基于遗传算法的磁流变阻尼器模型参数识别
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摘要
磁流变阻尼器是一种具有广阔应用前途的半主动控制装置,但其复杂的力学特性很难精确描述。非线性参数模型是一个相对简单并且能够很好地描述磁流变阻尼器力学特性的力学模型,但该模型既是非线性的、又是非解析的,参数识别十分困难。结合传统遗传算法良好的全局搜索能力和二分法可靠的收敛性,本文提出了一种改进的遗传算法——层次压缩遗传算法。对非线性参数模型的参数识别结果显示:层次压缩遗传算法简单可行,具有较高的识别精度。同时也验证了非线性参数模型描述磁流变阻尼器阻尼力特性的准确性。
Magnetorheological(MR) dampers are full of promise in vibration control of structures,however,their dynamic characters is very complicated.The nonlinear parameter model is one of the simplified models which can describe the dynamic performance of MR dampers very well in all regions.One of major problems with this model is that it is difficult to identify the model parameters due to the nonlinear and non-analytic formulation of the model.An improved genetic algorithm,namely the hierarchical compaction genetic algorithm(HCGA),is proposed and employed to identify the model parameters of MR dampers.The proposed algorithm has advantages to the global searching ability of genetic algorithm and the stable convergence of dimidiate searching root method.The results show that the HCGA is simple,effective,and of high precision in identifying the parameters of nonlinear model.It is also validated that the nonlinear parameter model describes well the dynamic performance of MR dampers.
Magnetorheological(MR) dampers are full of promise in vibration control of structures,however,their dynamic characters is very complicated.The nonlinear parameter model is one of the simplified models which can describe the dynamic performance of MR dampers very well in all regions.One of major problems with this model is that it is difficult to identify the model parameters due to the nonlinear and non-analytic formulation of the model.An improved genetic algorithm,namely the hierarchical compaction genetic algorithm(HCGA),is proposed and employed to identify the model parameters of MR dampers.The proposed algorithm has advantages to the global searching ability of genetic algorithm and the stable convergence of dimidiate searching root method.The results show that the HCGA is simple,effective,and of high precision in identifying the parameters of nonlinear model.It is also validated that the nonlinear parameter model describes well the dynamic performance of MR dampers.
引文
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[8]李守巨,刘迎曦,任明法,等.基于改进遗传算法的水轮发电机振动荷载参数识别[J].工程力学,2003,20(5):163-169.
[2]Yang G.Large-scale magnetorheological fluid damper for vibration mitigation:modeling testing and control[D].University of Notre Dame,Indi-ana,USA,2001.
[3]欧进萍,关新春.磁流变耗能器性能的试验研究[J].地震工程与工程振动,1999,19(4):76-81.
[4]李宏男,常治国,赵柏东.微种群遗传算法优化结构振动控制[J].地震工程与工程振动,2002,22(5):92-96.
[5]Spencer B F,Dyke S J,Sain MK,et al.Phenomenological model for magnetorheological dampers[J].Journal of Engineering Mechanics,1997,123(3):230-238.
[6]翁建生,胡海岩,张庙康.磁流变阻尼器的实验建模[J].振动工程学报,2000,13(4):616-621.
[7]Loh C H,Chung S T.Athree-stage identification approach for hysteretic systems[J].Earthquake Engineering and Structural Dynamics,1993,22(1):129-150.
[8]李守巨,刘迎曦,任明法,等.基于改进遗传算法的水轮发电机振动荷载参数识别[J].工程力学,2003,20(5):163-169.
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