Iterative learning control: a method that can improve product quality and productivity in manufacturing

Abstract

As the world moves toward a fully global economy, manufacturers need to search constantly for ways to secure or maintain a competitive advantage. Iterative learning control (ILC) and its sister field, repetitive control (RC), offer software methods to improve the performance of motion control systems, without requiring purchase of expensive high-precision equipment. And these can often translate into improved product quality, or into faster operations with resulting increases in productivity. When a manufacturing operation involves following a desired trajectory, classical feedback control systems repeatedly produce a trajectory different than commanded. ILC, iteratively adjusts the command aiming for zero tracking error in hardware operation. RC instead aims for zero error following periodic commands or in the presence of periodic disturbances. Improving the precision of motion control systems can translate directly into higher product quality. But it can also improve productivity by allowing higher speed operation while maintaining the original accuracy level. Experiments on a robot at NASA Langley Research Center improved the tracking accuracy by a factor of 1000 in 12 iterations. This article is a tutorial presenting a brief introduction to the concepts underlying these relatively new control methods, together with some illustrations from the author's experience, of their potential for improvement in manufacturing operations.

Keywords:

• iterative learning control
• repetitive control
• precision motion control
• high speed motion