基于径向基神经网络的压电作动器建模与控制
Modeling and control of piezoelectric actuator based on radial basis function neural network

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.
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.