Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
권호 표지
DOI QR코드

DOI QR Code

Stable Tracking Control to a Non-linear Process Via Neural Network Model

  • Zhai, Yujia (Department of Electrical and Electronic Engineering Xi'an Jiaotong-Liverpool University)
  • Received : 2014.08.12
  • Accepted : 2014.11.05
  • Published : 2014.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

A stable neural network control scheme for unknown non-linear systems is developed in this paper. While the control variable is optimised to minimize the performance index, convergence of the index is guaranteed asymptotically stable by a Lyapnov control law. The optimization is achieved using a gradient descent searching algorithm and is consequently slow. A fast convergence algorithm using an adaptive learning rate is employed to speed up the convergence. Application of the stable control to a single input single output (SISO) non-linear system is simulated. The satisfactory control performance is obtained.

Keywords

References

  1. HUNT, K.J., SBARBARO, R., ZBIKOWSKI, R. and GAWTHROP, P.J.: 'Neural networks for control systems-a survey', Automatica, pp. 575-587, 1992.
  2. HUNT, K.J. and SBARBARO, D.: 'Neural networks for non-linear internal model control', IEE Proc.D, pp. 431-438, 1991.
  3. GOMM, J.B., EVANS, J.T. and WILLIAMS, D.: 'Development and performance of a neural network predictive controller', Control Engineering Practice, pp. 49-59, 1997.
  4. YU, D.L., GOMM, J.B. and WILLIAMS, D.: 'On-line predictive control of a chemical process using neural network models', Proc. of IFAC 14th World Congress, pp. 121-126, 1999.
  5. LIGHTBODY, G. and IRWIN, G.W.: 'Direct neural model reference adaptive control', IEE Proc.D, pp. 31-43, 1995.
  6. WARWICK, K., IRWIN, G.W. and HUNT, K.J. (Eds.): 'Neutral networks for control and systems' (Peter Peregrinus, Stevenage, UK, 1992.
  7. JAGANNATHAN, J. and LEWIS, F.L.: 'Discrete-time neural net controller for a class of nonlinear dynamical systems', IEEE Trans. Automatic Contro, pp. 1693-1699l, 1996.
  8. FABRI, S. and KADIRKAMANATHAN, V.: 'Dynamic structure neural network for stable adaptive control of non-linear systems', IEEE Trans. Neural Networks, pp. 1151-1167, 1996.
  9. NORIEGA, J.R. and WANG, H.: 'A direct adaptive neural network control for unknown non-linear systems and it's application', IEEE Trans. Neural Networks, pp. 27-34, 1998.
  10. TAN, Y. and CAUWENBERGHE, A.V.: 'Non-linear one-step-ahead control using neural networks: control strategy and stability design', Automatica, pp. 1701-1706, 1996.
  11. BEHERA, L., GOPAL, M. and CHAUDHURY, S.: 'Inversion of RBF networks and application to adaptive control of non-linear systems', IEE Proc. D, pp. 617-624, 1995.
  12. FUNAHASHI, K.: 'On the approximate realization of continuous mappings by neural networks', Neural Networks, pp. 183-192, 1989.
  13. YU, D.L. and YU, D.W.: 'A fast optimization algorithm in neural network based control', Submitted to Electronics Letters, 2000.
  14. LEONARD, J.A. and KRAMER, M.A.: 'Radial basis functions for classifying process faults', IEEE Control Systems Magazine, pp. 31-38, 1991.
  15. YU, D.L., GOMM, J.B. and WILLIAMS, D.: 'A recursive orthogonal least squares algorithm for training RBF networks', Neural Processing Letters, pp.167-176, 1997.
  16. SENBORG, D.E., EDGAR, T.F. and MELLICHAMP, D.A.: 'Process dynamics and control' (John Wiley and Sons Inc., New York, USA, 1989)

Cited by

  1. Real-Time Object Tracking Algorithm based on Pattern Classification in Surveillance Networks vol.14, pp.2, 2016, https://doi.org/10.14400/JDC.2016.14.2.183