Abstract
AbstractThis paper addresses the problem of iterative learning control with well‐defined relative degree. The solution is a family of sampled‐data learning algorithms using lower‐order differentiations of the tracking error with the order less than the relative degree. A unified convergence condition for the family of learning algorithms is derived and is proved to be independent of the highest order of the differentiations. In the presence of initial condition errors, the system output is ensured to converge to the desired trajectory with a specified error bound at each sampling instant. The bound will reduce to zero whenever the bound on initial condition errors tends to zero. Numerical examples are provided to illustrate the tracking performance of the proposed learning algorithms. Copyright © 2004 John Wiley & Sons, Ltd.
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