Optimum Iterative Learning Control Design Using Imperialist Competitive Algorithm

This paper deals with the robust iterative learning control (ILC) design for uncertain single input-single output (SISO) linear time invariant (LTI) systems. In many industrial robot applications it is a fact that the robot is programmed to do the same task repeatedly. By observing the control error in the different iterations of thesame task it becomes clear that it is actually highly repetitive. The ILC allows to iteratively compensate for and,hence, remove this repetitive error. In this study different aspects of iterative learning control are covered. Although stability is the most important in practice the design aspect is also highlighted. Several design schemesfor iterative learning control methods are presented, including first order as well as second order iterativelearning control and parameter tuning. The main features of the design are that: The control signal is continuousand the coefficient of controller is optimized. Therefore it is chattering-free compared with the robust ILC using classical first-order sliding mode technique. In the proposed system, free parameters of controller have vital role in the performance of system. Therefore we suggest Imperialist Competitive Algorithm (ICA) for finding the best value of these parameters. The proposed system is tested on robotic manipulator problem and simulation results show that the recommended system has high accuracy.

Keywords:

Optimization , ICA , Free parameters , ILC , Nature base algorithm