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Communication Dans Un Congrès Année : 2015

Adaptive Estimation of Vehicle Dynamics Through RLS and Kalman Filter Approaches

Résumé

—This article presents a new methodology for estimation of vehicle's vertical forces in order to enhance road safety. Direct measurement of vertical forces requires a complex and expensive experimental setup , which is not acceptable for ordinary passenger cars. The main contribution of this article is providing a reliable estimator of vertical tire forces by using currently available low-cost sensors. The first advantage of the proposed method is that we modified the vehicle model to take into account the roll and pitch dynamics, which makes our estimator stay robust during sharp turning or at inclined road. The other advantage is that we proposed a process to identify the vehicle parameters, instead of regarding them as known constants. This could enable our estimator to stay reliable even when the parameters are wrongly configured. The parameter identification process is based on recursive least squares (RLS) algorithm. The state observers are based on Kalman filter. The estimation process is applied and compared to real experimental data obtained in real conditions. Experimental results validate and prove the feasibility of this approach.
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Dates et versions

hal-01243207 , version 1 (14-12-2015)

Identifiants

Citer

Kun Jiang, Alessandro Correa Victorino, Ali Charara. Adaptive Estimation of Vehicle Dynamics Through RLS and Kalman Filter Approaches. 18th IEEE International Conference on Intelligent Transportation Systems (ITSC 2015), Sep 2015, Canary Islands, Spain. pp.1741-1746, ⟨10.1109/ITSC.2015.283⟩. ⟨hal-01243207⟩
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