Abstract
A new method of fuzzy multiple reference models adaptive control (FMRMAC) for dealing with significant and unpredictable system parameter variations is presented. In this method, a suitable reference model is chosen by parameters estimation and fuzzy rules when changes occurred to the original model parameters. A successful application to the speed servo system of a dynamic model of a Brushless DC motor (BLDCM) shows this method works well with high dynamic performance under the condition of command speed change and load torque disturbance, so the applicability and validity of FMRMAC in pa-rameters variation system accommodation control was proven.
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References
Michael, A.D., Rosen, I.G.: Variable structure model reference adaptive control of parabolic distributed parameter systems. In: Proceeding of the American Control Conference, Anchorage, pp. 4371–4376 (2002)
Mitsuru, K., Masayoshi, T.: Model reference adaptive control of linear systems with input saturation. In: Proceeding of the 2004 IEEE International Conference on Control Application, Taipei, Taiwan, pp. 1318–1323 (2004)
Daniel, E.M.: A new approach to model reference adaptive control. IEEE Trans. Automatic Control. 48, 743–757 (2003)
Alvaro, K., Ramon, R., Liu, H., et al.: Multivariable adaptive control using high-frequency gain matrix factorization. IEEE Trans. Automatic Control. 49, 1152–1157 (2004)
Narendra, K., Balakrishnan, J.: Adaptive control using multiple models. IEEE Trans. Automatic Control. 42, 171–187 (1997)
Wang, Y., Liu, W.: A kind of PID-typed fuzzy neural network controller. Journal of System Simulation 15, 389–392 (2003)
Sukumar, K.: A new generation of adaptive control: an intelligent supervisory approach. Toledo University, Toledo (2004)
Sukumar, K., Adel, A.G., Khalid, S.A.: A fuzzy multiple reference model adaptive control scheme for flexible link robotic manipulator. In: Proceeding of the 2004 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Boston, pp. 162–167 (2004)
Zhang, B., Li, Z., Mao, Z.: Mathematical model of permanent-magnet synchronous motors and its fuzzy modeling. Control theory and application 19, 841–844 (2002)
Ji, Z., Shen, Y., Jiang, J.: A novel method for modeling and simulation of BLDC system based on Matlab. Journal of System Simulation 15, 1745–1749 (2003)
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© 2006 Springer-Verlag Berlin Heidelberg
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Ji, Z., Zhu, R., Shen, Y. (2006). Fuzzy Multiple Reference Models Adaptive Control Scheme Study. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_41
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DOI: https://doi.org/10.1007/11739685_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
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