High performance trajectory control using a neural network cross-coupling gain scheduler

Cross-coupling control is an accepted methodology for improving contouring performance in multiaxial motion systems where axis interaction exists. This paper describes a new approach based on the use of neural networks for scheduling optimal cross-coupling gains for linear contours as the angle subtended with the x axis varies. The procedure for obtaining the optimal cross-coupling gains involves finding a minimum in a measured performance index. The experimental results for a biaxial system show that the proposed approach reduces contouring errors at test angles as compared to conventional uncoupled control of the axes. Measured performance indices are compared with and without cross-coupling at representative-angle to indicate the performance improvements that can be obtained with this approach