Inner-Learning Mechanism Based Control Scheme for Manipulator with Multitasking and Changing Load

Abstract

With the rapid development of robot technology and its application, manipulators may face complex tasks and dynamic environments in the coming future, which leads to two challenges of control: multitasking and changing load. In this paper, a novel multicontroller strategy is presented to meet such challenges. The presented controller is composed of three parts: subcontrollers, inner-learning mechanism, and switching rules. Each subcontroller is designed with self-learning skills to fit the changing load under a special task. When a new task comes, switching rule reselects the most suitable subcontroller as the working controller to handle current task instead of the older one. Inner-learning mechanism makes the subcontrollers learn from the working controller when load changes so that the switching action causes smaller tracking error than the traditional switch controller. The results of the simulation experiments on two-degree manipulator show the proposed method effect.