Stability analysis of brain emotional intelligent controller with application to electrically driven robot manipulators

Abstract

This study discusses stability analysis of the brain emotional learning-based intelligent controller (BELBIC) for model-free tracking control of a class of uncertain non-linear systems. Due to the unique adaptation laws for the controller gains, stability analysis of these controllers is a challenging task. This issue constructs the motivation of this study to undertake this task. Similar to direct adaptive fuzzy control, the BELBIC plays the role of a direct intelligent controller. Thus, the stability of the BELBIC can be guaranteed based on the stability analysis of direct adaptive fuzzy control. The main objective of this study is that the stability of the BELBIC in tracking control of a class of uncertain non-linear systems is guaranteed. Based on the proposed stability analysis and Barbalat's lemma, BELBIC can yield in the asymptotic convergence of the tracking error to zero. In comparison with the existing works in the literature, the proposed approach has fewer tuning parameters. Therefore, the developed approach has a simple structure. The simulation results on an articulated manipulator actuated by permanent magnet DC motors and comparison with other model-free approaches are presented to show the superiority of the proposed approach.