On Analysis and Implementation of Iterative Learning Control

Sammanfattning: Many of the control systems used in factory production today are programmed to perform the same task repeatedly. In particular this is the case for industrial robots where the same motion is performed every time the same program is executed. An interesting observation, for the industrial robot, is that the error in the different iterations of the same exercise is highly repetitive.In the thesis Iterative Learning Control is applied to an industrial robot control system from ABB. Using Iterative Learning Control the tracking error on the motor side has been reduced without changing the internal structure or any parameters in the robot controller. The results from the experiments show that Iterative Learning Control can be used to successfully reduce the tracking error in an industrial robot control system.The implementation of the functions needed in the robot controller is described. By using a combination of already present functions in the system and software development the Iterative Learning Control method has been successfully applied to the commercial robot controller, S4C.The Iterative Learning Control method uses knowledge from previous exercises to improve the control in future executions of the same exercise. For the robot control case, this means remembering the error that was achieved in the previous iteration of the exercise and to change the input signal to the system based on this knowledge.A theory including analysis and synthesis for Iterative Learning Controlis provided, and a starting point for a more general theory on iterativesystems is given. A general discussion on how the Iterative Learning Control method can deal with repetitive and random disturbances is also included.Two of the given design algorithms are evaluated by experiments. The experiments show that the methods apply very well to the control of the ABB robot. The error reaches steady state levels, in fact the quantization level, already after 3 iterations with a model based synthesis approach.