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• W2561256395 abstract "Iterative learning control (ILC) of continuous-time nonlinear plants with periodic sampled-data inputs is considered via an extremum seeking approach. ILC is performed without exploiting knowledge about any plant model, whereby the input signal is constructed recursively so that the corresponding plant output tracks a prescribed reference trajectory as closely as possible on a finite horizon. The ILC is formulated in terms of a non-model-based extremum seeking control problem, to which local optimisation methods such as gradient descent and Newton are applicable. Sufficient conditions on convergence to a neighbourhood of the reference trajectory are given." @default.
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• W2561256395 date "2016-01-01" @default.
• W2561256395 modified "2023-09-27" @default.
• W2561256395 title "An extremum seeking approach to sampled-data iterative learning control of continuous-time nonlinear systems**This research was supported in part by the Institute for Mathematics and its Applications with funds provided by the National Science Foundation and the Australian Research Council." @default.
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• W2561256395 doi "https://doi.org/10.1016/j.ifacol.2016.10.292" @default.
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