Accurate contour control of mechatronic servo systems using Gaussian networks

This paper presents a method of contour control of mechatronic servo systems by using neural networks. The neural network learns the inverse dynamics of the mechatronic servo system. The input data for the mechatronic servo systems are modified from objective trajectories by using the neural network. The Gaussian network is adopted to construct the inverse dynamics of the mechatronic servo system because the Gaussian function is well defined, and its structure and initial parameters can be systematically selected such that the initial network approximates the inverse dynamics of the mechatronic servo system. The actual input/output data of the mechatronic servo system are used for the learning of the Gaussian network. Effectiveness of the proposed method is assured by experimental results of contour control of an X-Y table