Identification of Time-Varying Parameters of Hybrid Dynamical System Models and Its Application to Driving Behavior

Thomas WILHELEM  Hiroyuki OKUDA  Tatsuya SUZUKI  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E100-A   No.10   pp.2095-2105
Publication Date: 2017/10/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E100.A.2095
Type of Manuscript: PAPER
Category: Systems and Control
hybrid dynamical system models,  time-varying parameters identification,  particle filter,  driving behavior identification,  

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This paper presents a novel identification method for hybrid dynamical system models, where parameters have stochastic and time-varying characteristics. The proposed parameter identification scheme is based on a modified implementation of particle filtering, together with a time-smoothing technique. Parameters of the identified model are considered as time-varying random variables. Parameters are identified independently at each time step, using the Bayesian inference implemented as an iterative particle filtering method. Parameters time dynamics are smoothed using a distribution based moving average technique. Modes of the hybrid system model are handled independently, allowing any type of nonlinear piecewise model to be identified. The proposed identification scheme has low computation burden, and it can be implemented for online use. Effectiveness of the scheme is verified by numerical experiments, and an application of the method is proposed: analysis of driving behavior through identified time-varying parameters.