Iterative Learning Control With Predictive Trial Information: Convergence, Robustness, and Experimental Verification

Iterative learning control (ILC) is a control design method for high-performance trajectory tracking. Most existing results achieve this by learning from information collected over the past executions of the task (named trials). This brief proposes a novel ILC design framework that updates the control input by learning not only from the past trials but also from the predicted future trials using knowledge of the plant model. It is shown that by including information from the predicted future trials, the designed ILC controller is less short sighted, and therefore better performance can be achieved. Analysis of the algorithm's properties reveals potentially substantial benefit in terms of convergence speed; the proposed algorithm also possesses distinct robustness features with respect to model uncertainty. Both numerical simulations and experimental results using a nonminimum phase test facility are provided to demonstrate the effectiveness of the proposed method.