Iterative Learning Control and Gaussian Process Regression for Hydraulic Cushion Control

Abstract

In this paper, we investigate on extending a feed-forward control scheme for the force control circuit of a hydraulic cushion with Gaussian Process nonlinear regression and Iterative Learning Control. Gaussian Processes allow the possibility of estimating the unknown proportional valve nonlinearities and provide uncertainty measurements of the predictions. However, the system must realize a high precision tracking control which is not achievable if any uncertainty remains in the estimation. Therefore, an extra feed-forward signal based on Iterative Learning Control is used to obtain a precise and fast force reference tracking performance. The design of the Iterative Learning Control is based on an inverted linearized model in which a fourth-order low-pass filter is included to attenuate the unknown valve dynamics. The low-pass filter is split up into two second-order low-pass filters, one of which is applied in the positive, the other in the negative, direction of time, resulting in zero-phase filtering. Simulation results show that Gaussian Process regression allows the possibility of using feed-forward control and that the force tracking performance is improved by introducing Iterative Learning Control.