基于径向基神经网络的压电作动器建模与控制

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.

Modeling and control of piezoelectric actuator based on radial basis function neural network

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 modeldescribes 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 isutilized for position tracking control, and the relative error less than 2:98%. Validity of the control strategy is proved byexperimental results.