Research Of Iterative Learning Control For DGR-5A Robot

Iterative learning control,as a branch of the intelligent control, many researchers have paid their attentions to it because of its'simplicity and effectiveness. In recent years, it has developed quickly and been used widely. Iterative learning control is a technique for improving the transient response performance of system or processes that operate repetitively over a fixed time interval. It refines the next control input using the information such as current control input and error signals after each trial until the specified desired trajectory is followed to a high precision. The work of this paper is to research iterative learning control and it's application for DGR-5A robot.First, this paper is to find an effective analytical method, solving the kinematics and inverse kinematics of DGR-5A robot, the simulation of computer testifies it. Then discuss the basic theories of trajectory tracking and give the simulative results. And we also establish precise dynamic model.Then, basic knowledge about iterative learning control are introduced, include open-loop iterative learning control and closed-loop iterative learning control. The convergence conditions for them are discussed separately. And also discuss their application in the DGR-5A robot. Based that, gain coefficient variable iterative learning control and feed-back iterative learning control is presented. Simulation examples of DGR-5A robot show that The former can improve iterative learning speed and the latter can keep robustness when the parameter changed.