Many industrial systems, including pick-and-place machines or batch processes, perform the same task over and over again. Often, the same disturbances act on the system each task. These disturbances can for instance stem from a periodic disturbance torque profile, from unbalance in an axis, or from unmodelled friction effects. Iterative learning control (ILC) and repetitive control (RC) techniques are smart algorithms that learn from the errors of earlier tasks. This leads to much better performance compared to well-known feedback and feedforward controllers. Most of these 'iterative learning control' (ILC) and 'repetitive control' (RC) techniques have been developed in the past two decades and many successful industrial applications have been reported.
The fully revised and extended (2012) course provides knowledge and understanding of:
This course is intended for engineers involved in control systems who want to gain more insight into the possibilities and implementations of learning control in an industrial setting. It is recommended that participants already have a Bachelor or Master education in electrical engineering, mechanical engineering, mechatronics, physics, or equivalent practical experience and must have some basic understanding of servo control. This course is particularly suitable for engineers having followed the course in 'Motion control tuning'.
The following topics are treated:
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