Title :
Stable nonlinear identification from noisy repeated experiments via convex optimization
Author :
Tobenkin, Mark M. ; Manchester, Ian R. ; Megretski, Alexandre
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Massachusetts Inst. of Technol., Cambridge, MA, USA
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.
Keywords :
convex programming; identification; nonlinear dynamical systems; stability; state-space methods; convex optimization scheme; input-output data; model estimation; moment estimation; noisy repeated experiments; stability; stable nonlinear dynamical model; stable nonlinear identification; stable state-space model; Asymptotic stability; Data models; Noise; Noise measurement; Stability analysis; Vectors; Zinc;
Conference_Titel :
American Control Conference (ACC), 2013
Conference_Location :
Washington, DC
Print_ISBN :
978-1-4799-0177-7
DOI :
10.1109/ACC.2013.6580441