%0 Journal Article
%T 基于径向基神经网络的压电作动器建模与控制<br>Modeling and control of piezoelectric actuator based on radial basis function neural network
%A 范家华
%A 马磊
%A 周攀
%A 刘佳彬
%A 周克敏
%J 控制理论与应用
%D 2016
%R 10.7641/CTA.2016.50940
%X 针对压电作动器(piezoelectric actuator, PEA)的率相关迟滞非线性特性, 构建了Hammerstein模型对压电作 动器建模. 采用径向基(radial basis function, RBF)神经网络模型表征迟滞非线性, 利用自回归历遍模型(auto-regressive exogenous, ARX)表征频率的影响, 并对模型参数进行了辨识. 此模型可以在信号频率在1  300 Hz范围内时, 较好地描述压电作动器的迟滞特性, 建模相对误差为1:99% 4:08%. 采用RBF神经网络前馈逆补偿控制, 结合PI反 馈的复合控制策略实现跟踪控制, 控制误差小于2:98%, 证明了控制策略的有效性.<br>For the rate-dependent hysteresis nonlinearity of piezoelectric actuators, a Hammerstein model is established. Using a radial-basis-function (RBF) neural network to represent the hysteresis nonlinearity, an auto-regressive exogenous (ARX) model to represent the impact of frequency, and parameter identification is also accomplished. The proposed model describes the hysteresis characteristics of frequency ranged from 1 to 300 Hz of the signals, and the relative error is 1:99%  4:08%. A compound control strategy with RBF neural network feedforward inverse compensation and PI feedback is utilized for position tracking control, and the relative error less than 2:98%. Validity of the control strategy is proved by experimental results.
%K 率相关 迟滞 RBF神经网络 压电作动器 Hammerstein模型&lt
%K br&gt
%K rate-dependent hysteresis RBF neural network piezoelectric actuator Hammerstein model
%U http://jcta.alljournals.ac.cn/cta_cn/ch/reader/view_abstract.aspx?file_no=CCTA150940&flag=1