Development of command-based iterative learning control algorithm with consideration of friction, disturbance, and noise effects

Abstract

In this brief, a command-based iterative learning control (ILC) architecture is proposed to compensate for friction effect and to reduce tracking error caused by servo lag. In contrast to a feedback-feedforward control structure, the proposed methodology utilizes the learning algorithm that updates the input commands based on the tracking errors from the previous machining process. The effects of noise accumulations from each learning process of the ILC are analyzed by formulating the equivalent error dynamic and updated command equations, and the P-type ILC with a zero-phase filter is applied to alleviate noise and disturbance effects. It is shown that, for tracking a circle, the quadrant protrusions caused by friction can be reduced substantially by the updated command containing a concave shape located at the crossing of the zero velocity. Finally, analytical simulation and experimental results demonstrate that the command-based ILC algorithm can enhance the tracking performance significantly.