Iterative learning control for a non-minimum phase plant based on a reference shift algorithm

In order to improve the tracking performance of a non-minimum phase plant, a new method called the reference shift algorithm has been developed to overcome the problem of output lag encountered when using traditional feedback control combined with basic forms of iterative learning control. In the proposed algorithm a hybrid approach has been adopted in order to generate the next input signal. One learning loop addresses the system lag and another tackles the possibility of a large initial plant input commonly encountered when using basic iterative learning control algorithms. Simulations and experimental results have shown that there is a significant improvement in tracking performance when using this approach compared with that of other iterative learning control algorithms that have been implemented on the non-minimum phase experimental test facilit