Composite Adaptive Inverse Controller Design for Permanent Magnet Synchronous Motor

• Autorzy: Qu Y. , Gong Y. , Cui Y. , Han T.

Warianty tytułu

PL

Adaptacyjny, odwrotny system sterowania silnikami synchronicznymi z magnesem trwałym PMSM

• Języki publikacji: EN

Abstrakty

EN

Permanent magnet synchronous motor (PMSM) servo system is a nonlinear, multi-variables strong coupling system. To improve the performance of the PMSM system, a composite adaptive inverse control strategy is proposed. This control strategy adopt improved radial basis function (RBF) neural network and FIR filter as nonlinear filter. The proposed filter is used to identify the system, inverse system and design the adaptive inverse controller. Meanwhile the chaos multi-population particle swarm optimization (CMPSO) algorithm is proposed to training the parameters of the nonlinear filter offline. And then an improved variable step size LMS (IVSLMS) algorithm is used to optimize the parameters online. These algorithm improves the convergence speed and accuracy, further improves the control performance of adaptive inverse control. The results of simulation and experiment indicate that the PMSM servo system has good dynamic, static performance and robustness by using proposed hybrid adaptive inverse control strategy.

PL

W celu poprawy parametrów silnika synchronicznego z magnesami trwałymi PMSM zaproponowano kompozytowa adaptacyjną strategię sterowania. Strategia wykorzystuje sieć neuronową i nieliniowy filtr SOI.

Słowa kluczowe

EN

PMSM; adaptive inverse control; nonlinear filter; CMPSO algorithm; IVSLMS algorithm

PL

silnik synchroniczny PMSM; filtr nieliniowy

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 7b
• Strony: 365--369
• Opis fizyczny: Bibliogr. 12 poz., schem., wykr.

Twórcy

autor

Qu Y.

• Changchun University of Science and Technology
• Beihua University

autor

Gong Y.

• Changchun University of Science and Technology

autor

Cui Y.

• Beihua University

autor

Han T.

• Changchun University of Science and Technology

Bibliografia

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• [2] Cetin Elmas, Oguz Ustun. A hybrid controller for speed control of a permanent magnet sychronous motor drive. Control Engineering Practce, 16(2008), 260-270.
• [3] Jong-Sun Ko, Byung-Moon Han. Precision Position Control of PMSM using Neural Network Disturbance Observer and Parameter Compensator. IEEE International Conference, (2006), 316-320.
• [4] Guo Qingding, Sun Yibiao, Wang Limei. Modern Permanent Magnet Synchronous Motor Servo System. Beijing: Chinese Electric Power Press, (2006).
• [5] Bernard Widrow. Adaptive Inverise Control. IFAC Adaptive Inverise Control and Signal Proceing, Lund, Sweden, (1986).
• [6] Liu XiaoQing, Yi Jianqiang, Zhao Dongfu, et al. a nonlinear adaptive inverse control system based on RBF network[J]. Control and Decision, 19(2004), 1175-1177.
• [7] Han Hua. The research of adaptiveinverse control based on LMS algorithmf. Central South University Doctoral Dissertation, (2008).
• [8] Selami Beyhan, Musa Alci. Stable modeling based control methods using a new RBF network. ISA Transactions, 49(2010), 510-518.
• [9] Kennedy J, Eberhart R. Particle Swarm Optimization [C].IEEE Int Conference on Neural Networks, Perth, Australia, 1995:1942-1948.
• [10] D C Huynh, M W Dunnigan. Parameter estimation of an induction machine using advanced particle swarm optimization algorithms[J]. IET Electric Power Applications, 2010, 4(9):748-760.
• [11] J Moody, C J Darken. Fast learning in networks of locally tuned processing units. Neural Computation, (1989), 281-294.
• [12] Yang Zhidong, Huang Qitao, Han Junwei,et al. Adaptive inverse control of random vibration based on the filtered-X LMS algorithm. Earthquake engineering and engineering vibration, 9(2010),141-146.