Abstract
Neural network controller using adaptation algorithm is a new and simple controller, in which a feedback network propagating the error is not required. So it can be applied to hardware easily. Nevertheless, our simulations show that while the order of controlled plant is high, some unstable phenomenon appear and we also find that sometimes the error is far from being satisfactory, although when the order of controlled plant is low. Moreover, the present adaptation algorithm can not solve this problem. In this paper we will give our derivation of adaptation algorithm used in the neural network controller and configuration of an adaptive neural network controller. Then give some simulation figures to illustrate defect for the new controller. Finally we will develop a hybrid neural network to solve the problem and improve the accuracy as well as reduce the cost to the least in the practical application.
This paper is supported by the National Natural Science Foundation of China (20577038).
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
Brandt, R.D., Lin, F.: Supervised Learning in Neural Network Without Explicit Error Back-propagation. In: Proceeding of the 32nd Annual Allenton Conference on Communication, Control and Computing, pp. 294–303 (1994)
Brandt, R.D., Lin, F.: Can Supervised Learning be Achieved without Explicit Error Back-propagation. In: Proceeding of International conference on Neural Networks, pp. 300–305 (1996)
Sakelaris, G., Lin, F.: A neural Network Controller by Adaptive Interaction. In: Proceedings of the American Control Conference, Arrington, VA, June 25–27 (2001)
Cabrera, J.B.D., Narendra, K.S.: Issues in the Application of Neural Networks for Tracking Based on Inverse Control. IEEE Trans. Automatic Control 44, 2007–2027 (1999)
Brandt, R.D., Lin, F.: Adaptive Interaction and Its Application to Neural Networks. Information Sciences 121, 201–205 (1999)
Brandt, R.D., Lin, F.: Optimal Layering of Neurons. In: IEEE International Symposium on Intelligent Control, pp. 497–501 (1996)
Narendra, K.S., Parthasarathy, K.: Identification and Control of Dynamical Systems Using Neural Networks. IEEE Trans. Neural Networks 1, 1–27 (1990)
Kuschewski, J.G., Hui, S.: Application of Feedforward Neural Networks to Dynamical System Identificaion and Control. IEEE Trans. Control systems Technology 1, 37–49 (1993)
Levin, A.U., Narendra, K.S.: Control of Nonlinear Dynamical Systems Using Neural Networks. IEEE Trans. Neural Networks 7, 30–42 (1996)
Chen, F.C., Khalil, H.K.: Adaptive Control of Nonlinear Systems Using Neural Networks. In: IEEE Proceedings on the 29th Conference on Decision and Control, vol. 3, pp. 1707–1712 (1990)
Narendra, K.S., Parthasarathy, K.: Gradient Methods for Optimization of Dynamical systems Containing Neural Networks. IEEE Trans. Neural Networks 2, 252–262 (1991)
Yamada, T., Yabuta, T.: Neural Network Controller Using Autoturning Method for Nonlinear Functions. IEEE Trans. Neural Networks 3, 595–601 (1992)
Chen, F.C., Khalil, H.K.: Adaptive Control of a Class of Nonlinear Discrete-Time Systems Using Neural Networks. IEEE Trans. Automatic Control 40, 791–801 (1995)
Brdys, M.A., Kulawski, G.L.: Dynamic Neural for Induction Motor. IEEE Trans. Neural Networks 10, 340–355 (1999)
Narendra, K.S., Mukhopadhyay, S.: Adaptive Control Using Neural Networks and Approximate Models. IEEE Trans. Neural Networks 8, 475–485 (1999)
Park, Y.M., Choi, M.S., Lee, K.Y.: An Optimal Tracking Neuro-Controller for Nonlinear Dynamic Systems. IEEE Trans. Neural Networks 7, 1099–1110 (1999)
Sakelaris, G., Lin, F.: A neural Network Controller by Adaptive Interaction. In: Proceedings of the American Control Conference, Arrington VA, June 2001, pp. 25–27 (2001)
Brandt, R.D., Lin, F.: Supervised Learning in Neural Networks Without Feedback Network. In: IEEE International Symposium on Intelligent Control, pp. 86–90 (1996)
Shukla, D., Dawson, D.M., Paul, F.W.: Multiple Neural-Network Based Adaptive Controller Using Orthonormal Activation Function Neural Networks. IEEE Trans. Neural Networks 10, 1494–1501 (1999)
Spooner, J.T., Passino, K.M.: Decentralized Adaptive Control of Nonlinear Systems Using Radial Basis Neural Networks. IEEE Trans. Neural Networks 44, 2025–2057 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cai, M., Liu, J., Tian, G., Zhang, X., Wu, T. (2007). Hybrid Neural Network Controller Using Adaptation Algorithm. In: Liu, D., Fei, S., Hou, ZG., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4491. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72383-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-72383-7_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72382-0
Online ISBN: 978-3-540-72383-7
eBook Packages: Computer ScienceComputer Science (R0)