Adaptive fuzzy control of a non-linear servo-drive: Theory and experimental results

Adaptive fuzzy control has been an active research area over the last decade and several stable adaptive fuzzy controllers have been proposed in the literature. Such controllers are generally based on feedback linearization and their parameters are updated by tracking-error-based adaptive laws, designed by Lyapunov synthesis. In this paper, different indirect adaptive schemes have been studied and compared by means of an experimental benchmark consisting of two coupled servo-drives. Parametric and structural changes are introduced to the controlled plant, in order to emphasize the advantages and limitations of the considered adaptive controllers. As the standard composite adaptation laws from the literature were found too sensitive to noise and unmodeled dynamics, a novel variant of the composite adaptation laws has been proposed, based on the filtered prediction error. Experimental results demonstrate that the proposed method improves the controller's tracking performance.