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This study addresses a robust iterative learning control (ILC) scheme for non-linear discrete-time systems in which both the trail lengths and the initial state shifts could be randomly variant in iteration domain. The proposed higher-order ILC law guarantees that as the iteration number goes to infinity, the ILC tracking errors at the desired output trail period are bounded in mathematical expectation,...
This paper presents a two-gain Iterative Learning Control (ILC) design for nonlinear discrete systems with time delays in state. The convergence of the proposed ILC rule with sufficient condition is derived based on a kind of two-dimensional (2-D) linear inequalities. The proposed ILC rule is able to guarantee convergence of the learning process against fixed iterative initial errors. An example is...
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