基于RBF神经网络的迟滞非线性模型预测控制

压电执行器具有响应快、质量比大、刚度高等特点,在纳米技术领域得到了广泛的应用.迟滞特性作为一种固有的非线性特性,极大地影响了迟滞控制的性能.本文提出了一种非线性模型预测控制(NMPC)方法来解决压电执行器的位移跟踪问题.首先,利用RBF神经网络实现了压电执行器的外源输入非线性自回归移动平均(NARMAX)模型;其次...

Nonlinear model predictive control of hysteresis based on RBF neural network

Piezoelectric actuators have the characteristics of fast response, large mass ratio, high rigidity, etc., and have been widely used in the field of nanotechnology. As a kind of inherent nonlinear characteristic, hysteresis characteristic greatly affects the performance of hysteresis control. This paper proposes a nonlinear model predictive control (NMPC) method to solve the displacement tracking problem of piezoelectric actuators. First, the RBF neural network is used to realize the external input nonlinear autoregressive moving average (NARMAX) model of the piezoelectric actuator; secondly, the NMPC principle is used to transform the tracking control problem into an optimization problem, and then the gradient descent algorithm is used to solve it. In order to verify the effectiveness of the proposed modeling and control methods, MATLAB and COMSOL simulation experiments were carried out. The results show that the proposed RBF prediction model has satisfactory accuracy, the maximum absolute error between the expected displacement and the actual displacement obtained by the NMPC method tracking is 0.016um, and the average absolute error is 0.0121um, which has high accuracy.