Similar articles

Abstract: The problem of self-tuning control with a two-manipulator system holding a rigid object in the presence of inaccurate translational base frame parameters is addressed. An adaptive robust neural controller is proposed to cope with inaccurate translational base frame parameters, internal force, modeling uncertainties, joint friction, and external disturbances. A radial basis function neural network is adopted for all kinds of dynamical estimation, including undesired internal force. To validate the effectiveness of the proposed approach, together with simulation studies and analysis, the position tracking errors are shown to asymptotically converge to zero, and the internal force can be maintained in a steady range. Using an adaptive engine, this approach permits accurate online calibration of the relative translational base frame parameters of the involved manipulators. Specialized robust compensation is established for global stability. Using a Lyapunov approach, the controller is proved robust in the face of inaccurate base frame parameters and the aforementioned uncertainties.

基座参数欠精确环境下双机械臂刚体夹持系统的自适应神经鲁棒控制

[1]Aghili F, 2011. Self-tuning cooperative control of manipulators with position/orientation uncertainties in the closed- kinematic loop. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.4187-4193.

[2]Aghili F, 2013. Adaptive control of manipulators forming closed kinematic chain with inaccurate kinematic model. IEEE/ASME Trans Mechatron, 18(5):1544-1554.

[3]Cheah CC, Liu C, Slotine J, 2004. Approximate Jacobian adaptive control for robot manipulators. IEEE Int Conf on Robtics and Automation, p.3075-3080.

[4]Cheah CC, Liu C, Slotine JJE, 2006. Adaptive Jacobian tracking control of robots with uncertainties in kinematic, dynamic and actuator models. IEEE Trans Autom Contr, 51(6):1024-1029.

[5]Cheng L, Hou ZG, Tan M, 2009. Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model. Automatica, 45(10):2312-2318.

[6]Corke P, 1996. A robotics toolbox for Matlab. IEEE Robot Autom Mag, 3(1):24-32.

[7]Deng H, Wu H, Yang C, et al., 2015. Base frame calibration for multi-robot coordinated systems. IEEE Int Conf on Robotics and Biomimetics, p.1489-1494.

[8]Erhart S, Hirche S, 2013. Adaptive force/velocity control for multi-robot cooperative manipulation under uncertain kinematic parameters. IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.307-314.

[9]Gan Y, Dai X., 2011. Base frame calibration for coordinated industrial robots. Robot Auton Syst, 59(7-8):563-570.

[10]Gueaieb W, Al-Sharhan S, Bolic M, 2007a. Robust computationally efficient control of cooperative closed-chain manipulators with uncertain dynamics. Automatica, 43(5): 842-851.

[11]Gueaieb W, Karray F, Al-Sharhan S, 2007b. A robust hybrid intelligent position/force control scheme for cooperative manipulators. IEEE/ASME Trans Mechatron, 12(2):109- 125.

[12]Lewis F, Jagannathan S, Yesildirak A, 1998. Neural Network Control of Robot Manipulators and Non-linear Systems. CRC Press, France, p.1-468.

[13]Li Z, Xiao S, Ge SS, et al., 2015. Constrained multilegged robot system modeling and fuzzy control with uncertain kinematics and dynamics incorporating foot force optimization. IEEE Trans Syst Man Cybern Syst, 46(1):1-15.

[14]Liu JF, Abdel-Malek K, 2000. Robust control of planar dual- arm cooperative manipulators. Robot Comput-Integr Manuf, 16(2):109-119.

[15]Liu YC, 2015. Distributed synchronization for heterogeneous robots with uncertain kinematics and dynamics under switching topologies. J Franklin Instit, 352(9):3808- 3826.

[16]Liu YC, Khong MH, 2015. Adaptive control for nonlinear teleoperators with uncertain kinematics and dynamics. IEEE/ASME Trans Mechatron, 20(5):2550-2562.

[17]Lizarralde F, Leite AC, Hsu L, et al., 2013. Adaptive visual servoing scheme free of image velocity measurement for uncertain robot manipulators. Automatica, 49(5):1304- 1309.

[18]Mohajerpoor R, Rezaei M, Talebi A, et al., 2011. A robust adaptive hybrid force/position control scheme of two planar manipulators handling an unknown object interacting with an environment. Proc Instit Mech Eng Part I J Syst Contr Eng, 226(4):509-522.

[19]Namvar M, Aghili F, 2005. Adaptive force-motion control of coordinated robots interacting with geometrically unknown environments. IEEE Trans Robot, 21(4):678-694.

[20]Panwar V, Kumar N, Sukavanam N, et al., 2012. Adaptive neural controller for cooperative multiple robot manipulator system manipulating a single rigid object. Appl Soft Comput, 12(1):216-227.

[21]Park IW, Lee BJ, Cho SH, et al., 2012. Laser-based kinematic calibration of robot manipulator using differential kinematics. IEEE/ASME Trans Mechatron, 17(6):1059-1067.

[22]Park J, Sandberg IW, 1991. Universal approximation using radial-basis-function networks. Neur Comput, 3(2):246- 257.

[23]Parra-Vega V, Arimoto S, Liu YH, et al., 2003. Dynamic sliding pid control for tracking of robot manipulators: theory and experiments. IEEE Trans Robot Autom, 19(6):967- 976.

[24]Su CY, Stepanenko Y, 1995. Adaptive sliding mode coordinated control of multiple robot arms attached to a constrained object. IEEE Trans Syst Man Cybern, 25(5):871- 878.

[25]Szewczyk J, Plumet F, Bidaud P, 2002. Planning and controlling cooperating robots through distributed impedance. J Robot Syst, 19(6):283-297.

[26]Tavasoli A, Eghtesad M, Jafarian H, 2009. Two-time scale control and observer design for trajectory tracking of two cooperating robot manipulators moving a flexible beam. Robot Auton Syst, 57(2):212-221.

[27]Zhang YH, Wei W, Dan YU, et al., 2011. A tracking and predicting scheme for ping pong robot. J Zhejiang Univ-Sci C (Comput & Electron), 12(2):110-115.

[28]Zhao D, Li S, Zhu Q, 2014a. Adaptive synchronised tracking control for multiple robotic manipulators with uncertain kinematics and dynamics. Int J Syst Sci, 47(4):1-14.

[29]Zhao D, Ni W, Zhu Q, 2014b. A framework of neural networks based consensus control for multiple robotic manipulators. Neurocomputing, 140:8-18.

[30]Zhao D, Zhu Q, Li N, et al., 2014c. Synchronized control with neuro-agents for leader–follower based multiple robotic manipulators. Neurocomputing, 124:149-161.

[31]Zribi M, Karkoub M, Huang L, 2000. Modelling and control of two robotic manipulators handling a constrained object. Appl Math Model, 24(12):881-898.