Further results on the experimental evaluation of iterative learning control algorithms for non-minimum phase plants

This paper builds on previous work in which simple structure ILC algorithms were experimentally evaluated on a non-minimum phase plant. The phase-lead update law, the most promising of those implemented, is examined using an established performance criterion. The use of a forgetting factor is found to overcome the problem of instability, but at the expense of increased final error. This is verified through experimentation. The phase-lead algorithm is improved using additional phase-leads to remove the instability and improve convergence and final error. This technique is generalized to produce an optimization routine which leads to greatly improved results. The learning law utilizing the plant adjoint is found to fit naturally into this framework and practical results are presented to compare their respective performance. This algorithm, which requires a model of the plant, is reformulated into one which does not. Results are presented using this technique and practical guidelines are produced and tested to improve its performance. A simple method of increasing the learning at higher frequencies is proposed and practical limitations are addressed and verified experimentally. All the algorithms tested are compared with respect to their performance, practicality and robustness.