Stable Nonlinear Identification From Noisy Repeated Experiments via Convex Optimization

Mark M. Tobenkin; Ian R. Manchester; Alexandre Megretski

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arXiv · 1303.4175

Stable Nonlinear Identification From Noisy Repeated Experiments via Convex Optimization

Mark M. Tobenkin·Ian R. Manchester·Alexandre Megretski

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Abstract

This paper introduces new techniques for using convex optimization to fit input-output data to a class of stable nonlinear dynamical models. We present an algorithm that guarantees consistent estimates of models in this class when a small set of repeated experiments with suitably independent measurement noise is available. Stability of the estimated models is guaranteed without any assumptions on the input-output data. We first present a convex optimization scheme for identifying stable state-space models from empirical moments. Next, we provide a method for using repeated experiments to remove the effect of noise on these moment and model estimates. The technique is demonstrated on a simple simulated example.

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BibTeX RIS

Mark M. Tobenkin, Ian R. Manchester, Alexandre Megretski. 2013-03-18. Stable Nonlinear Identification From Noisy Repeated Experiments via Convex Optimization. https://arxiv.org/abs/1303.4175

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