Abstract
In this paper we propose an indirect adaptive control scheme using Hopfield-based dynamic neural network for SISO nonlinear systems with external disturbances. Hopfield-based dynamic neural networks are used to obtain uncertain function estimations in an indirect adaptive controller, and a compensation controller is used to suppress the effect of approximation error and disturbance. The weights of Hopfield-based dynamic neural network are on-line tuned by the adaptive laws derived in the sense of Lyapunov, so that the stability of the closed-loop system can be guaranteed. In addition, the tracking error can be attenuated to a desired level by selecting some parameters adequately. Simulation results illustrate the applicability of the proposed control scheme. The designed parsimonious structure of the Hopfield-based dynamic neural network makes the practical implementation of the work in this paper much easier.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Chen, B.S., Lee, C.H., Chang, Y.C.: H ∞ Tracking Design of Uncertain Nonlinear SISO Systems: Adaptive Fuzzy Approach. IEEE Trans. Fuzzy Syst. 4, 32–43 (1996)
Li, Y., Qiang, S., Zhang, X., Kaynak, O.: Robust and Adaptive Backstepping Control for Nonlinear Systems Using RBF Neural Networks. IEEE Trans. Neural Networks 15, 693–701 (2004)
Lin, C.T., George Lee, C.S.: Neural Fuzzy Systems: a Neuro-Fuzzy Synergism to Intelligent Systems. Prentice-Hall, Englewood Cliffs (1996)
Pham, D.T., Liu, X.: Dynamic System Identification Using Partially Recurrent Neural Networks. J. Syst. Eng. 2, 90–97 (1992)
Poznyak, A.S., Yu, W., Sanchez, D.N., Perez, J.P.: Nonlinear Adaptive Trajectory Tracking Using Dynamic Neural Networks. IEEE Trans. Neural Networks 10, 1402–1411 (1999)
Chow, T.W.S., Li, X.D., Fang, Y.: A Real-Time Learning Control Approach for Nonlinear Continuous-Time System Using Recurrent Neural Networks. IEEE Trans. Ind. Electronics. 47, 478–486 (2000)
Ren, X.M., Rad, A.B., Chan, P.T., Lo, W.L.: Identification and Control of Continuous-time Nonlinear Systems via Dynamic Neural Networks. IEEE Trans. Ind. Electronics 50, 478–486 (2003)
Hopfield, J.J.: Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proceedings of National Academy of sciences, USA 79, 2554–2558 (1982)
Hopfield, J.J.: Neurons with Graded Response Have Collective Computational Properties Like Those of Two-State Neurons. Proceedings of National Academy of sciences, USA 81, 3088–3092 (1984)
Wang, L.X.: Adaptive Fuzzy Systems and Control - Design and Stability Analysis. Prentice-Hall, Englewood Cliffs (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Chen, PC., Wang, CH., Lee, TT. (2009). Indirect Adaptive Control Using Hopfield-Based Dynamic Neural Network for SISO Nonlinear Systems. In: Fodor, J., Kacprzyk, J. (eds) Aspects of Soft Computing, Intelligent Robotics and Control. Studies in Computational Intelligence, vol 241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03633-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-03633-0_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03632-3
Online ISBN: 978-3-642-03633-0
eBook Packages: EngineeringEngineering (R0)