In order to suppress the influence of inertia disturbance on a high performance servo drive system, the system should possess the function of inertia identification and self-tuning of controller parameters. Among them, identifying the real-time inertia value accurately and rapidly should be solved first as the key. Research on inertia identification is conducted in this paper, which derives an inertia identification gradient formula of the permanent magnet synchronous motor (PMSM), and analyzes factors influencing the convergence time and precision of inertia identification. Simulation and experiments demonstrate that the gradient algorithm is effctive. The convergence time of identification is short and it can be limited to around five seconds.The shorter the period and the larger the given range of a given velocity, the better is the real-time property of inertia identification. This study can provide basis for the parameter self-tuning of the speed controller.
LIANG Jiaoyan, HU Yuwen, LU Wenqi
. Research on Inertia Identification Performance of Permanent Magnet Servo Systems Based on Gradient Algorithm[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2011
, 32(3)
: 488
-496
.
DOI: CNKI:11-1929/V.20101111.0914.027
[1] Fujita K, Sado K. Instantaneous speed detection with parameter identification for AC servo systems[J]. IEEE Transactions on Industry Applications, 1990, 28(4): 864-872.
[2] Guo Y J, Huang L P, Muramatsu M. Research on inertia identification and auto-tuning of speed controller for AC servo system//Proceedings of the Power Conversion Conference-Osaka. 2002, 2: 896-901.
[3] Lee K B, Yoo J Y, Song J H, et al. Improvement of low speed operation of electric machine with an inertia identification using ROELO[J]. IEE Proceedings Electric Power Applications, 2004, 151(1): 116-120.
[4] Cao X Q, Bi M. Extended Luenberger observer based on dynamic neural network for inertia identification in PMSM servo system//2009 Fifth International Conference on Natural Computation. 2009, 2: 48-52.
[5] Dessaint L A, Hebert B J, Le-Huy H, et al. A DSP-based adaptive controller for a smooth positioning system[J]. IEEE Transactions on Industrial Electronics, 1990, 37(5): 372-377.
[6] 刘永钦, 沈艳霞, 纪志成. 基于改进型最小二乘法的感应电机转动惯量辨识[J]. 电机与控制应用, 2008, 35(12): 13-17. Liu Yongqin, Shen Yanxia, Ji Zhicheng. Induction motor inertia identification based on improved least square method[J]. Electric Machines & Control Application, 2008, 35(12): 13-17. (in Chinese)
[7] de Campos M, Caratti E G, Grundling H A. Design of a position servo with induction motor using self-tuning regulator and Kalman filter//Conference Record of the 2000 IEEE Industry Applications Conference. 2000, 3: 1613-1618.
[8] Li S H, Liu Z G . Adaptive speed control for permanent magnet synchronous motor system with variations of load inertia[J]. IEEE Transactions on Industrial Electronics, 2009, 56(8): 3050-3059.
[9] 徐湘元. 自适应控制理论与应用[M]. 北京: 电子工业出版社, 2007: 21-23. Xu Xiangyuan. Theory and application of adaptive control[M]. Beijing: Electrical Industry Press, 2007: 21-23. (in Chinese)
[10] 丁锋, 杨慧中. 基于梯度的扰动时变系统辨识算法及其收敛性[J]. 江南大学学报: 自然科学版, 2005, 4(3): 221-226. Ding Feng, Yang Huizhong. Gradient based identification algorithm and its convergence for disturbance time-varying systems[J]. Journal of Southern Yangtze University: Natural Science Edition, 2005, 4(3): 221-226. (in Chinese)
[11] L. 雍T. 索德斯图姆. 递推辨识的理论与实践[M]. 北京: 科学出版社, 1989. Lennart Ljung Torsten Sderstrm. Theory and practice of recursive identification[M]. Beijing: Science Press, 1989. (in Chinese)
[12] 胡寿松. 多变量系统参数辨识的相关分析法[J]. 航空学报, 1990, 11(7): 400-404. Hu Shousong. Identification of parameters of MIMO system by correlation analysis[J]. Acta Aeronautica et Astronautica Sinica, 1990, 11(7): 400-404. (in Chinese)
CJA