Iterative learning control of a flexible manipultor

Abstract

Control systems have played an important role in the development of modern life and technology. In many industrial robot applications, they are programmed to do the same task repeatedly. One of the intelligent control methodologies is iterative learning control (ILC). In this thesis, ILC method for a single-link flexible manipulator is proposed to achieve precise tracking control and end-point vibration suppression of the system. The learning is done in a feedback signal and the learning law updates the feed forward input from the error of the previous trial. Three learning algorithms are used: P-type, PI-type and PD-type. The dynamic model of the flexible manipulator is derived using the finite element method. Simulation results of the response of the manipulator with the controllers are presented in the time and frequency domains. The performance of the ILC scheme is assessed in terms of input tracking and level of vibration reduction. It is demonstrated that PD-type ILC provides the highest performance for control of a flexible manipulator.