Identification of mixed linear/nonlinear state-space models

The primary contribution of this paper is an algorithm capable of identifying parameters in certain mixed linear/nonlinear state-space models, containing conditionally linear Gaussian substructures. More specifically, we employ the standard maximum likelihood framework and derive an expectation maximization type algorithm. This involves a nonlinear smoothing problem for the state variables, which for the conditionally linear Gaussian system can be efficiently solved using a so called Rao-Blackwellized particle smoother (RBPS). As a secondary contribution of this paper we extend an existing RBPS to be able to handle the fully interconnected model under study.

• Type: Journal
• Year: 2010
• Where: CDC
• Authors: Fredrik Lindsten, Thomas B. Schön
• Keywords: CDC 2010, Control Systems, Linear Gaussian Substructures, Maximization Type Algorithm, Maximum Likelihood Framework