升船机地震鞭梢效应基于神经网络预测的MR智能半主动控制
|
摘要
磁流变(MR)智能阻尼器是利用磁流变液产生阻尼力的半主动控制装置,该装置具有机械构造简单,动力范围宽广,只需要较小的能量输入就能产生大的输出力,因此被证明能有效运用于土木工程结构来抵抗强烈地震和强风。但是由于磁路材料、线圈结构、控制电源等因素的影响,使得MR阻尼器存在明显的磁迟效应。目前,应用MR阻尼器作为控制装置的研究中对磁迟效应考虑较少。神经网络具有很强的自适应性和处理非线性问题的能力,能够对难以用数学方法描述的非线性对象进行精确预测和建模。运用神经网络来预测MR阻尼器的力学性能,通过对未来时刻输出力的精确预测来弥补磁迟效应所耽搁的时间,在此基础上实现基于神经网络预测的MR智能半主动控制。应用提出的方法来控制三峡升船机屋盖MR智能隔震系统以减小顶部厂房的地震鞭梢效应,仿真计算结果表明:神经网络预测能很好的解决MR阻尼器的磁滞效应对控制带来的不利影响,升船机顶部厂房层间位移和柱底弯矩均有明显的减小,能有效抑制升船机顶部厂房的地震鞭梢效应。
Magnetorheological(MR) fluid damper has been shown to meet application demands and constraint to offer an attractive means of protecting civil infrastructure systems against severe earthquake and wind loading because of its mechanical simplicity,low power requirement and large force capacity.For the influence of some factors,such as,the performance of magnetorheological(MR) fluid,magnetic material etc,MR fluid dampers have magnetic hysteresis,that is the response time of the MR damper's magnetic circuit,which can influence the semi-active control effect.Artificial neural network is proposed to predict the response of controlled structure to solve the problem of magnetic hysteresis.Based on the predicted result,the method of fuzzy semi-active control is achieved to be applied to the roof MR intelligent isolation system to prevent the whiplash effect.Numerical results indicate that artificial neural network can efficiently solve the problem resulting from the magnetic hysteresis of the MR damper.The system can prevent the seismic whiplash effect of the top machinery building of ship lift structure and can restrain the displacement between layers and the moment at the bottom of the pillar are obviously restrained.
Magnetorheological(MR) fluid damper has been shown to meet application demands and constraint to offer an attractive means of protecting civil infrastructure systems against severe earthquake and wind loading because of its mechanical simplicity,low power requirement and large force capacity.For the influence of some factors,such as,the performance of magnetorheological(MR) fluid,magnetic material etc,MR fluid dampers have magnetic hysteresis,that is the response time of the MR damper's magnetic circuit,which can influence the semi-active control effect.Artificial neural network is proposed to predict the response of controlled structure to solve the problem of magnetic hysteresis.Based on the predicted result,the method of fuzzy semi-active control is achieved to be applied to the roof MR intelligent isolation system to prevent the whiplash effect.Numerical results indicate that artificial neural network can efficiently solve the problem resulting from the magnetic hysteresis of the MR damper.The system can prevent the seismic whiplash effect of the top machinery building of ship lift structure and can restrain the displacement between layers and the moment at the bottom of the pillar are obviously restrained.
引文
[1]J.Ghaboussi and A.Joghataie.Active Control of StructuresUsing Neural Networks[J].Engrg Mech.Div.,ASCE,1995,121(4):555-567.
[2]K.Nikzad,J.Ghaboussi and S.Paul.A Study of ActuatorDynamics and Delay Compensation Using Neuro-Controllers[J].Engrg Mech.Div.,ASCE,1996,122:966-975.
[3]Spencer Jr B F,Dyke S J,Sain M K,and Carlson J D.Phenomenological Model for Magnetorheological Dampers[J].Journal of Engineering Mechanics,ASCE,1997,123(3):230-238.
[4]Erik A.Johnson,Juan C.Ramallo,Billie F.Spencer,Jr.and Michael K.Sain.Intelligent base isolation systems.Proc.Of 2nd World Conf.On Struct.Control,Kyoto,Japan,1998,1:367-376.
[5]长江水利委员会.三峡大坝永久导航结构研究[M].武汉:湖北科学技术出版社,中国湖北,1997.
[6]瞿伟廉,王军武,徐幼麟,陈朝晖.MR智能阻尼器耦联的带裙房高层建筑结构地震反应的半主动控制[J].地震工程与工程振动,2000,20(4):87-95.
[7]吕明云.大坝升船机地震鞭梢效应的MR智能半主动控制研究[J].武汉理工大学博士学位论文,2002.
[8]袁曾任,人工神经元网络及其应用[M].北京:清华大学出版社,1992.
[9]焦李成.神经网络系统理论[M].西安:西安电子科技大学出版社,1990.
[2]K.Nikzad,J.Ghaboussi and S.Paul.A Study of ActuatorDynamics and Delay Compensation Using Neuro-Controllers[J].Engrg Mech.Div.,ASCE,1996,122:966-975.
[3]Spencer Jr B F,Dyke S J,Sain M K,and Carlson J D.Phenomenological Model for Magnetorheological Dampers[J].Journal of Engineering Mechanics,ASCE,1997,123(3):230-238.
[4]Erik A.Johnson,Juan C.Ramallo,Billie F.Spencer,Jr.and Michael K.Sain.Intelligent base isolation systems.Proc.Of 2nd World Conf.On Struct.Control,Kyoto,Japan,1998,1:367-376.
[5]长江水利委员会.三峡大坝永久导航结构研究[M].武汉:湖北科学技术出版社,中国湖北,1997.
[6]瞿伟廉,王军武,徐幼麟,陈朝晖.MR智能阻尼器耦联的带裙房高层建筑结构地震反应的半主动控制[J].地震工程与工程振动,2000,20(4):87-95.
[7]吕明云.大坝升船机地震鞭梢效应的MR智能半主动控制研究[J].武汉理工大学博士学位论文,2002.
[8]袁曾任,人工神经元网络及其应用[M].北京:清华大学出版社,1992.
[9]焦李成.神经网络系统理论[M].西安:西安电子科技大学出版社,1990.
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心