CD播放器机械臂轨迹跟踪的鲁棒自适应迭代学习控制
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摘要
针对CD播放器机械臂轨迹跟踪问题,提出一种鲁棒自适应迭代学习控制算法。分析了CD播放器系统的基本结构,根据空间运动特性建立了机械臂系统模型,提供了机械臂轨迹跟踪控制方案,并结合机械臂系统的结构特点设计了鲁棒自适应控制算法。该算法由可变增益比例—微分控制、前馈学习控制和鲁棒控制组成,可变增益比例—微分控制根据自适应切换规则调整控制增益,保证系统稳定性并加快收敛速度,前馈学习控制通过学习规则计算出每次迭代过程中期望的前馈力矩,以补偿模型不确定性,鲁棒控制确保了系统在外部随机扰动下的鲁棒性。通过实验表明,所提控制算法能加快系统的收敛速度,提高系统的跟踪性能。
For the trajectory tracking of CD player arm,a new robust adaptive iterative learning control algorithm was presented.The CD player system structure was analyzed,the system model was established according to the characteristics of motion of mechanical arm,the trajectory tracking control scheme of mechanical arm was provided,and the robust adaptive iterative learning control algorithm was designed based on the structural characteristics of mechanical arm.The algorithm consisted of a variable gain PD controller,a feedforward learning controller and a robust control term.The variable gain PD control flexibly adjusted the control gain to guarantee the stability of the system status according to the gain switching technique,the feedforward learning control calculated the desired feedforward torque at each iterative step by a learning rule to compensate the model uncertainty,the robust control ensured the robustness of the system under external random disturbances.Experiments showed that the proposed control algorithm could accelerate the convergence and improve the tracking performance.
For the trajectory tracking of CD player arm,a new robust adaptive iterative learning control algorithm was presented.The CD player system structure was analyzed,the system model was established according to the characteristics of motion of mechanical arm,the trajectory tracking control scheme of mechanical arm was provided,and the robust adaptive iterative learning control algorithm was designed based on the structural characteristics of mechanical arm.The algorithm consisted of a variable gain PD controller,a feedforward learning controller and a robust control term.The variable gain PD control flexibly adjusted the control gain to guarantee the stability of the system status according to the gain switching technique,the feedforward learning control calculated the desired feedforward torque at each iterative step by a learning rule to compensate the model uncertainty,the robust control ensured the robustness of the system under external random disturbances.Experiments showed that the proposed control algorithm could accelerate the convergence and improve the tracking performance.
引文
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[14]OUYANG P R,ZHANG W J,GUPTA M M.An adaptive switching learning control method for trajectory tracking of robot manipulators[J].Mechatronics,2006,16(1):51-61.
[15]TAYEBI A.Adaptive iterative learning control for robot manipulator[J].Automatica,2004,40(7):1195-1203.
[16]ZHANG L,CHEN W,LIU J,et al.A robust adaptive iterative learning control for trajectory tracking of permanentmagnet spherical actuator[J].IEEE Transactions on Industrial Electronics,2016,63(1):291-301.
[2]ALVAREZ-RAMIREZ J,KELLY R,CERVANTES I.Semiglobal stability of saturated linear PID control for robot manipulators[J].Automatica,2003,39(6):989-995.
[3]TOMEI P.Adaptive PD controller for robot manipulators[J].IEEE Transactions on Robotics&Automation,2002,7(4):565-570.
[4]LIU Fucai,LIANG Lihuan,GAO Juanjuan,et al.Trajectory tracking control with active disturbance rejection forspace manipulator in different gravity environments[J].Control Theory&Applications,2014,31(3):352-360(in Chinese).[刘福才,梁利环,高娟娟,等.不同重力环境的空间机械臂自抗扰轨迹跟踪控制[J].控制理论与应用,2014,31(3):352-360.]
[5]LIU Fucai,LIANG Lihuan,QIN Li,et al.Sliding mode control for space manipulator trajectory trackingconsidering effects of gravity[J].Control and Decision,2014,29(10):1856-1860(in Chinese).[刘福才,梁利环,秦利,等.考虑重力影响的空间机械臂轨迹跟踪滑模控制[J].控制与决策,2014,29(10):1856-1860.]
[6]CHEN Youdong,GUO Jiaxin,LIU Jialei,et al.Adaptive strategy of collaborative robot based on double Gussian process[J].Computer Integrated Manufacturing Systems,2017,23(9):1891-1898(in Chinese).[陈友东,郭佳鑫,刘嘉蕾,等.基于双高斯过程的协作机器人自适应策略[J].计算机集成制造系统,2017,23(9):1891-1898.]
[7]LIANG Jie,CHEN Li,LIANG Pin.Neural network adaptive control and vibration hierarchical fuzzy control of flexible arm space robot[J].Computer Integrated Manufacturing Systems,2012,18(9):1930-1937(in Chinese).[梁捷,陈力,梁频.柔性臂空间机器人的神经网络自适应控制及振动模态分级模糊控制[J].计算机集成制造系统,2012,18(9):1930-1937.]
[8]HU Lingmei,CHEN Yang,CHEN Junfeng,et al.Compound control strategy for manipulator based on adaptive fuzzy compensition[J].Information and Control,2016,45(3):342-347(in Chinese).[胡灵美,陈杨,陈俊风,等.基于自适应模糊补偿的机械臂复合控制策略[J].信息与控制,2016,45(3):342-347.]
[9]LUO Tianhong,CHEN Cai,LI Fuying.Inverse solution of redundantmechanical arm based on giowworm swarm optimization algorithm of time-varying[J].Computer Integrated Manufacturing Systems,2016,22(2):576-582(in Chinese).[罗天洪,陈才,李富盈.基于时变萤火虫群算法的冗余机器人手臂逆解[J].计算机集成制造系统,2016,22(2):576-582.]
[10]ZHENG Kunming,ZHANG Qiuju.Fuzzy PID control of delta robot based on elastic dynamic model and genetic algorithm[J].Computer Integrated Manufacturing Systems.2016,22(7):1707-1716(in Chinese).[郑坤明,张秋菊.基于弹性动力学模型与遗传算法的Delta机器人模糊PID控制[J].计算机集成制造系统,2016,22(7):1707-1716.]
[11]ZHENG Qingshan,HOU Baolin.Robust control of robotic manipulator with two degrees of freedom based on oscillatory base[J].Machine Building and Automation,2013,42(3):155-157(in Chinese).[郑青山,侯保林.基础振动下两自由度机械臂的鲁棒控制[J].电气与自动化,2013,42(3):155-157.]
[12]TONG G F,LIN M X.Alignment condition-based robust adaptive iterative learning control of uncertain robot system[J].Advances in Mechanical Engineering,2014(2):1-8.
[13]KUC T Y,NAM K,LEE J S.An iterative learning control of robot manipulators[J].IEEE Transactions on Robotics and Automation,1991,1(6):835-842.
[14]OUYANG P R,ZHANG W J,GUPTA M M.An adaptive switching learning control method for trajectory tracking of robot manipulators[J].Mechatronics,2006,16(1):51-61.
[15]TAYEBI A.Adaptive iterative learning control for robot manipulator[J].Automatica,2004,40(7):1195-1203.
[16]ZHANG L,CHEN W,LIU J,et al.A robust adaptive iterative learning control for trajectory tracking of permanentmagnet spherical actuator[J].IEEE Transactions on Industrial Electronics,2016,63(1):291-301.