A servo system tracking controller based on neural networks

Abstract

A novel neural network (NN) based trajectory tracking controller for a servo system that also incorporates a knowledge-based control scheme is proposed in this paper. A decentralized control scheme, which neither requires a priori knowledge of the plant nor learning of the system dynamics, is introduced to deactivate the coupled dynamics associated with certain systems like robotic manipulators. The NN is employed to classify the system input-output measurements into several patterns depending on the displacement and velocity deviations from the respective desired trajectories. A proportional plus derivative gain control action is determined from a look-up table corresponding to the classification from the NN. Furthermore, an integrator is applied to enhance system performance. Several PD gains are introduced in a staggered format relative to the magnitudes of the displacement and velocity tracking errors, resulting in a controller that is robust to both structured and unstructured uncertainties.