Iterative Self-Improvement of Force Feedback Control in Contour Tracking

Abstract

A very general 3 level learning method is presented, aiming at self-improvement of the parameters of a force feedback controller in contour tracking tasks. It is assumed that no model is known a priori, neither of the robot nor of the contour to be tracked. The system identifies such a model, including information about its reliability. Model and estimated noise are used to generate optimal control actions for the sample trajectory. They are then used for estimation of the parameters of the controller. This controller then produces a new trajectory, which in turn may be optimized and trainedthereafter. Kalman filter techniques are applied in all adaptation levels involved. Learning is possible off-line or on-line. Model and controller may be based on linear difference equations or include nonlinear mappings as associative or tabular memories or neural networks. It is shown, that even for a linear controller substantial improvements can be attained, the more as no assumptions are needed about the bandwidth.