A State-Input Estimation Approach for Force Identification on an Automotive Suspension Component
Input force evaluation is always a crucial step for the adequate design of any kind of mechanical system. Direct measurements of input forces typically involve devices that are expensive, intrusive or difficult to calibrate. This is also the case for Road Load Data Acquisition (RLDA) testing campaigns, where the loads due to the road excitations acting on a vehicle are acquired. During RLDA testing campaigns, expensive measurement wheels are commonly used. An appealing alternative procedure, which consists of the use of less expensive sensors in combination with a numerical model of the system, is investigated. In order to combine experimental and simulation data, a coupled state-input estimation approach is used in the proposed procedure. In this approach a finite element model of the system provides simulated data, while common accelerometers and strain gauges provide experimental data. A Kalman filter is then used in order to perform the estimation. This paper presents the derivation of the filter equations that are necessary for the envisioned approach. A numerical example is then performed where the system-under-investigation is an automotive suspension component.
KeywordsInput estimation Virtual Sensing Automotive Kalman filter Multiple force estimation
The authors gratefully acknowledge the European Commission for its support of the Marie Sklodowska Curie program through the ITN ANTARES project (GA 606817).
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