Abstract
Abstract The learning rates achievable in the digital iterative learning control of linear multi-variable plants are investigated. It is shown that the irregularity and stability characteristics of the plants under control impose severe constraints on the achievable learning rates. These results are not only significant in their own right but also strongly motivate the introduction of compensators to increase the learning rates achievable in irregular plants. These general results are illustrated by the presentation of numerical results for the iterative learning digital control of an uncompensated fourth-order completely irregular plant with two inputs and two outputs
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