Warianty tytułu
Nowy adaptacyjny odwrotny system sterowania silnikiem synchronicznym o magnesach trwałych
Języki publikacji
Abstrakty
The adaptive inverse control is a novel method in control system. It makes set signal, parameter disturbance and external disturbance separately controlled and makes them reach the optimal control without compromiseThe traditional adaptive inverse control system often used FIR filters. It madde the system costing long training time and slow convergence. So, it is unable to adapt the requirement of real-time control system. In this paper, a novel adaptive inverse control with adaptive neuro-fuzzy inference system (ANFIS) was designed for permanent magnet synchronous motor (PMSM) servo system. Meanwhile the microhabitat particle swarm optimization (MPSO) algorithm and RLS algorithm were used for updating parameters for ANFIS. This hybrid learning algorithm can reduce the computing costs and improve the convergence speed. Also, the ANFIS was used to for identification and inverse modeling of PMSM servo system. The simulation results show that the PMSM servo system based on novel adaptive inverse control strategy achieve higher tracking ability, steady precision and good robustness.
Przedstawiono nowy adaptacyjny system sterowania odwrotnego wykorzystujący neuronowy-rozmyty system interferencji ANFIS. System zastosowano do układu serwomechanizmu z silnikiem synchronicznym o magnesach trwałych. Dodatkowo rojowy algorytm optymalizacji oraz algorytm RLS były wykorzystywane do zmiany parametrów. Badania symulacyjne potwierdziły, że nowy system ma dobra precyzję i odporność na zakłócenia.
Czasopismo
Rocznik
Tom
Strony
9-14
Opis fizyczny
Bibliogr. 18 poz., schem., tab., wykr.
Twórcy
Bibliografia
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- [8] Shinkyu Jeong, Shoichi Hasegawa, Koji Shimoyama, Shigeru Obayashi. Dvelopment and Investigation of Efficient GA/MPSO-Hybrid Algorithm Applicable to Real-World Design Optimization[J]. IEEE Computational Intelligence Magazine, (2009).
- [9] H Shayeghi, M Mahdavi, A Bagheri. Discrete MPSO algorithm based optimization of transmission lines loading in TNEP problem[J]. Energy Conversion and Management, (2010), 112–121.
- [10] Zhiyuan Duan, Chengxue Zhang, Zhijian Hu, Tao Ding. Design for Multi-machine Power System damping Controller Via Particle Swarm Optimization Approach[J]. SUPERGEN '09. International Conference (2009),1-6.
- [11] Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab, Ali Khaki Sedigh. Training ANFIS as an identifier with intelligent hybryd stable learning algorithm based on particle swarm optimization and extended Kalman filter[J]. Fuzzy Sets and Systems, (2009), 922–948.
- [12] Cihan Karakuzu. Fuzzy controller training using particle swarm optimization for nonlinear system control[J]. ISA Transactions (2008), 229-239.
- [13] J P S Catalão, H M I Pousinho, V M F Mendes. Hybrid Wavelet-MPSO-ANFIS Approach for Short-Term Wind Power Forecasting in Portugal[J]. IEEE Transactions on Sustainable Energy, (2011), 50-59.
- [14] Mahdi Aliyari Shoorehdeli, Mohammad Teshnehlab, Ali Khaki Sedigh, et al. Identification using ANFIS with intelligent hybrid stable learning algorithm approaches and stability analysis of training methods[J]. Applied Soft Computing, (2009), No.9, 833–850.
- [15] Cetin Elmas, Oguz Ustun. A hybrid controller for the speed control of a permanent magnet synchronous motor drive[J]. Control Engineering Practice, (2008), 260-270.
- [16] Rui-Hua Li, Jian-Fei Zhao,Bo Hu,et al. Adaptive inverse control of permanent magnet synchronous motor drive in a micro-electric vehicle[J]. Proceeding of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, (2009), 1909-1914.
- [17] Hong-Wei Ge, Feng Qian, Yan-Chun Liang, et al. Identificaton and control of nonlinear systems by a dissimilation particie swarm optimization-based Elman neural network[J]. Nonlinear Analysis, (2008), 1345-1360.
- [18] Lina Yang, Gang Liu, Huade Li. Adaptive Inverse Control of Permanent Magnet Synchronous Motors Drive System[J]. Machine Learning and Cybernetics International Conference, (2009), 1909-1914.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.baztech-article-BPOH-0062-0003