(2019GK4025)和湖南省科技创新计划(2020RC4038)资助项目。
摘    要:工业机器人执行接触性作业任务时,通常需要稳定控制接触力,比如在磨抛过程中,不平稳的法向接触力容易影响表面质量。为解决力跟踪控制时法向控制速度易超调和不确定环境造成法向接触力不平稳的问题,提出一种基于模型预测控制的工业机器人曲面跟踪方法。首先,根据工件模型几何信息计算出末端工具的运动轨迹,再结合机器人当前位姿求解末端工具的笛卡儿速度;然后,建立末端工具与工件接触时的状态空间模型,并依据末端工具的姿态变化对法向阻尼系数进行在线调节;最后,根据实时力信号的反馈,利用模型预测控制算法对法向速度进行修正,实现曲面恒力跟踪。基于Staubli TX90工业机器人,在末端工具姿态不变和姿态改变的情况下分别进行了曲面跟踪实验,结果显示法向接触力波动范围分别为±1 N和±2 N,方差分别为0.038 1 N2和0.105 9 N2,能够达到较好的力跟踪效果。

关 键 词:模型预测控制  曲面跟踪  速度修正  恒力控制  
收稿时间:2022-01-06

Research on Surface Tracking Method of Industrial Robot Based on Model Predictive Control
YANG Zhenzhen,LI Mingfu,ZHANG Liming,DENG Xukang.Research on Surface Tracking Method of Industrial Robot Based on Model Predictive Control[J].Chinese Journal of Mechanical Engineering,2022,58(19):24-33.
Authors:YANG Zhenzhen  LI Mingfu  ZHANG Liming  DENG Xukang
Affiliation:1. School of Mechanical Engineering, Xiangtan University, Xiangtan 411105;2. Engineering Research Center of Complex Tracks Processing Technology and Equipment of Ministry of Education, Xiangtan 411105;3. Key Laboratory of Welding Robot and Application Technology of Hunan Province, Xiangtan 411105
Abstract:The stable control of contact force is generally required for industrial robot to perform the contact work. For example, in the grinding process, the surface quality can be easily affected by the unstable contact force in the normal direction. To solve the overshoot of normal speed and the unstable contact force in the normal direction, which caused by uncertain environment, a force tracking method for industrial robot based on model predictive control is proposed. Firstly, the trajectory of the end tool is calculated based on the geometric information of the workpiece. And the Cartesian velocity of the end tool is then calculated by combining with the current robot position. Secondly, the state-space model of contact status between the end tool and the workpiece is developed, and the damping coefficient in the normal direction is adjusted online based on the attitude of end tool. Thirdly, the normal velocity is corrected by the model predictive control algorithm based on the feedback of the real-time force signal to achieve constant force tracking of the surface. Finally, two surface tracking experiments are conducted under the situations of constant and changeable end tool attitude respectively, by Staubli TX90 industrial robot. The experimental results show that, the contact force in the normal direction fluctuated in the range of 1 N and 2 N with variance of 0.038 1 N2 and 0.105 9 N2, respectively, which can realize favorable force tracking.
Keywords:model predictive control  surface tracking  velocity correction  constant force control  
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