基座弹性的双柔杆空间机器人的神经网络动态面控制
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摘要
针对基座弹性的双柔杆漂浮基空间机器人系统存在外部干扰时的轨迹跟踪及柔性抑振问题,推导了系统的动力学模型,应用奇异摄动理论,将系统分解为表示刚性运动的慢变子系统和表示基座弹性、双柔杆振动的快变子系统。对于慢变子系统,设计了一种基于动态面的神经网络控制器,通过动态面的应用避免反步法带来的计算膨胀问题;通过RBF神经网络逼近了含有外部干扰在内的动力学不确定项;针对快变子系统,采用线性二次型最优控制同时抑制弹性基座与双柔杆的振动。数值仿真验证了控制方法的有效性。
The trajectory tracking and vibration suppression of a two-flexible-link and elastic base freefloating space robot system with the external disturbance were discussed. The dynamic model of the system was derived,and then a slow-subsystem describing the rigid motion and a fast-subsystem corresponding to vibration of elastic base and two flexible links were obtained using singular perturbation theory. For the slow-subsystem,a neural network controller based on dynamic surface was designed. Dynamic surface control scheme was adopted to avoid calculation expansion caused by back stepping method and to simplify calculation. The RBF neural network was applied to approximate uncertainties terms of dynamic equation including the external disturbance. For the fast-subsystem,an optimal linear quadratic regulator controller was adopted to damp out the vibration of the two flexible links and the elastic base. The numerical simulations demonstrated the effectiveness of the control method.
The trajectory tracking and vibration suppression of a two-flexible-link and elastic base freefloating space robot system with the external disturbance were discussed. The dynamic model of the system was derived,and then a slow-subsystem describing the rigid motion and a fast-subsystem corresponding to vibration of elastic base and two flexible links were obtained using singular perturbation theory. For the slow-subsystem,a neural network controller based on dynamic surface was designed. Dynamic surface control scheme was adopted to avoid calculation expansion caused by back stepping method and to simplify calculation. The RBF neural network was applied to approximate uncertainties terms of dynamic equation including the external disturbance. For the fast-subsystem,an optimal linear quadratic regulator controller was adopted to damp out the vibration of the two flexible links and the elastic base. The numerical simulations demonstrated the effectiveness of the control method.
引文
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[2]程靖,陈力,不确定空间机器人自适应模糊H∞控制[J].载人航天,2015,21(6):564-567,588.Cheng Jing,Chen Li. Adaptive fuzzy H-infinity control of un-certain space robot system[J]. Manned Spaceflight,2015,21(6):564-567,588.(in Chinese)
[3] Evans L. Canadian space robotics on board the internationalspace[C]//2005 CCToMM Symposium on Mechanism,Ma-chines,and Mechatronics. Canadian Space Agency,Montre-al,Canada,2005:26-27.
[4] Yang B J,Calise A J,Craig J I. Adaptive output feedbackcontrol of a flexible base manipulator[J]. Journal of Guid-ance,Control and Dynamics,2007,30(4):1068-1080.
[5] YU Xiaoyan,CHEN Li. Singular perturbation adaptive controland vibration suppression of free-flying flexible space manipu-lators[J]. Proceedings of the Institution of Mechanical Engi-neers,Part C:Journal of Mechanical Engineering Science,2015,229(11):1989-1997.
[6]梁捷,陈力.基座弹性影响下空间站柔性关节机械臂的鲁棒自适应滑模控制及双重弹性振动主动抑制[J].载人航天,2016,22(6):788-796.LIANG Jie,CHEN Li. Robust adaptive sliding mode controland dual vibration suppression in flexible joint manipulator ofspace station with elastic foundation[J]. Manned Spaceflight,2016,22(6):788-796.(in Chinese)
[7] Park S H,Han S I. Robust-tracking control for robot manipu-lator with dead-zone and friction using backstepping andRFNN controller[J]. IET Control Theory and Applications.2011,5(12):1397-1417.
[8] Swaroop D,Hedrick J K,Yip P P,et al. Dynamic surfacecontrol for a class of nonlinear systems[J]. IEEE Transactionson Automatic Control,2000,45(10):1893-1899.
[9] Cheong J Y,Han S I,Lee J M. Adaptive fuzzy dynamic sur-face sliding mode position control for a robot manipulator withfriction and dead zone[J]. Mathematical Problems in Engi-neering,2013,2013:1-15.
[10]倪振华.振动力学[M].西安:西安交通大学出版社,1988:373-375.NI Zhenhua. Vibration Mechanics[M]. Xi'an:Xi'an JiaoTong University Press,1988:373-375.(in Chinese)
[11] Chen W S. Adaptive backstepping dynamic surface control forsystems with periodic disturbances using neural networks[J].IET Control Theory and Applications,2009,3(40):1383-1394.