A New Iterative Learning Controller Using Variable Structure Fourier Neural Network

A new iterative learning control approach based on Fourier neural network (FNN) is presented for the tracking control of a class of nonlinear systems with deterministic uncertainties. The proposed controller consists of two loops. The inner loop is a feedback control action that decreases system variability and reduces the influence of random disturbances. The outer loop is an FNN-based learning controller that generates the system input to suppress the error caused by system nonlinearities and deterministic uncertainties. The FNN employs orthogonal complex Fourier exponentials as its activation functions. Therefore, it is essentially a frequency-domain method that converts the tracking problem in the time domain into a number of regulation problems in the frequency domain. Through a novel phase compensation technique, this model-free method makes it possible to use higher-frequency components in the FNN to improve the tracking performance. In addition, the structure of the FNN can be reconfigured according to the system output information to make the learning more efficient and increase the convergent speed of the tracking error. Experiments on both a commercial gear box and a belt-driven positioning table are conducted to show the effectiveness of the proposed controller.