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- W3202093384 abstract "Formulating a control design for integer-valued inputs might be advantageous over, for example, rounding strategies based on a real-valued solution. However, the speed of convergence, computational cost, and stability are challenges to be addressed. This is especially true for optimisation-based approaches such as the norm-optimal iterative learning control (ILC). In this contribution, the convergence properties of integer-valued ILC are derived and compared against the known real-valued ILC. After an evaluation of two recently presented solutions, a new optimal set controller synthesis method for integer-valued norm-optimal ILC is presented. The approach guarantees monotonic convergence and ensures that each iteration of the ILC will require a deterministic computational effort to find the suboptimal solution, with an upper bound for the objective function of the optimisation. This deterministic computational effort makes the setup applicable for real-time implementation. Finally, an example using the introduced new approach is given to highlight its advantages." @default.
- W3202093384 created "2021-10-11" @default.
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- W3202093384 date "2021-09-29" @default.
- W3202093384 modified "2023-10-01" @default.
- W3202093384 title "Norm-optimal iterative learning control in an integer-valued control domain" @default.
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- W3202093384 doi "https://doi.org/10.1080/00207179.2021.1983213" @default.
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