BELBIC-Sliding Mode Control of Robotic Manipulators With Uncertainties and Switching Constraints

This paper addresses the control problem for trajectory tracking of a class of robotic manipulators presenting uncertainties and switching constraints using a biomimetic approach. Uncertainties, system-inherent as well as environmental disturbances deteriorate the performance of the system. A change in constraints between the robot’s end-effector and the environment resulting in a switched nonlinear system, undermines the stable system performance. In this work, a robust adaptive controller combining sliding mode control and BELBIC (Brain Emotional Learning-Based Intelligent Control) is suggested to remediate the expected impacts on the overall system tracking performance and stability. The controller is based on an interplay of inputs relating to environmental information through error-signals of position and sliding surfaces and of emotional signals regulating the learning rate and adapting the future behaviour based on prior experiences. The proposed control algorithm is designed to be applicable to discontinuous freeform geometries. Its stability is proven theoretically and a simulation, performed on a two-link manipulator verifies its efficacy.