Simultaneous input and state estimation for nonlinear systems with applications to flow field estimation

• Published in: Automatica (Oxford) Vol. 49; no. 9; pp. 2805 - 2812
• Main Authors: Fang, Huazhen, de Callafon, Raymond A., Cortés, Jorge
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01-09-2013; Elsevier
• Subjects: Applied sciences; Bayesian analysis; Bayesian filtering; Computer science; control theory; systems; Control theory. Systems; Dynamical systems; Earth, ocean, space; Exact sciences and technology; External geophysics; Input estimation; Modelling and identification; Nonlinear dynamics; Nonlinear system; Nonlinearity; Optimization; Physics of the oceans; Position measurement; State estimation; Submersibles; Input estimation; Bayesian filtering; State estimation; Nonlinear system; Bayes estimation; Gauss Newton method; Non linear control; Non linear programming; Dynamical system; Non linear system; Filter; System identification; Submarine vehicle; Oceanology
• ISSN: 0005-1098; 1873-2836
• DOI: 10.1016/j.automatica.2013.05.010

This paper studies the problem of simultaneous input and state estimation (SISE) for nonlinear dynamical systems with and without direct input–output feedthrough. We take a Bayesian perspective to develop a sequential joint input and state estimation approach. Our scheme gives rise to a nonlinear Maximum a Posteriori optimization problem, which we solve using the classical Gauss–Newton method. The proposed approach generalizes a number of SISE methods presented in the literature. We illustrate the effectiveness of the proposed scheme for nonlinear systems with direct feedthrough in an oceanographic flow field estimation problem involving submersible drogues that measure position intermittently and acceleration continuously.