期刊文献+

基于自适应速度观测器的感应电机控制策略 被引量:1

Induction Motor Control Strategy Based on Full Order Adaptive Speed Observer
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摘要 给出了一种基于全阶模型参考自适应速度观测器的感应电机速度和磁通控制策略。该控制策略能确保转子速度和转子磁通幅值准确跟踪参考值,且对转子时间常数的漂移和负载转矩的变化具有强鲁棒性。控制器的结构由开环和闭环两部分组成,其中开环控制器确保控制信号能够准确跟踪期望值,闭环控制器由实现系统鲁棒镇定。所采用的速度观测器在低速条件下性能良好,观测结果明显优于传统速度观测器。仿真结果验证了该控制策略和速度观测器的有效性。 An induction motor speed and flux control strategy based on full order adaptive speed observer was proposed The control strategy ensured the rotor speed and the magnitude of the rotor flux to track their reference value accurately, and had high robustness against to the variations of the rotor time constant and the load torque. The proposed control law was based on two parts: an open-loop controller which ensured that control output could track the desired trajectories accurately and a closed-loop strategy based on PI controllers for the stabilization realized the robust stability. The proposed speed observer had a good performance at low speed region and behaved obvious advantage compared to traditional speed observers. The simulation verified the validity of the method.
出处 《系统仿真学报》 CAS CSCD 北大核心 2009年第20期6581-6584,6588,共5页 Journal of System Simulation
关键词 感应电机 速度控制 全阶观测器 鲁棒性 induction motor speed control full order observer robustness
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参考文献10

  • 1Green T C. Scalar Controlled Induction Motor Drives [D]. UK: Hefiot-Watt University, 1990.
  • 2Theocharis K Boukas, Thomas G Habetler. High-Performance Induction Motor Speed Control Using Exact Feedback Linearization with State and State Derivative Feedback [J]. IEEE Transactions on Power Electronics (S0885-8993), 2004, 19(4): 1022-1028.
  • 3Furtunato A F A, Salazar A O, Dantas de Araujo A. Robust Control for Induction Motor using a Variable Structure Model Reference Adaptive Control [J]. IEEE Power Electronics Congress ($7803-5006), 1998, CIEP98(1): 61-69.
  • 4Miloudi A, Miloud Y, Draou A. A neural Network Based Speed Control Design Strategy of an Indirect Vector Controlled Induction Machine Drive [C]// IEEE Bologna PowerTech Conference ($7803-7967), Bologna, Italy, 2003. USA: IEEE, 2003.
  • 5Uddin M N, Radwan T S, Rahman M A. Performances of Fuzzy- Logic-Based Indirect Vector Control for Induction Motor Drive [J]. IEEE Transactions on Industry Applications (S0093-9994), 2002, 38(5): 1219-1225.
  • 6Utkin V I. Sliding Mode Control Design Principles and Applications to Electric Drives [J]. IEEE Trans. Industrial Electronics (S0278- 0046), 1993, 40(1): 23-26.
  • 7Maaziz M K, Mendes E, P Boucher. A New Nonlinear Multivariable Control Strategy of Induction Motors [J]. Control Engineering Practice (S0967-0661), 2002, 10(6): 605-613.
  • 8Schauder C. Adaptive Speed Identification for Vector Control of Induction Motors without Rotational Transducers [J]. IEEE Trans. Industry Applications (S0093-9994), 1992, 28(5): 1054-1061.
  • 9Fang-Zheng Peng, Fukao T. Robust Speed Identification for Speed-Sensorless Vector Control of Induction Motors [J]. IEEE Trans. Industrial Applications (S0093-9994), 1994, 30(5): 1234-1240.
  • 10Chul-Woo Park, Woo-Hyen Kwon. Simple and Robust Speed Sensorless Vector Control of Induction Motor Using Stator Current Based MRAC [J]. Electric Power Systems Research (S0378-7796), 2004, 71(3): 257-266.

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  • 1刘艳,邵诚.感应电机广义模型的建立及仿真研究[J].系统仿真学报,2004,16(9):2052-2055. 被引量:9
  • 2Alonge F, D'Ippolito F, Ferrante G, et al. Parameter identification of induction motor model using genetic algorithms [J]. IEE Proceedings-Control Theory and Applications (S1350-2379), 1998, 145(6): 587-593.
  • 3Alonge F, Fagiolini A, Sferlazza A. Extended complex Kalman filter for sensorless control of an induction motor [J]. Control Engineering Practice (S0967-0661), 2014, 27(1): 1-10.
  • 4Gutierrez-Villalobos J M, Rodriguez-Resendiz J, Rivas-Araiza E A, et al. A review of parameter estimators and controllers for induction motors based on artificial neural networks [J]. Neurocomputing (S0925-2312), 2013, 118(1): 87-100.
  • 5Arslan M, Cunkas M, Sag T. Determination of induction motor parameters with differential evolution algorithm [J]. Neural Computing and Applications (S0941-0643), 2012, 21(8): 1995-2004.
  • 6Sakthivel V P, Bhuvaneswari R, Subramanian S. Artificial immune system for parameter estimation of induction motor [J]. Expert Systems with Applications (S0957-4174), 2010, 37(8): 6109-6115.
  • 7Sakthivel V P, Bhuvaneswari R, Subramanian S. Multi-objective parameter estimation of induction motor using particle swarm optimization [J]. Engineering Applications of Artificial Intelligence (S0952-1976), 2010, 23(3): 302-312.
  • 8Nikranajbar A, Ebrahimi M K, Wood A S. Parameter identification of a cage induction motor using particle swarm optimization [J]. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering (S0959-6518), 2010, 224(5): 479-491.
  • 9Shi Y, Eberhart R. A modified particle swarm optimizer [C]// Evolutionary Computation Proceedings, the 1998 IEEE International Conference on Computational Intelligence, USA: IEEE, 1998: 69-73.
  • 10Clerc M, Kennedy J. The particle swarm-explosion, stability, and convergence in a multidimensional complex space [J]. IEEE Transactions on Evolutionary Computation (S1089-778X), 2002, 6(1): 58-73.

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