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2005 | 15 | 4 | 527-540

Tytuł artykułu

Finite horizon nonlinear predictive control by the Taylor approximation application to robot tracking trajectory

Treść / Zawartość

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
In industrial control systems, practical interest is driven by the fact that today's processes need to be operated under tighter performance specifications. Often these demands can only be met when process nonlinearities are explicitly considered in the controller. Nonlinear predictive control, the extension of well-established linear predictive control to nonlinear systems, appears to be a well-suited approach for this kind of problems. In this paper, an optimal nonlinear predictive control structure, which provides asymptotic tracking of smooth reference trajectories, is presented. The controller is based on a finite-horizon continuous time minimization of nonlinear predicted tracking errors. A key feature of the control law is that its implementation does not need to perform on-line optimization, and asymptotic tracking of smooth reference signal is guaranteed. An integral action is used to increase the robustness of the closed-loop system with respect to uncertainties and parameters variations. The proposed control scheme is first applied to planning motions problem of a mobile robot and, afterwards, to the trajectory tracking problem of a rigid link manipulator. Simulation results are performed to validate the tracking performance of the proposed controller.

Rocznik

Tom

15

Numer

4

Strony

527-540

Opis fizyczny

Daty

wydano
2005
otrzymano
2004-08-24
poprawiono
2005-03-08
(nieznana)
2005-09-08

Twórcy

  • USTHB University, College of Electronics and Computer Sciences Department of Control and Instrumentation Al-Alia, Bab-Ezzeour, Algeria
  • USTHB University, College of Electronics and Computer Sciences Department of Control and Instrumentation Al-Alia, Bab-Ezzeour, Algeria
  • Supélec, Service Automatique, Plateau de Moulon, Gif-Sur-Yvette, Paris, Cedex 91192, France, France
autor
  • Supélec, Service Automatique, Plateau de Moulon, Gif-Sur-Yvette, Paris, Cedex 91192, France, France

Bibliografia

  • Boucher P. and Dumur D. (1996): La commande predictive. - Paris: Technip.
  • Canudas De Wit C. and Fixot N. (1992): Adaptive control of robot manipulators via velocity estimated feedback.- IEEE Trans. Automat. Contr., Vol. 37, No. 8, pp. 1234-1237.
  • Chen W.H., Balance D.J. and Gawthrop P.J. (2003): Optimal control of nonlinear systems: A predictive control approach. - Automatica, Vol. 39, No. 4, pp. 633-641.
  • Chun Y.S. and Stepanenko Y. (1996): On the robust control of robot manipulators including actuator dynamics. - J. Robot. Sys., Vol. 13, No. 1, pp. 1-10.
  • Clarke D.W, Mohtadi C. and Tuffs P.S. (1987a): Generalized predictive control, Part I: The basic algorithm. - Automatica, Vol. 23, No. 2, pp. 137-148.
  • Clarke D.W, Mohtadi C. and Tuffs P.S. (1987b): Generalized predictive control, Part II. Extension and interpretations. - Automatica, Vol. 23, No. 2, pp. 149-160.
  • Demircioglu H. and Gawthrop P.J. (1991): Continuous-time generalized predictive control (GPC). - Automatica, Vol. 27, No. 1, pp. 55-74.
  • Gauthier J.P., Hammouri H. and Othman S. (1992): A simple observer for nonlinear systems: Application to bioreactor. - IEEE Trans. Automat. Contr., Vol. 37, No. 6, pp. 875-880.
  • Henson M.A. and Seborg D.E. (1997): Nonlinear Process Control. - Englewood Cliffs, NJ: Prentice Hall.
  • Henson M.A. (1998): Nonlinear model predictive control: Current status and future directions. - Comput. Chemi. Eng., Vol. 23, No. 2, pp. 187-202.
  • Khalil H.K. (1992): Nonlinear Systems. - New York: Macmillan.
  • Kim M.S., Shin J.H., Hong S.G. and Lee J.J. (2003): Designing a robust adaptive dynamic controller for nonholonomic mobile robots under modelling uncertainty and disturbances. - Mechatron. J., Vol. 13, No. 5, pp. 507-519.
  • Lee K.W. and Khalil H.K. (1997): Adaptive output feedback control of robot manipulators using high gain observer. - Int. J. Contr., Vol. 67, No. 6, pp. 869-886.
  • Mayne D.Q., Rawlings J.B., Rao C.V. and Scokaert P.O.M. (2000): Constrained model predictive control: stability and optimality. - Automatica, Vol. 36, No. 6, pp. 789-814.
  • Michalska H. and Mayne D.Q. (1993): Robust receding horizon control of constrained nonlinear systems. - IEEE Trans. Automat. Contr., Vol. 38, No. 11, pp. 1623-1633.
  • Morari M., Lee J.H. (1999): Model predictive control: past, present and future. - Comput. Chem. Eng., Vol. 23, No. 4-5, pp. 667-682.
  • Ping L. (1995): Optimal predictive control of continuous nonlinear systems. - Int. J. Contr., Vol. 62, No. 2, pp. 633-649.
  • Singh S.N., Steinberg M. and DiGirolamo R.D. (1995): Nonlinear predictive control of feedback linearizable systems and flight control system design. - J. Guid. Contr. Dynam., Vol. 18, No. 5, pp. 1023-1028.
  • Souroukh M. and Kravaris C. (1996): A continuous-time formulation of nonlinear model predictive control. - Int. J. Contr., Vol. 63, No. 1, pp. 121-146.
  • Spong M.W. and Vidyasagar M. (1989): Robot Dynamics and Control. - New York: Wiley.
  • Spong M.W. (1992): Robust control of robot manipulators. - IEEE Trans. Automat. Contr., Vol. 37, No. 11, pp. 1782-1786.

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Bibliografia

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