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Neural network control with fuzzy logic compensation

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper presents a new AI based control strategy. A dynamic neural network is used to identify the plant on-line. The control signal is then calculated iteratively according to the responses of a reference model and the identified neural model of the process. A fuzzy logic block with four very simple rules is added to the loop to improve the overall loop properties. This synergetic AI control paradigm is tested via simulation. The results demonstrate that the proposed control strategy provides better disturbance rejection and tracking properties of the control loop than those achieved by an optimally tuned PID controller.
Rocznik
Strony
31--44
Opis fizyczny
Bibliogr. 27 poz.
Twórcy
autor
  • Centre for Engineering Research, Technikon Natal, P.O. Box 4000, Durban, South Africa
autor
  • Centre for Engineering Research, Technikon Natal, P.O. Box 4000, Durban, South Africa
autor
  • Centre for Engineering Research, Technikon Natal, P.O. Box 4000, Durban, South Africa
autor
  • Centre for Engineering Research, Technikon Natal, P.O. Box 4000, Durban, South Africa
Bibliografia
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  • [3] V. B. Bajić and A. Rybalov, Fuzzy Modifier of PID Control for Improvement of Tracking Properties in Servo System, Proceeding of the International Conference on Intelligent Technologies in Human-Related Sciences (ITHURS'96), Vol. II, pp. 111-115, Leon, Spain, July 5-7, 1996.
  • [4] V. B. Bajić and M. Mcleod, Process Identification and Assisted Controller Tuning for Industrial Process Loops, Preprints of the IFAC-IFIP-IMACS Conference, Vol. 3/3, pp. 216-221, Belfort, France, May 20-22, 1997.
  • [5] P. B. Boyagoda and M. Nakaoka, An Advanced Tracking Controller with Neural Networks for Servo Systems, IEEE Trans. Ind. Electron., Vol. 47, No. 1, pp. 219-222, Feb., 2000.
  • [6] С. H. Chen (Ed.), Fuzzy Logic and Neural Network Handbook, McGraw-Hill, Inc., 1996.
  • [7] T. C. Chen and C. Y. Liaw, Robust Sliding Mode Controller with Fuzzy Load Torque Identifier for Induction Motor Drive, International Journal of Knowledge-Based Intelligent Engineering Systems, Vol. 3, No. 3, pp. 154-161, 1999.
  • [8] T. C. Chen and C. Y. Liaw, Robust Speed controlled Induction Motor Drive Based on Model Reference with Neural Network, International Journal of Knowledge-Based Intelligent Engineering Systems, Vol. 3, No. 3, pp. 162-171, 1999.
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  • [10] P. Govender and V. B. Bajić, Fuzzy Logic Enhancement of Antiwindup PID Control for Servo Systems, in Advances in Intelligent Systems (F.C. Morabito, Ed.), pp. 474-478, IOS Press, Amsterdam, 1997.
  • [11] C. Y. Huang, T. C. Chen and C. L. Huang, Robust Control of Induction Motor with A Neural-Network Load Torque Estimator and A Neural-Network Identification, IEEE Trans. Ind. Electron., Vol. 46, No. 5, pp. 990-998, Oct., 1999.
  • [12] S. J. Huang and R. J. Lian, A Hybrid Fuzzy Logic and Neural Network Algorithm for Robot Motion Control, IEEE Trans. Ind. Electron., Vol.44, No.3 pp. 217-229, Jun., 2000.
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  • [14] Liang Jin and P. N. Nikiforuk, Adaptive Control of Discrete-time Nonlinear Systems Using Recurrent Neural Networks, IEE Proc., Control Theory Application, Vol. 141. No. 3, pp. 169-176, 1994.
  • [15] Liang Jin and P. N. Nikiforuk, Dynamical Recurrent Neural Networks for Unknown Nonlinear Systems, ASME Journal of Dynamic Systems, Measurement and Control, Vol. 116, No. 4, pp. 567-576,1994.
  • [16] K. Kiguchi and T. Fukuda, Intelligent Position/Force Controller for Industrial Robot Manipulators-Application of Fuzzy Neural Networks, IEEE Trans. Ind. Electron., Vol. 44, No. 6, pp. 753-761, Dec., 1997.
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  • [21] K. S. Narendra and K. Parthasarthy, Identification and Control of Dynamical System Using Neural Networks, IEEE Trans. Neural Networks, Vol. 1, No. 1, pp. 4-27, 1990.
  • [22] K. S. Narendra and S. Mukhopadhyay, Adaptive Control of Nonlinear Multivariable Systems Using Neural Networks', Neural Networks, Vol. 7, No. 5, pp. 737-752, 1994.
  • [23] A. Rubaai and R. Kotaru, Neural Net-Based Robust Controller Design for Brushless DC Motor Drives, IEEE Trans. Syst. Man. Cybern., Vol. 29, No. 3, pp. 460-473, Aug., 1999.
  • [24] D. Sbarbaro, J. P. Segovia, S. Alcozer and J. Gonzales, Applications of Radial Basis Network Technology to Process Control, IEEE Trans. Control Systems Technology, Vol. 8, No. 1, pp. 14-20, Jan, 2000.
  • [25] P. K. Simpson (Ed.), Neural Networks Applications, IEEE Technology Activities Board, IEEE Inc., New York, 1996.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-LOD7-0028-0046
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