Stable nonlinear identification from noisy repeated experiments via convex optimization
Author(s)Tobenkin, Mark M.; Manchester, Ian R.; Megretski, Alexandre
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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.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the 2013 American Control Conference
Institute of Electrical and Electronics Engineers (IEEE)
Tobenkin, Mark M., Ian R. Manchester, and Alexandre Megretski. “Stable Nonlinear Identification from Noisy Repeated Experiments via Convex Optimization.” 2013 American Control Conference (June 2013).