Abstract
Norm optimal iterative learning control is an optimization-based approach where, in contrast to other such approaches, a term in the cost function is a measure of the difference between the control vectors used on successive trials. This approach has very well defined theoretical properties and is predicted to give superior performance relative to alternatives when applied to physical processes. In this paper, we describe on-going work towards the implementation of controllers designed using this approach to chain conveyor systems with the eventual aim of examining/verifying performance in 'real world' applications.
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