A Sliding Mode Control Using Brain Limbic System Control Strategy for a Robotic Manipulator

Abstract

This paper presents a robust bio-inspired sliding mode control approach, designed to achieve a favourable tracking performance in a class of robotic manipulators with uncertainties. To this end, brain emotional learning-based intelligent control (BELBIC) is applied, to adaptively adjust the control input law in the sliding mode control. The combined form provides an adjustment of the control input law that effectively alleviates the chattering effects of the sliding mode control. Specifically, the online parameters computed from the parameter uncertainties and external disturbances help to improve the system robustness. The simulation results demonstrate that the proposed bio-inspired control strategy is very successful at tracking the given trajectories with less chattering as compared to both the conventional and fuzzy sling mode control schemes.