基于FEL的液压振动台加速度幅相控制
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摘要
目的针对液压振动台加速度正弦波波形复现、加速度扫频两类试验项目,利用反馈误差学习(FEL)控制策略解决传统三状态控制策略不能解决的跟踪精度低、鲁棒性差的问题.方法利用ADALINE神经网络、归一化LMS算法、结合正弦函数的特点,设计FEL控制策略中的前馈神经网络.结果对于正弦波和扫频信号参考输入,即使在阀控缸系统参数发生变化的情况下,所设计控制器仍然在幅值和相位上达到很高的跟踪精度.结论基于FEL思想设计的幅相控制器跟踪精度高,结构简单,运算量小,能满足对实时性要求很高的液压振动台控制系统的要求,也有利于基于DSPs的嵌入式实现.
For the two test items of hydraulic vibration generator system,acceleration sinusoidal signal waveform replication,acceleration sweep,Feedback-Error-Learning(FEL) control strategy is utilized to solve the weakness of tracking accuracy and robustness which traditional three variable control strategy can't do.Feedforward neural networks of FEL control strategy was designed,using ADALINE neural networks,normalized LMS,plus characteristics of sinusoidal function.For sinusoidal and sweep reference input,even in cases that valve-controlled cylinder system parameters changed,the designed controller still achieved high tracking accuracy in amplitude and in phase.Furthermore,it has the advantage of simple construct and smaller amount of computation,which can satisfy the demand of control system that requires higher real-time capability and DSPs solution.
For the two test items of hydraulic vibration generator system,acceleration sinusoidal signal waveform replication,acceleration sweep,Feedback-Error-Learning(FEL) control strategy is utilized to solve the weakness of tracking accuracy and robustness which traditional three variable control strategy can't do.Feedforward neural networks of FEL control strategy was designed,using ADALINE neural networks,normalized LMS,plus characteristics of sinusoidal function.For sinusoidal and sweep reference input,even in cases that valve-controlled cylinder system parameters changed,the designed controller still achieved high tracking accuracy in amplitude and in phase.Furthermore,it has the advantage of simple construct and smaller amount of computation,which can satisfy the demand of control system that requires higher real-time capability and DSPs solution.
引文
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[3]Zeng H,Sepehri N.On tracking control of cooperativehydraulic manipulators[J].International Journal ofControl,2007(80):454-469.
[4]韩俊伟,于丽明,赵慧,等.地震模拟振动台三状态控制的研究[J].哈尔滨工业大学学报,1999,31(3):21-23.
[5]韩俊伟.三向六自由度大型地震模拟振动台的研制[D].哈尔滨:哈尔滨工业大学机电学院,1996.
[6]Hiroaki G,Mitsuo K.Neural network control for aclosed-loop system using feedback-error-learning[J].Neural Networks,1993,6(7):933-946.
[7]何玉彬,王晓予,闫桂荣.神经网络在线学习模糊自适应控制及其应用[J].中国电机工程学报,2000,20(10):67-70.
[8]李洪人.液压控制系统[M].北京:国防工业出版社,1990.
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[10]Hagan MT,Demuth HB.Neural network design[M].Beijing:China Machine Press,2002.
[11]Haykin S.Adaptive filter theory[M].Beijing:Pub-lishing House of Electronics Industry,2002.
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