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Motion control of mobile under-actuated manipulators by implicit function using support vector machines

Motion control of mobile under-actuated manipulators by implicit function using support vector machines

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In this study, the authors propose implicit control using least square support vector machines (LS-SVMs) approximation for the motion control of wheeled under-actuated manipulators. For approximating the multi-input and multi-output non-linear system, an LS-SVM matrix operator is proposed. Further, by using implicit function with SVMs, a control is constructed to obtain motion tracking of wheeled under-actuated manipulators. The relative degree of the regulated output is assumed to be known enabling the system feedback linearisable. It is shown that the tracking error can be controlled in a small neighbourhood of zero through Lyapunov's direct method. The methodology is applicable to minimum phase observable and stabilisable systems of unknown but finite dimension, as long as the relative degree is known. The effectiveness of the proposed control method is substantiated by the simulation results.

References

    1. 1)
      • S. Lin , A.A. Goldenberg . Neural-network control of mobile manipulators. IEEE Trans. Neural Netw. , 5 , 1121 - 1133
    2. 2)
      • W. Dong . On trajectory and force tracking control of constrained mobile manipulators with parameter uncertainty. Automatica , 1475 - 1484
    3. 3)
      • D. Sun . A synchronization approach to mutual error compensation in controlling the vehicle with an installed manipulator. Int. J. Veh. Des. , 287 - 305
    4. 4)
      • Li, Z., Ming, A., Xi, N., Shimojo, M.: `Motion control of nonholonomic mobile underactuated manipulator', IEEE Int. Conf. on Robotics and Automation, 2006, p. 3512–3519.
    5. 5)
      • F. Grasser , A.D. Arrigo , S. Colombi . JOE: a mobile, inverted pendulum. IEEE Trans. Ind. Electron. , 1 , 107 - 114
    6. 6)
      • Z. Li , J. Luo . Adaptive robust dynamic balance and motion controls of mobile wheeled inverted pendulums. IEEE Trans. Control Syst. Technol. , 1 , 233 - 241
    7. 7)
      • M. Zhang , T. Tarn . Hybrid control of the pendubot. IEEE/ASME Trans. Mechatronics , 1 , 79 - 86
    8. 8)
      • H. Arai , K. Tanie . Nonholonomic control of a three-DOF planar underactuted manipulator. IEEE Trans. Robot. Autom. , 5 , 681 - 694
    9. 9)
      • A. De Luca , G. Oriolo . Trajectory planning and control for planar robots with passive last joint. Int. J. Robot. Res. , 575 - 590
    10. 10)
      • Y. Liu , Y. Xu , M. Bergerman . Cooperation control of multiple manipulators with passive joints. IEEE Trans. Robot. Autom. , 2 , 258 - 267
    11. 11)
      • R. Tinos , M.H. Terra , J.Y. Ishihara . Motion and force control of cooperative robotic manipulators with passive joints. IEEE Trans. Control Syst. Technol. , 4 , 725 - 734
    12. 12)
      • S.S. Ge , C.C. Hang , T. Zhang . A direct adaptive controller for dynamic systems with a class of nonlinear parameterizations. Automatica , 741 - 747
    13. 13)
      • F.L. Lewis , A. Yesildirek , K. Liu . Multilayer neural network robot controller with guaranteed tracking performance. IEEE Trans. Neural Netw. , 2 , 388 - 399
    14. 14)
      • Z. Li , C. Yang , J. Gu . Neuro-adaptive compliant force/motion control for uncertain constrained wheeled mobile manipulator. Int. J. Robot. Autom. , 3 , 206 - 214
    15. 15)
      • G.L. Wang , Y.F. Li , D.X. Bi . Support vector machine networks for friction modeling. IEEE/ASME Trans. Mechatronics , 3 , 601 - 606
    16. 16)
      • H.R. Zhang , X.D. Wang , C.J. Zhang , X.S. Cai . Robust identification of non-linear dynamic systems using support vector machine. IEE Proc. Sci. Meas. Technol. , 3 , 125 - 129
    17. 17)
      • Lu, G., Song, J., Hua, L., Sun, C.: `Inverse system control of nonlinear systems using LS-SVM', Proc. 26th Chinese Control Conf., 2007, China, p. 233–236.
    18. 18)
      • Xu, J., Chen, S.: `Adaptive control of a class of nonlinear discrete-time systems using support vector machine', Proc. Fifth World Congress on Intelligent Control and Automation, 2004, China, p. 440–443.
    19. 19)
      • D. Bi , Y.F. Li , S.K. Tso , G.L. Wang . Friction modeling and compensation for Haptic display based on support vector machine. IEEE Trans. Ind. Electron. , 2 , 491 - 500
    20. 20)
      • W. Zhang , S.S. Ge . A global implicit function theorem without initial point and its applications to control of non-affine systems of high dimensions. J. Math. Anal. Appl. , 251 - 261
    21. 21)
      • M.W. Hirsch , S. Smale . (1974) Differential equations, dynamical systems, and linear algebra.
    22. 22)
      • J. Wang , Q. Chen , Y. Chen . RBF kernel based support vector machine with universal approximation and its application, Lecture Notes in Computer Science, Part III Support Vector Machines.
    23. 23)
      • V.N. Vapnik . (1998) Statistical learning theory.
    24. 24)
      • S.S. Ge , C.C. Hang , T.H. Lee , T. Zhang . (2001) Stable adaptive neural network control.
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