柔性关节驱动机构的复合神经动态面控制
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摘要
为实现柔性关节驱动机构的高精度位置控制,首先对其建立了包含Lu Gre摩擦模型、柔性变形和外界扰动力矩等非线性因素的动力学模型.然后针对该模型设计了复合神经动态面控制器,模型的不确定项通过径向基函数(RBF)神经网络(NN)在线逼近和补偿,为提高神经网络对不确定项的逼近速度和逼近精度,结合预测误差和补偿跟踪误差构建了神经网络权值的复合自适应律.通过李亚普诺夫理论证明了系统一致最终稳定有界.与传统动态面控制相比,仿真结果表明复合神经动态面控制器提高了神经网络对不确定项的逼近精度和逼近速度,提高了柔性关节驱动机构的位置跟踪精度.
To realize high-precision position control of the flexible joint driving mechanism,we establish a dynamic model that includes nonlinear factors such as the Lu Gre friction model,the flexible deformation,and the external disturbance torque. On the basis of this dynamics model,we propose a composite neural dynamic surface controller. The uncertainties of the model are approximated and compensated online by using the RBF neural network( NN). To improve the approximation speed and the accuracy of uncertainties,we construct composite adaptive laws for neural weight updating based on prediction and compensated tracking errors. We guarantee the uniformly ultimate boundedness stability via Lyapunov theory. Compared with the classic dynamic surface control method,the proposed control method is fast and achieves better accuracy in uncertainty approximation,as indicated by simulation results,and the position tracking accuracy of the flexible joint driving mechanism is improved.
To realize high-precision position control of the flexible joint driving mechanism,we establish a dynamic model that includes nonlinear factors such as the Lu Gre friction model,the flexible deformation,and the external disturbance torque. On the basis of this dynamics model,we propose a composite neural dynamic surface controller. The uncertainties of the model are approximated and compensated online by using the RBF neural network( NN). To improve the approximation speed and the accuracy of uncertainties,we construct composite adaptive laws for neural weight updating based on prediction and compensated tracking errors. We guarantee the uniformly ultimate boundedness stability via Lyapunov theory. Compared with the classic dynamic surface control method,the proposed control method is fast and achieves better accuracy in uncertainty approximation,as indicated by simulation results,and the position tracking accuracy of the flexible joint driving mechanism is improved.
引文
[1]Freidovich L,Robertsson A,Shiriaev A,et al.Lu Gre model-based friction compensation[J].IEEE Transactions on Control Systems Technology,2010,18(1):194-200.
[2]Hauschild J P,Heppler G R.Control of harmonic drive motor actuated flexible linkages[C]//IEEE International Conference on Robotics and Automation.Piscataway,NJ,USA:IEEE,2007:3451-3456.
[3]于伟,马佳光,李锦英,等.基于Lu Gre模型实现精密伺服转台摩擦参数辨识及补偿[J].光学精密工程,2011,11(19):2736-2743.Yu W,Ma J G,Li J Y,et al.Friction parameter identification and friction compensation for precision servo turning table[J].Optics and Precision Engineering,2011,11(19):2736-2743.
[4]向红标,谭文斌,李醒飞,等.基于Lu Gre模型的自适应摩擦补偿[J].机械工程学报,2012,48(17):70-74.Xiang H B,Tan W B,Li X F,et al.Adaptive friction compensation based on Lu Gre model[J].Journal of Mechanical Engineering,2012,48(17):70-74.
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[6]王雪竹,李洪谊,王越超,等.柔性关节机器人高精度自适应反步法控制[J].信息与控制,2016,45(1):1-7.Wang X Z,LI H Y,Wang Y C,et al.High-precision adaptive backstepping control of flexible joint robots[J].Information and Control,2016,45(1):1-7
[7]陈明金,李树荣,曹乾磊.时滞柔性关节机械臂自适应位置/力控制[J].控制理论与应用,2015,32(2):217-223.Chen M J,Li S R,Cao Q L.Adaptive motion/force control forrigid-link flexible-joint manipulators with time delay[J].Control Theory&Applications,2015,32(2):217-223.
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[9]刘金琨.机器人控制系统的设计与MATLAB仿真[M].北京:清华大学岀版社,2008:345-401.Liu J K.Design of robot control system and MATLAB simulation[M].Beijing:Tsinghua University Press,2008:345-401.
[10]Swaroop D,Hedrick J K,Yip P P,et al.Dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form[J].IEEE Transactions on Automatic Control,2000,45(10):1893-1899.
[11]邓涛,姚宏,杜军,等.一类不确定高阶随机非线性系统的自适应动态面控制[J].信息与控制,2014,43(4):457-462,469.Deng T,Yao H,Du J,et al.Adaptive dynamic surface control for a class of uncertain high-order stochastic nonlinear systems[J].Information and Control,2014,43(4):457-462,469.
[12]王家序,刘彪,李俊阳,等.谐波驱动系统自适应神经网络动态面控制[J].华中科技大学学报:自然科学版,2015,43(10):81-86.Wang J X,Liu B,Li J Y,et al.Adaptive neural-network dynamic surface control of the harmonic drive system[J].Journal of Huazhong University of Science and Technology:Natural Science Edition,2015,43(10):81-86.
[13]罗绍华,王家序,李俊阳,等.柔性滤波驱动机构的自适应动态面模糊控制[J].哈尔滨工程大学学报,2014,35(7):889-894.Luo S H,Wang J X,Li J Y,et al.Adaptive dynamic surface fuzzy control of the flexible filter driving mechanism[J].Journal of Harbin Engineering University,2014,35(7):889-894.
[14]Xu B,Shi Z,Yang C,et al.Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form[J].IEEE Transactions on Cybernetics,2014,44(12):2626-2634.
[15]Liu J,Yu L U.Adaptive RBF neural network control of robot with actuator nonlinearities[J].Journal of Control Theory&Applications,2010,8(2):249-256.
[16]LI G J,Chen S M.Study of a new dynamic model for harmonic drive in precision control system[J].Journal of the University of Electronic Science&Technology of China,2010,39(5):742-741.
[17]乔俊飞,韩红桂.RBF神经网络的结构动态优化设计[J].自动化学报,2010,36(6):865-872.Qiao J F,Han H G.Optimal structure design for RBFNN Structure[J].Acta Automatica Sinica,2010,36(6):865-872.
[18]Wang L X.Design and analysis of fuzzy identifiers of nonlinear dynamic systems[J].IEEE Transactions on Automatic Control,1995,40(1):11-23.
[19]Dong W,Farrell J A,Polycarpou M M,et al.Command filtered adaptive backstepping[J].IEEE Transactions on Control Systems Technology,2012,20(3):566-580.
[20]Wang D,Huang J.Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form[J].IEEE Transactions on Neural Networks,2005,16(1):195-203.
[2]Hauschild J P,Heppler G R.Control of harmonic drive motor actuated flexible linkages[C]//IEEE International Conference on Robotics and Automation.Piscataway,NJ,USA:IEEE,2007:3451-3456.
[3]于伟,马佳光,李锦英,等.基于Lu Gre模型实现精密伺服转台摩擦参数辨识及补偿[J].光学精密工程,2011,11(19):2736-2743.Yu W,Ma J G,Li J Y,et al.Friction parameter identification and friction compensation for precision servo turning table[J].Optics and Precision Engineering,2011,11(19):2736-2743.
[4]向红标,谭文斌,李醒飞,等.基于Lu Gre模型的自适应摩擦补偿[J].机械工程学报,2012,48(17):70-74.Xiang H B,Tan W B,Li X F,et al.Adaptive friction compensation based on Lu Gre model[J].Journal of Mechanical Engineering,2012,48(17):70-74.
[5]张文静,台宪青.基于Lu Gre模型的火炮伺服系统摩擦力矩自适应补偿[J].清华大学学报:自然科学版,2007,47(z2):1756-1760.Zhang W J,Tai X Q.Adaptive friction compensation in gun servo systems based on the Lu Gre model[J].Journal of Tsinghua University:Science and Technology,2007,47(z2):1756-1760.
[6]王雪竹,李洪谊,王越超,等.柔性关节机器人高精度自适应反步法控制[J].信息与控制,2016,45(1):1-7.Wang X Z,LI H Y,Wang Y C,et al.High-precision adaptive backstepping control of flexible joint robots[J].Information and Control,2016,45(1):1-7
[7]陈明金,李树荣,曹乾磊.时滞柔性关节机械臂自适应位置/力控制[J].控制理论与应用,2015,32(2):217-223.Chen M J,Li S R,Cao Q L.Adaptive motion/force control forrigid-link flexible-joint manipulators with time delay[J].Control Theory&Applications,2015,32(2):217-223.
[8]Zouari L,Abid H,Abid M.Flexible joint manipulator based on backstepping controller[C]//International Conference on Sciences and Techniques of Automatic Control and Computer Engineering.Piscataway,NJ,USA:IEEE,2014:1597-1610.
[9]刘金琨.机器人控制系统的设计与MATLAB仿真[M].北京:清华大学岀版社,2008:345-401.Liu J K.Design of robot control system and MATLAB simulation[M].Beijing:Tsinghua University Press,2008:345-401.
[10]Swaroop D,Hedrick J K,Yip P P,et al.Dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form[J].IEEE Transactions on Automatic Control,2000,45(10):1893-1899.
[11]邓涛,姚宏,杜军,等.一类不确定高阶随机非线性系统的自适应动态面控制[J].信息与控制,2014,43(4):457-462,469.Deng T,Yao H,Du J,et al.Adaptive dynamic surface control for a class of uncertain high-order stochastic nonlinear systems[J].Information and Control,2014,43(4):457-462,469.
[12]王家序,刘彪,李俊阳,等.谐波驱动系统自适应神经网络动态面控制[J].华中科技大学学报:自然科学版,2015,43(10):81-86.Wang J X,Liu B,Li J Y,et al.Adaptive neural-network dynamic surface control of the harmonic drive system[J].Journal of Huazhong University of Science and Technology:Natural Science Edition,2015,43(10):81-86.
[13]罗绍华,王家序,李俊阳,等.柔性滤波驱动机构的自适应动态面模糊控制[J].哈尔滨工程大学学报,2014,35(7):889-894.Luo S H,Wang J X,Li J Y,et al.Adaptive dynamic surface fuzzy control of the flexible filter driving mechanism[J].Journal of Harbin Engineering University,2014,35(7):889-894.
[14]Xu B,Shi Z,Yang C,et al.Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form[J].IEEE Transactions on Cybernetics,2014,44(12):2626-2634.
[15]Liu J,Yu L U.Adaptive RBF neural network control of robot with actuator nonlinearities[J].Journal of Control Theory&Applications,2010,8(2):249-256.
[16]LI G J,Chen S M.Study of a new dynamic model for harmonic drive in precision control system[J].Journal of the University of Electronic Science&Technology of China,2010,39(5):742-741.
[17]乔俊飞,韩红桂.RBF神经网络的结构动态优化设计[J].自动化学报,2010,36(6):865-872.Qiao J F,Han H G.Optimal structure design for RBFNN Structure[J].Acta Automatica Sinica,2010,36(6):865-872.
[18]Wang L X.Design and analysis of fuzzy identifiers of nonlinear dynamic systems[J].IEEE Transactions on Automatic Control,1995,40(1):11-23.
[19]Dong W,Farrell J A,Polycarpou M M,et al.Command filtered adaptive backstepping[J].IEEE Transactions on Control Systems Technology,2012,20(3):566-580.
[20]Wang D,Huang J.Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form[J].IEEE Transactions on Neural Networks,2005,16(1):195-203.