Nonlinear system identification in Sobolev spaces
نویسندگان
چکیده
We consider the problem of approximating an unknown function from experimental data, while at same time its derivatives. Solving this is useful, for instance, in context nonlinear system identification, obtaining models that are more accurate and reliable than traditional ones based on plain approximation. Indeed, identified by accounting derivatives can provide improved performance several endeavours, such as multi-step prediction, simulation, Nonlinear Model Predictive Control, control design general. In paper, we propose a novel approach convex optimisation, allowing us to solve aforementioned identification problem. develop optimality analysis, showing derived using enjoy suitable properties Sobolev spaces. The analysis also leads derivation tight uncertainty bounds demonstrate effectiveness with three numerical examples, concerned univariate prediction Chua chaotic circuit, inverted pendulum.
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ژورنال
عنوان ژورنال: International Journal of Control
سال: 2022
ISSN: ['0020-7179', '1366-5820']
DOI: https://doi.org/10.1080/00207179.2022.2058617