Abstract
This paper investigates the iterative learning control for continuous-time multi-agent formation systems to realize the desired formation, where the trial lengths could be randomly varying at each iteration. To be specific, an ILC (iterative learning control) protocol with an iteratively moving average operator is established for multiple nonlinear agents with switching topologies, where a modified formation tracking error is defined and the control information from several previous trials is used to deal with the varying trail lengths. The convergence conditions are derived by using a redefied λ-norm with mathematical expectation for both the zero and varying initial shift cases, which ensure that the formation performance can still be maintained during the whole motion process when the actual trial length is greater or less than the desired one. In the end, simulation results illustrate the effectiveness of the proposed method.
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