Stable Nonlinear Identification From Noisy Repeated Experiments via Convex Optimization
Optimization and Control
2013-03-19 v1 Systems and Control
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.
Cite
@article{arxiv.1303.4175,
title = {Stable Nonlinear Identification From Noisy Repeated Experiments via Convex Optimization},
author = {Mark M. Tobenkin and Ian R. Manchester and Alexandre Megretski},
journal= {arXiv preprint arXiv:1303.4175},
year = {2013}
}