Performance Optimisation of Learning Feed Forward Control

Abstract

The performance of sub-optimal feedback controllers can be improved in several ways. In this paper a learning control strategy is considered. The learning control system consists of the feedback and a feed forward controller. The feed forward controller is implemented as a neural network that is trained during control in order to minimise the tracking error. The type of neural network is a single layer network, in which B-spline basis functions are used to store the input-output mapping. The distribution of the B-splines on the domain of the input(s) is of influence on the performance of the learning controller. Until recently, the basis functions were distributed by rule of thumb. In this paper fuzzy clustering techniques are used to obtain the distribution in a systematic way. In experiments the learning controller has been used to control a linear motor. Also when the B-splines are chosen by rule of thumb, the learning controller was able to improve the performance of the feedback controller considerably. The tracking error could be reduced further by determining the distribution of the basis functions using fuzzy clustering.