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.
- Dynamic Systems and Control Division
Using an Adaptive High-Gain Extended Kalman Filter With a Car Efficiency Model
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Sebesta, KD, Boizot, N, Busvelle, E, & Sachau, J. "Using an Adaptive High-Gain Extended Kalman Filter With a Car Efficiency Model." Proceedings of the ASME 2010 Dynamic Systems and Control Conference. ASME 2010 Dynamic Systems and Control Conference, Volume 1. Cambridge, Massachusetts, USA. September 12–15, 2010. pp. 899-906. ASME. https://doi.org/10.1115/DSCC2010-4219
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