Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2005 | 15 | 3 | 369-381
Tytuł artykułu

Stabilising solutions to a class of nonlinear optimal state tracking problems using radial basis function networks

Treść / Zawartość
Warianty tytułu
Języki publikacji
A controller architecture for nonlinear systems described by Gaussian RBF neural networks is proposed. The controller is a stabilising solution to a class of nonlinear optimal state tracking problems and consists of a combination of a state feedback stabilising regulator and a feedforward neuro-controller. The state feedback stabilising regulator is computed on-line by transforming the tracking problem into a more manageable regulation one, which is solved within the framework of a nonlinear predictive control strategy with guaranteed stability. The feedforward neuro-controller has been designed using the concept of inverse mapping. The proposed control scheme is demonstrated on a simulated single-link robotic manipulator.
Opis fizyczny
  • Department of Electrical Engineering, University of Skikda, Al-Hadaik Rd., P.B.: 26, Skikda 21000, Algeria
  • Department of Electronics, Mentouri University, Zerzara, Ain-Bey Rd., Constantine 25000, Algeria
  • Department of Cybernetics, University of Reading, Whiteknights, Reading RG6 6AY, U.K.
  • Becerra V.M., Roberts P.D. and Griffiths G.W. (1998): Novel developments in process optimisation using predictive control. - J. Process Contr., Vol. 8, No. 2, pp. 117-138.
  • Becerra V.M., Abu-el-zeet Z.H. and Roberts P.D. (1999): Integrating predictive control and economic optimisation. - Comput. Contr. Eng. J., Vol. 10, No. 5, pp. 198-208.
  • Chen C.C. and Shaw L. (1982): On receding horizon feedback control. - Automatica, Vol. 18, No. 3, pp. 349-352.
  • Chen H. and Allgower F. (1998): A quasi-infinite horizon nonlinear model predictive control scheme with guaranteed stability. - Automatica, Vol. 34, No. 10, pp. 1205-1217.
  • De Nicolao G., Magni L. and Scattolini R. (1997): Stabilizing receding-horizon control of nonlinear time-varying systems. - IEEE Trans.Automat. Contr., Vol. 43, No. 7, pp. 1030-1036.
  • Eaton J.W. and Rawlings J.B. (1992): Model predictive control of chemical processes. - Chem. Eng. Sci., Vol. 47, No. 4, pp. 705-720.
  • Garces F., Becerra V.M., Kambhampati C. and Warwick K. (2003): Strategies for Feedback Linearisation: A Dynamic Neural Network Approach. - London: Springer.
  • Garcia C.E., Prett D.M. and Morari M. (1989): Model predictive control: Theory and practice - A survey. - Automatica, Vol. 25, No. 3, pp. 335-347.
  • Hornik K., Stinchcombe M. and White H. (1989): Multilayer feedforward networks are universal approximators. - Neural Networks, Vol. 2,No. 5, pp. 359-366.
  • Hunt K.J., Sbarbaro D., Zbikowski R. and Gawthrop P.J. (1992): Neural networks for control systems: A survey. - Automatica, Vol. 28, No. 6,pp. 1083-1112.
  • Kadirkamanathan V. and Niranjan M. (1993): A function estimation approach to sequential learning with neural networks. - Neural Comput., Vol. 5,No. 6, pp. 954-975.
  • Kambhampati C., Delgado A., Mason J.D. and Warwick K. (1997): Stable receding horizon control based on recurrent networks. - IEE Proc. Contr. Theory Applic., Vol. 144, No. 3, pp. 249-254.
  • Keerthi S.S. and Gilbert E.G. (1988): Optimal, infinite-horizon feedback laws for a general class of constrained discrete-time systems. - J. Optim. Theory Applic., Vol. 57, No. 2, pp. 265-293.
  • Kwakernaak H. and Sivan R. (1972): Linear Optimal Control Systems.- New York: Wiley.
  • Magni L., De Nicolao G., Magnani L. and Scattolini R. (2001): A stabilizing model-based predictive control algorithm for nonlinear systems. - Automatica, Vol. 37, No. 9, pp. 1351-1362.
  • Mayne D.Q. and Michalska H. (1990): Receding horizon control of nonlinear systems. - IEEE Trans. Automat. Contr., Vol. 35, No. 7, pp. 814-824.
  • 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. and Lee J.H. (1999): Model predictive control: Past, present and future. - Comput. Chem. Eng., Vol. 23, No. 4, pp. 667-682.
  • Narendra K.S. and Parthasarathy K. (1990): Identification and control of dynamical using neural networks. - IEEE Trans. Neural Netw., Vol. 1, No. 1, pp. 4-27.
  • Parisini T. and Zoppoli R. (1995): A receding-horizon regulator for nonlinear systems and a neural approximation. - Automatica, Vol. 31, No. 10, pp. 1443-1451.
  • Parisini T., Sanguinetti M. and Zoppoli R. (1998): Nonlinear stabilization by receding-horizon neural regulators. - Int. J. Contr., Vol. 70,No. 3, pp. 341-362.
  • Park Y.M., Choi M.S. and Lee K.W. (1996): An optimal tracking neuro-controller for nonlinear dynamic systems. - IEEE Trans. Neural Netw., Vol. 7, No. 5, pp. 1099-1110.
  • Richalet J. (1993): Industrial applications of model based predictive control. - Automatica, Vol. 29, No. 5, pp. 1251-1274.
  • Richalet J., Rault A., Testud J.L. and Papon J. (1978): Model predictive heuristic control: Application to industrial processes. - Automatica, Vol. 14,No. 2, pp. 413-428.
  • Yingwei L., Sundararajan N. and Saratchandran P. (1997): Identification of time-varying nonlinear systems using minimal radial basis function neural networks. - IEE Proc. Contr. Theory Appl., Vol. 144, No. 2, pp. 202-208.
  • Zhihong M., Wu H.R. and Palaniswami M. (1998): An adaptive tracking controller using neural networks for a class of nonlinear systems. - IEEE Trans. Neural Netw., Vol. 9, No. 5, pp. 947-954.
Typ dokumentu
Identyfikator YADDA
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.