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


Iterative learning management (ILC) is a management style method for prime-performance trajectory tracking. Most existing results achieve this by learning from data collected over the past executions of the task (named trials). This transient proposes a unique ILC design framework that updates the management input by learning not only from the past trials but additionally from the anticipated future trials using information of the plant model. It's shown that by together with information from the predicted future trials, the designed ILC controller is less short sighted, and therefore better performance will be achieved. Analysis of the algorithm’s properties reveals doubtless substantial profit in terms of convergence speed; the proposed algorithm also possesses distinct robustness features with respect to model uncertainty. Each numerical simulations and experimental results using a nonminimum section take a look at facility are provided to demonstrate the effectiveness of the proposed methodology.

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