Using an Adaptive High-Gain Extended Kalman Filter With a Car Efficiency Model

Abstract

The authors apply the Adaptive High-Gain Extended Kalman Filter (AEKF) to the problem of estimating engine efficiency with data gathered from normal driving. The AEKF is an extension of the traditional Kalman Filter that allows the filter to be reactive to perturbations without sacrificing noise filtering. An observability normal form of the engine efficiency model is developed for the AEKF. The continuous-discrete AEKF is presented along with strategies for dealing with asynchronous data. Empiric test results are presented and contrasted with EKF-derived results.

On the stability of a differential Riccati equation for continuous-discrete observers

“…Continuous-discrete systems are used in order to model processes having continuous state dynamics and a discrete measurement procedure, which reflects many practical situations. This approach is meaningful when the measurements sampling time is not constant, or when state estimates are needed between updates (Ahmed-Ali, Postoyan, & Lamnabhi-Lagarrigue, 2009;Astorga, Othman, Othman, Hammouri, & McKenna, 2002;Andrieu, Nadri, Serres, & Vivalda, 2013;Bakir, Othman, Fevotte, & Hammouri, 2006;Bristeau, Dorveaux, Vissière, & Petit, 2010;Dymkov, Gaishun, Rogers, Galkowski, & Owens, 2004;Jazwinski, 1970;Karafyllis & Kravaris, 2009;Nadri & Hammouri, 2003;Nicolao & Strada, 1998;Sebesta & Boizot, 2014;Sebesta, Boizot, Busvelle, & Sachau, 2010;Song & Shin, 2010).…”