Abstract
In this paper a new self-tuning controller algorithm for non-linear dynamical systems has been derived using the Radial Basis Function Neural Network (RBF). In the proposed controller, the unknown non-linear plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a non-linear sub-model. The parameters of the linear sub-model are identified by a recursive least squares algorithm with a directional forgetting factor, whereas the unknown non-linear sub-model is modelled using the (RBF) network resulting in a new non-linear controller with a generalised minimum variance performance index. In addition, the proposed controller overcomes the shortcomings of other linear designs and provides an adaptive mechanism which ensures that both the closed-loop poles and zeros are placed at their pre-specified positions. Example simulation results using a non-linear plant model demonstrate the effectiveness of the proposed controller.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .
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References
Clarke, D.W., Gawthrop, P.J.: Self-tuning control. Proc. Inst. Electr. Engineering, Part D 126, 633–640 (1979)
Astrom, K.J., Wittenmark, B.: On self-tuning regulators. Automatica 9, 185–199 (1973)
Allidina, A.Y., Hughes, F.M.: Generalised self-tuning controller with pole assignment. Proc. Inst. Electr. Engineering, Part D 127, 13–18 (1980)
Zhu, Q., Ma, Z., Warwick, K.: Neural network enhanced generalised minimum variance self-tuning controller for nonlinear discrete-time systems. IEE Proc. Contol theory Appl. 146, 319–326 (1999)
Zayed, A., Hussain, A., Smith, L.: A New Multivariable Generalised Minimum-variance Stochastic Self-tuning with Pole-zero Placement. International Journal of Control and Intelligent Systems 32(1), 35–44 (2004)
Sirisena, H.R., Teng, F.C.: Multivariable pole-zero placement self-tuning controller. Int. J. Systems Sci. 17(2), 345–352 (1986)
Zayed, A., Hussain, A., Grimble, M.: Novel non-linear Multiple-Controller framework for Complex Systems incorporating a Learning sub-model, invited paper. Int. J. of Control & Intelligent Systems, Special Issue, 33 (to appear 2005)
Zayed, A., Hussain, A., Smith, L.: A New Non-linear Self-tuning pole-zero placement incorporating Neural Networks. In: Proceedings of the third International NASO Symposium on Engineering of Intelligent Systems Conference, Malaga Spain, December 24-27, p. 107 (2002); paper no 130 in CD
Haykin, S.: Neural Networks A Comprehensive Foundation. Macmillan, Basingstoke (1994)
Passino, K.: Biomimcry for Optimization, Control, and Automation. Springer, Heidelberg (2005)
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Abdullah, R., Hussain, A., Zayed, A. (2005). A New RBF Neural Network Based Non-linear Self-tuning Pole-Zero Placement Controller. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_56
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DOI: https://doi.org/10.1007/11550907_56
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