Researches on Parameter Identification of Nonlinear Model for Giant Magnetostrictive Actuator

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Journal of Vibration and Shock ›› 2015, Vol. 34 ›› Issue (18) : 142-146.

Yangli Hua1, LI Jian-fei1,WU Hai-ping1, LOU Jing-jun2

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Abstract

Accurate identification of nonlinear model parameters is a prerequisite to precisely control precise displacement of giant magnetostrictive actuator, Aiming at the shortcomings of standard PSO algorithm such as existing premature convergence and easily falling into local optimum in later iteration. An improved PSO identification algorithm is proposed in this paper, which can dynamically change inertia weighting, study factors and genetic variation, so it can balance global and local search capability to improve the convergence speed and identification accuracy. Moreover, it is applied to parameters identification of nonlinear model for giant magnetostrictive actuator. Then the results show that: the improved algorithm can effectively identify the nonlinear model parameters of giant magnetostrictive actuator. It also has a higher degree of agreement between calculations and experiments and a better anti-interference ability.

Key words

Giant magnetostrictive actuator / nonlinear model / parameter identification / improved PSO

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Yangli Hua1, LI Jian-fei1,WU Hai-ping1, LOU Jing-jun2. Researches on Parameter Identification of Nonlinear Model for Giant Magnetostrictive Actuator[J]. Journal of Vibration and Shock, 2015, 34(18): 142-146

References

[1]Valadkhan S，Morris K，Khajepour A. Review and comparison of hysteresis models for magnetostrictive materials[J]. Journal of Intelligent Material System and Structures, 2009,20(1):131-142.
[2]贾振元,王晓煌,王福吉.超磁致伸缩执行器的动力学参数及磁滞模型参数的辨识方法[J].机械工程学报,2007,43(10) :9-13.
Jia Zhen-yuan, Wang Xiao-huang, Wang Fu-ji. Identification method of giant magnetostrictive transducer’s dynamic parameters and magnetic parameters[J].Chinese Journal of Mechanical Engineering, 2007, 43(10):9-13.
[3]Tan Xiao-bo, John S. Baras. Modeling and control of hysteresis in magnetostrictive actuators [J].Automatica,
2004,40:1469-1480.
[4]Smith R.C, Dapino M.J, Seelecke Stefan. Free energy model for hysteresis in magnetostrictive transducers[J]. Journal of Applied Physics, 2003, 93(1):458-466.
[5]Jiles D.C，Thoelke J.B，Devine M.K. Numerical determination of hysteresis parameters for the modeling of magnetic properties using the theory of ferromagnetic hysteresis [J]. IEEE Transactions on Magnetics, 1992, 28(1):27-35.
[6]Calkins F T, Smith R C, Flatau A B. Energy-based hysteresis model for magnetostrictive transducers [J]. IEEE Trans. Magn, 2000,36(2):429-439.
[7]曹淑英，王博文，郑家驹等.应用混合遗传算法的超磁致伸缩致动器磁滞模型的参数辨识[J].中国电机工程学报,2004,24(10):127-132.
Cao Shu-ying，Wang Bo-wen，Zheng Jia-ju,et al. Parameter identification of hysteresis model for giant magnetostrictive actuators using hybrid genetic algorithm [J]. Proceedings of the CSEE, 2004, 24(10):127-132.
[8]徐彭有,杨斌堂,孟光等.天文望远镜子镜超磁致伸缩驱动器驱动模型及参数识别[J].天文研究与技术,2010,7(2):150-157.
Xu Peng-you,Yang Bin-tang,Meng Guang, et al. Modeling and Parameter Identification for Giant Magnetostrictive Actuators Applied in Driving Segmented Mirrors[J]. Astronomical Research and
Technology,2010,7(2):150-157.
[9]Zheng Jia-ju，Cao Shu-ying，Wang Hong-li. Hybrid genetic algorithms for parameter identification of a hysteresis model of magnetostrictive actuators [J].Neurocomputing, 2007, 70 (4-6):749-761.
[10]张伟,柳萍,刘青松,等.基于模糊树的Hammerstein -like模型对超磁致伸缩作动器的建模与控制[J].振动与冲击,2013,32(15):184-189.
Zhang Wei, Liu Ping,Liu Qing-song,et al. Modeling and control of giant magnetostrictive actuator based on a hammerstein-like model by using fuzzy tree method[J].Journal of Vibration and Shock,2013,32(1 5):184-189.
[11]Qinghai Bai.Analysis of Particle Swarm Optimization Algorithm [J].Computer and Information Science, 2010, 3(1):180-184.
[12]Liang J.J, Qin A. K, S. Baskar. Comprehensive Learning Particle Swarm Optimizer for Global Optimization of Multimodal Functions[J]. IEEE Transaction on evolutionary computation, 2006, 10(3): 281-295.
[13]刘红军,刘洁,叶芳.用于超磁致伸缩作动器的一种改进的控制方法[J].哈尔滨工业大学学报,2012,44(9):91-95.
Liu Hong-jun，Liu Jie，Ye Fang. An improved control method for the giant magnetostrictive actuator[J]. Journal of Harbin Institute of Technology,2012,44(9):91-95.
[14]舒亮,陈定方,卢全国等.超磁致伸缩致动器中的时滞建模与Smith控制策略[J].系统仿真学报.2009,21(10): 3017-3021.
   Shu Liang, Chen Dingfang, Lu Quanguo. Modeling of Time-delay in Giant Magnetostrictive Actuator and Smith Strategy[J].Journal of System Simulation. 2009,21(10): 3017-3021.
[15]王博文,曹淑瑛,黄美文.磁致伸缩材料与器件[M].北京:冶金工业出版社, 2008.
   Wang Bowen, Cao Shuying, Huang Meiwen. Magnetostrictive materials and devices[M].Beijing: Metallurgical Industry Press, 2008
[16]Chikazumi S,John Wiley Sons,Kennedy J, Eberhert R C. Particle swarm optimization. IEEE international Conference on Neural Networks, IV. Piscataway, NJ: IEEE Service Certer, 1995:1942-1948．
[17]张家骏.基于粒子群算法的 PID 控制器参数优化研究[J].计算机仿真,2010,27(10):191-222．
   Zhang Jia-jun. Optimization Parameters of PID Controller Parameters Based on Particle Swarm Optimization[J].Computer Simulation,2010,27(10):191-222．
[18]史峰,王晓川,郁磊等.Mat lab神经网络30个案例分析[M].北京航空航天大学出版社,北京,2010.
Shi Feng, Wang Xiao-chuan, Yu Lei,etc. Matlab analysis of neural network in 30 cases [M].Beihang University press, Beijing, 2010