Data-driven iterative learning control for continuous-time systems

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In this project we aim at developing continuous-time iterative learning control design schemes to provide effective, accurate and applicable data-driven solutions to control complex systems. The project builds on the extensive research experience of the two proposed supervisors in iterative learning control, data-driven control, and applications of approximation theory to data-driven continuous-time control. The main thrust of the research will be theoretical, but we aim to target specific exemplary problems in power electronics (grid-tied inverters) or electrical machines.