Feedback-error-learning control with considering smoothness of unknown nonlinearities

Learning control of nonlinear systems by using neural networks has been widely studied. Among them the feedback-error-learning control proposed by Kawato et al. (1987), has been recognized to be an excellent learning method because of the fact that this method makes it possible to realize inverse models of unknown nonlinear controlled objects on neural networks. Since forward or inverse models of controlled objects, in general, are expressed by nonlinear smooth functions, taking account of the smoothness of forward or inverse models in the learning control would improve its performance considerably. In this paper the feedback error-learning control is extended so as to being able to treat the smoothness of unknown nonlinearity of controlled objects. The proposed method makes it possible to realize inverse models more accurately and to attain more precise control