Optimisation of neural network controller architecture in DC motor model

• Autorzy: Józefowicz K. , Kołota J. , Stepień S.
• Języki publikacji: EN

Abstrakty

EN

The past few years have witnessed a dynamic growth variety of neural network applications. Range of these applications is very wide especially in industrial process control. As in nature, the neural network is determined by the connections between the elements, and we can train its to perform the particular function by adjusting special values (weights between elements) [1,4,7]. This paper presents DC motor model controlled by neural network Proportional-Derivate controller in comparison with classic PD controller. The research concern different network architectures and training functions. The models are presented and the experimental results signals are shown using graphical charts.

Słowa kluczowe

EN

Levenberg-Marquardt; neural network

PL

sieć neuronowa; metoda Levenberga-Marquardta

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Journal of Applied Computer Science
• Rocznik: 2008
• Tom: Vol. 16, nr 2
• Strony: 81--88
• Opis fizyczny: Bibliogr. 9 poz.

Twórcy

autor

Józefowicz K.

autor

Kołota J.

autor

Stepień S.

• Poznan University of technology Chair of Computer Engineering ul. Piotrowo 3a, 60-965 Poznan, Poland,

Bibliografia

• [1] Demuth H., Beale M., Hogan M.: Neural Network Toolbox User's Guide, The MathWorks, Inc. 1992-2007.
• [2] Le Cun Y., Kanter I., Soła S.A.: A statistical to generalization in layered neural networks, Proceedings of the IEEE, No 78(10), pp. 1568-1574, 1991.
• [3] Lowe D., Webb A.R.: Optimized feature extraction and the Bayes decision in feedforward classifier networks, IEEE Trans, on Pattern Analysis and Machine Intelligence, Vol. 13, April 1991, pp. 355-364.
• [4] Marashdeh Q., Warsito Q., Liang-Shih F., Teixeira F.L.: Nonlinear forward problem solution for electrical capacitance tomography using feed-forward neural network, IEEE Sensors Journal, Vol. 6, Issue 2, April 2006, pp. 441-449.
• [5] Simulink Control Design User's Guide, The MathWorks, Inc. 1990-2007.
• [6] Narendra K.S. and Parthasarathy K.: Identification and Control of Dynamical Systems Using Neural Networks, IEEE Transactions on Neural Networks, Vol. 1, pp. 4-27,1990.
• [7] Hui H., Peng-Yung W.: Fuzzy supervisory sliding-mode and neural-network control for robot manipulators, IEEE Transactions on Industrial Electronics, Vol. 53, Issue 3, June 2006, pp. 929-940.
• [8] Sangbong P., Cheol Hoon P., Kuntanapreeda S., Fullmer R.R.: Comments on "A training rule which guarantees finite-region stability for a class of closed-loop neural-network control systems", IEEE Transactions on Neural Networks, Vol. 8, Issue 5, Sept. 1997, pp. 1217-1218.
• [9] Safaric R., Jezernik K., Rodic M., Sabanovic A., Uran S.: Sliding-mode neural network robot controller. Advanced Motion Control, 1996. AMC '96-MIE. Proceedings, 1996 4th International Workshop on Vol. 1, 18-21 March 1996, pp. 395-400.