Wen Shuhuan, Zhu Jinghai, Li Xiaoli, Chen Shengyong
Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China.
Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China.
ISA Trans. 2014 Sep;53(5):1603-8. doi: 10.1016/j.isatra.2014.05.024. Epub 2014 Jun 24.
Robot force control is an essential issue in robotic intelligence. There is much high uncertainty when robot end-effector contacts with the environment. Because of the environment stiffness effects on the system of the robot end-effector contact with environment, the adaptive generalized predictive control algorithm based on quantitative feedback theory is designed for robot end-point contact force system. The controller of the internal loop is designed on the foundation of QFT to control the uncertainty of the system. An adaptive GPC algorithm is used to design external loop controller to improve the performance and the robustness of the system. Two closed loops used in the design approach realize the system׳s performance and improve the robustness. The simulation results show that the algorithm of the robot end-effector contacting force control system is effective.
机器人力控制是机器人智能中的一个关键问题。当机器人末端执行器与环境接触时，存在很大的不确定性。由于环境刚度对机器人末端执行器与环境接触系统的影响，基于定量反馈理论设计了自适应广义预测控制算法用于机器人端点接触力系统。内环控制器基于定量反馈理论设计，以控制系统的不确定性。采用自适应广义预测控制算法设计外环控制器，以提高系统的性能和鲁棒性。设计方法中使用的两个闭环实现了系统的性能并提高了鲁棒性。仿真结果表明，机器人末端执行器接触力控制系统的算法是有效的。
