动力学参数辨识的SCARA机器人PTP加速度优化
|
摘要
针对SCARA机器人高速运动时驱动力矩超限对减速机造成损伤的问题,提出一种基于动力学参数辨识的PTP加速度优化方法。通过对粘滞+库伦摩擦模型进行改进得到更好的表征高速运动时的摩擦模型,并由激励轨迹最小二乘法完成SCARA机器人改进后动力学模型参数辨识。进一步通过改进的动力学模型对SCARA机器人的PTP运动进行力矩预测,选取合适的迭代步长通过寻优算法得到最优的PTP加速度。ADAMS仿真和力矩预测实验表明,改进后的SCARA机器人动力学模型具有更高的力矩预测精度,加速度寻优算法平均耗时8 ms满足工程实时性要求,所得最优加速度在保证运行效率不降低的同时使得多点位PTP运动峰值转矩从112.2 N·m降低到了84.19 N·m,有效的提高减速机的使用寿命。
A method for improving the point to point(PTP) acceleration of a selective compliance assembly robot arm(SCARA) manipulator is designed to eliminate the mechanical damage to the reducer due to the unlimited torque when the manipulator operates at a high speed. Instead of the Coulomb and viscous friction model, we propose an improved friction model to better represent the high-speed friction and identify the improved dynamic parameters of the SCARA manipulator by using the excitation trajectory and the least squares method. Furthermore, the identified dynamic parameters are used to predict the driving torque of the PTP acceleration of the SCARA manipulator and to implement the optimization algorithm with the well-chosen to obtain the optimal acceleration. Simulation and experimental results show that the improved dynamic model has a better torque prediction accuracy and higher computational efficiency of 8 ms on average, thus ensuring the real-time control. Above all, the optimal acceleration accomplishes a high efficiency with low torque from 112.2 N·m to 84.19 N·m of the multi-point PTP acceleration, which effectively extends the life span of the reducer.
A method for improving the point to point(PTP) acceleration of a selective compliance assembly robot arm(SCARA) manipulator is designed to eliminate the mechanical damage to the reducer due to the unlimited torque when the manipulator operates at a high speed. Instead of the Coulomb and viscous friction model, we propose an improved friction model to better represent the high-speed friction and identify the improved dynamic parameters of the SCARA manipulator by using the excitation trajectory and the least squares method. Furthermore, the identified dynamic parameters are used to predict the driving torque of the PTP acceleration of the SCARA manipulator and to implement the optimization algorithm with the well-chosen to obtain the optimal acceleration. Simulation and experimental results show that the improved dynamic model has a better torque prediction accuracy and higher computational efficiency of 8 ms on average, thus ensuring the real-time control. Above all, the optimal acceleration accomplishes a high efficiency with low torque from 112.2 N·m to 84.19 N·m of the multi-point PTP acceleration, which effectively extends the life span of the reducer.
引文
[1]李俊阳.空间润滑谐波减速器失效机理及其加速寿命试验方法研究[D].重庆:重庆大学,2012Li J Y.Failure mechanism theory and accelerated life testing method research for space lubrication harmonic drive[D].Chongqing:Chongqing University,2012(in Chinese)
[2]Ding L,Wu H T,Yao Y,et al.Dynamic model identification for 6-DOF industrial robots[J].Journal of Robotics,2015(11):471478
[3]Bingl■ Z,Karahan O.Dynamic identification of Staubli RX-60 robot using PSO and LS methods[J].Expert Systems with Applications,2011,38(4):4136-4149
[4]Rogers-Marcovitz F,Kelly A.On-line mobile robot model identification using integrated perturbative dynamics[M]//Experimental Robotics.Berlin,Heidelberg:Springer,2014:417-431
[5]Abdelhedi F,Bouteraa Y,Chemori A,et al.Nonlinear PIDand feedforward control of robotic manipulators[C]//Proceedings of the 2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering.Hammamet,Tunisia:IEEE,2014:349-354
[6]贺红林,何文丛,刘文光,等.神经网络与计算力矩复合的机器人运动轨迹跟踪控制[J].农业机械学报,2013,44(5):270-275He H L,He W C,Liu W G,et al.Tracking control of robot using hybrid controller based on neural network and computed torque[J].Transactions of the Chinese Society for Agricultural Machinery,2013,4(5):270-[[[[[[275(in Chinese)
[7]王铁军.基于ADAMS的串联机器人运动可靠性仿真[D].沈阳:东北大学,2006Wang T J.Motion reliability simulation based on ADAMS of a serial-link robot[D].Shenyang:Northeastern University,2006(in Chinese)
[8]A~ngel L,Pérez M P,Díaz-quintero C,et al.ADAMS/MATLAB Co-Simulation:dynamic systems analysis and control tool[J].Applied Mechanics and Materials,2012,232:527-531
[9]李仕建.齿轮传动伺服系统非线性摩擦补偿控制研究[D].重庆:重庆大学,2015Li S J.Nonlinear friction compensation control of gear drive servo system[D].Chongqing:Chongqing University,2015(in Chinese)
[10]朱坚民,费家人,黄春燕.工业机器人空间位置精度预测模型研究[J].制造业自动化,2016,38(3):47-52Zhu J M,Fei J R,Huang C Y.Study on the prediction model of spatial location precision for industrial robots[J].Manufacturing Automation,2016,38(3):47-52(in Chinese)
[11]Calanca A,Capisani L M,Ferrara A,et al.MIMOclosed loop identification of an industrial robot[J].IEEETransactions on Control Systems Technology,2011,19(5):1214-1224
[12]Plooij M,Wolfslag W,Wisse M.Robust feedforward control of robotic arms with friction model uncertainty[J].Robotics and Autonomous Systems,2015,70:83-91
[13]赵登步,白瑞林,沈程慧,等.SCARA机器人点对点运动轨迹规划方法[J].计算机工程,2015,41(8):306-312Zhao D B,Bai R L,Shen C H,et al.Trajectory planning method of point-to-point motion for SCARArobot[J].Computer Engineering,2015,41(8):306-312(in Chinese)
[14]Macfarlane S,Croft E A.Design of jerk bounded trajectories for online industrial robot applications[C]//Proceedings of 2011 IEEE International Conference on Robotics and Automation.Seoul,South Korea:IEEE,2001:979-984
[15]Ata A A,Zawawi A E,Razek M A E.Jerk bounded trajectory planning for non-holonomic mobile manipulator[C]//Proceedings of the 27th European Conference on Modelling and Simulation.2013:733-738
[2]Ding L,Wu H T,Yao Y,et al.Dynamic model identification for 6-DOF industrial robots[J].Journal of Robotics,2015(11):471478
[3]Bingl■ Z,Karahan O.Dynamic identification of Staubli RX-60 robot using PSO and LS methods[J].Expert Systems with Applications,2011,38(4):4136-4149
[4]Rogers-Marcovitz F,Kelly A.On-line mobile robot model identification using integrated perturbative dynamics[M]//Experimental Robotics.Berlin,Heidelberg:Springer,2014:417-431
[5]Abdelhedi F,Bouteraa Y,Chemori A,et al.Nonlinear PIDand feedforward control of robotic manipulators[C]//Proceedings of the 2014 15th International Conference on Sciences and Techniques of Automatic Control and Computer Engineering.Hammamet,Tunisia:IEEE,2014:349-354
[6]贺红林,何文丛,刘文光,等.神经网络与计算力矩复合的机器人运动轨迹跟踪控制[J].农业机械学报,2013,44(5):270-275He H L,He W C,Liu W G,et al.Tracking control of robot using hybrid controller based on neural network and computed torque[J].Transactions of the Chinese Society for Agricultural Machinery,2013,4(5):270-[[[[[[275(in Chinese)
[7]王铁军.基于ADAMS的串联机器人运动可靠性仿真[D].沈阳:东北大学,2006Wang T J.Motion reliability simulation based on ADAMS of a serial-link robot[D].Shenyang:Northeastern University,2006(in Chinese)
[8]A~ngel L,Pérez M P,Díaz-quintero C,et al.ADAMS/MATLAB Co-Simulation:dynamic systems analysis and control tool[J].Applied Mechanics and Materials,2012,232:527-531
[9]李仕建.齿轮传动伺服系统非线性摩擦补偿控制研究[D].重庆:重庆大学,2015Li S J.Nonlinear friction compensation control of gear drive servo system[D].Chongqing:Chongqing University,2015(in Chinese)
[10]朱坚民,费家人,黄春燕.工业机器人空间位置精度预测模型研究[J].制造业自动化,2016,38(3):47-52Zhu J M,Fei J R,Huang C Y.Study on the prediction model of spatial location precision for industrial robots[J].Manufacturing Automation,2016,38(3):47-52(in Chinese)
[11]Calanca A,Capisani L M,Ferrara A,et al.MIMOclosed loop identification of an industrial robot[J].IEEETransactions on Control Systems Technology,2011,19(5):1214-1224
[12]Plooij M,Wolfslag W,Wisse M.Robust feedforward control of robotic arms with friction model uncertainty[J].Robotics and Autonomous Systems,2015,70:83-91
[13]赵登步,白瑞林,沈程慧,等.SCARA机器人点对点运动轨迹规划方法[J].计算机工程,2015,41(8):306-312Zhao D B,Bai R L,Shen C H,et al.Trajectory planning method of point-to-point motion for SCARArobot[J].Computer Engineering,2015,41(8):306-312(in Chinese)
[14]Macfarlane S,Croft E A.Design of jerk bounded trajectories for online industrial robot applications[C]//Proceedings of 2011 IEEE International Conference on Robotics and Automation.Seoul,South Korea:IEEE,2001:979-984
[15]Ata A A,Zawawi A E,Razek M A E.Jerk bounded trajectory planning for non-holonomic mobile manipulator[C]//Proceedings of the 27th European Conference on Modelling and Simulation.2013:733-738
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |