Control Neuronal por Modelo Inverso de un Servosistema Usando Algoritmos de Aprendizaje Levenberg-Marquardt y Bayesiano

In this paper we present the experimental results of the neural network control of a servo-system in order to control its speed. The control strategy is implemented by using an inverse-model control based on Artificial Neural Networks (ANNs). The network training was performed using two learning algorithms: Levenberg-Marquardt and Bayesian regularization. We evaluate the generalization capability for each method according to both the correct operation of the controller to follow the reference signal, and the control efforts developed by the ANN-based controller.