Parameter estimation for Jump Markov Linear Systems. (January 2022)

• Record Type: Journal Article
• Authors: Balenzuela, Mark P.; Wills, Adrian G.; Renton, Christopher; Ninness, Brett
• Is Part Of: Automatica. Volume 135(2022)
• Publication Date: 2022-01
• Subjects: Jump Markov systems -- Nonlinear state estimation -- Optimisation -- Expectation maximisation -- Identification; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2021.109949
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: Jump Markov linear systems (JMLS) are a useful model class for capturing abrupt changes in system behaviour that are temporally random, such as when a fault occurs. In many situations, accurate knowledge of the model is not readily available and can be difficult to obtain based on first principles. This paper presents a method for learning parameter values of this model class based on available input–output data using the maximum-likelihood framework. In particular, the expectation–maximisation method is detailed for this model class with attention given to a deterministic and numerically stable implementation. The presented algorithm is compared to state-of-the-art methods on several simulation examples with favourable results.