Sadhana

, Volume 25, Issue 2, pp 169–180 | Cite as

A hyperstable neural network for the modelling and control of nonlinear systems

Abstract

A multivariable hyperstable robust adaptive decoupling control algorithm based on a neural network is presented for the control of nonlinear multivariable coupled systems with unknown parameters and structure. The Popov theorem is used in the design of the controller. The modelling errors, coupling action and other uncertainties of the system are identified on-line by a neural network. The identified results are taken as compensation signals such that the robust adaptive control of nonlinear systems is realised. Simulation results are given.

Keywords

Computer control neural networks nonlinear systems adaptive control 

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References

  1. Behera L, Chaudhury S, Gopal M 1998 Application of self-organizing neural networks in robot tracking control.IEE Proc. Control Theor. Appl. 145: 135–140CrossRefGoogle Scholar
  2. Chen F C, Liu C C 1994 Adaptively controlling nonlinear continuous time systems using multilayer neural networks.IEEE Trans. Autom. Control 39: 1306–1310MATHCrossRefGoogle Scholar
  3. Fortescue T, Kershenbaum L S, Ydstie B E 1981 Implementation of self-tuning regulators with variable forgetting factors.Automatica 17: 831–835CrossRefGoogle Scholar
  4. Garces F, Warwick K, Craddock C 1998 Multiple PID mapping using neural networks in a MIMO generator system.Proc. Int. Conf. Control 98, Swansea, pp 503-508Google Scholar
  5. Guonam F, Rui Y 1989 A microcomputer controller optimal adaptive flight simulator servo system.Acta Autom. Sin. 15: 193–199Google Scholar
  6. Gupta M M, Sinha N K (eds) 1996Intelligent control systems (New York: IEEE Press)MATHGoogle Scholar
  7. Harris C J, Billings S A 1985Self-tuning control: Theory and applications rev. 2nd edn (London: Peter Peregrinus)MATHGoogle Scholar
  8. Hunt K J, Sbarbaro D, Zbikowski R, Gawthrop P J 1992 Neural networks for control systems — A survey.Automatica 28: 1083–1112MATHCrossRefMathSciNetGoogle Scholar
  9. Hunt K J, Irwin G W, Warwick K (eds) 1995Neural network engineering in dynamic control systems (Berlin: Springer-Verlag).Google Scholar
  10. Ioannou PA, Kokotovic P V 1983Adaptive systems with reduced models (Berlin: Springer-Verlag)MATHGoogle Scholar
  11. Ioannou P A, Sun J 1996Robust adaptive control (Englewood Cliffs, NJ: Prentice Hall)MATHGoogle Scholar
  12. Karny M, Warwick K, Kurkova V (eds) 1998Dealing with complexity: a neural network approach (New York: Springer-Verlag)Google Scholar
  13. Mazak J, Damen A A H, Backx A C P M, Weiland S 1996 Nonlinear transition controller using neural networks tested on a polymerization reactor.Proc. 35th IEEE Conference on Decision and Control, Kobe, Japan, pp 1720–1721CrossRefGoogle Scholar
  14. Narendra K S 1991 Adaptive control using neural networks. InNeural networks for control (eds) W T Miller, R S Sutton, P W Werbos (Cambridge, MA: MIT Press) pp 115–142Google Scholar
  15. Peterson B B, Narendra K S 1982 Bounded adaptive control.IEEE Trans. Autom. Control AC-27: 1161–1168CrossRefGoogle Scholar
  16. Praly L 1983 Robustness of model reference adaptive control.Proc. 3rd Yale Workshop on the Applications of Adaptive Systems Theory, Yale University, New Haven, CTGoogle Scholar
  17. Rohrs C E, Valavani L, Athans M, Stein G 1982 Robustness of adaptive control algorithms in the presence of unmodelled dynamics.Proc. 21st IEEE Conf. Decision & Control, Orlando, Florida, pp3-llGoogle Scholar
  18. Tomizuka M, Hu J-S, Chin T-C 1992 Synchronization of two motion control axes under adaptive feedforward control.Trans. ASME 114: 196–203MATHCrossRefGoogle Scholar
  19. Zhu Q M, Ma Z, Warwick K 1999 A neural network enhanced generalized minimum variance self-tuning controller for nonlinear discrete-time systems.IEE Proc. Control Theor. Appl. 146: 319–326CrossRefGoogle Scholar

Copyright information

© Printed in India 2000

Authors and Affiliations

  1. 1.Department of CyberneticsUniversity of ReadingWhiteknightsUK
  2. 2.Department of Mechanical and Electrical EngineeringAston UniversityBirminghamUK
  3. 3.Center for Engineering Research Technikon NatalDurbanSouth Africa

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