Influence of Nonminimum Phase Zeros on the Performance of Optimal Continuous-Time Iterative Learning Control

Iterative learning control can be applied to systems that execute the same tracking task over a finite time duration. An execution is known as a trial, and once each is complete, the system resets to the starting location and the next trial begins. All previous trial information is available for use in constructing the control input for the next trial, and the basic idea is to improve tracking performance from trial-to-trial. This brief analyzes the effects of nonminimum phase zeros on the trial-to-trial error norm convergence of norm optimal iterative learning control, a commonly used algorithm, for differential linear systems with supporting experimental results from a test facility.