A comparison of statistical learning approaches for engine torque estimation
Identifieur interne : 000A14 ( France/Analysis ); précédent : 000A13; suivant : 000A15A comparison of statistical learning approaches for engine torque estimation
Auteurs : A. Rakotomamonjy [France] ; Rodolphe Le Riche [France] ; David Gualandris [France] ; Zaid Harchaoui [France]Source :
- Control Engineering Practice [ 0967-0661 ] ; 2008.
Abstract
Engine torque estimation has important applications in the automotive industry: for example, automatically setting gears, optimizing engine perfor- mance, reducing emissions and designing drivelines. A methodology is described for the on-line calculation of torque values from the gear, the accelerator pedal position and the engine rotational speed. It is based on the availability of input-torque experimental signals that are pre- processed (resampled, filtered and segmented) and then learned by a statistical machine-learning method. Four methods, spanning the main learning principles, are reviewed in a uni- fied framework and compared using the torque estimation problem: linear least squares, linear and non-linear neural networks and support vector machines. It is found that a non-linear model structure is necessary for accurate torque estimation. The most efficient torque model built is a non-linear neural net- work that achieves about 2% test normalized mean square error in nominal conditions.
Url:
DOI: 10.1016/j.conengprac.2007.03.009
Affiliations:
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A methodology is described for the on-line calculation of torque values from the gear, the accelerator pedal position and the engine rotational speed. It is based on the availability of input-torque experimental signals that are pre- processed (resampled, filtered and segmented) and then learned by a statistical machine-learning method. Four methods, spanning the main learning principles, are reviewed in a uni- fied framework and compared using the torque estimation problem: linear least squares, linear and non-linear neural networks and support vector machines. It is found that a non-linear model structure is necessary for accurate torque estimation. The most efficient torque model built is a non-linear neural net- work that achieves about 2% test normalized mean square error in nominal conditions.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Haute-Normandie</li><li>Région Normandie</li></region><settlement><li>Le Havre</li><li>Rouen</li></settlement><orgName><li>Université de Rouen</li><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Région Normandie"><name sortKey="Rakotomamonjy, A" sort="Rakotomamonjy, A" uniqKey="Rakotomamonjy A" first="A." last="Rakotomamonjy">A. Rakotomamonjy</name></region><name sortKey="Gualandris, David" sort="Gualandris, David" uniqKey="Gualandris D" first="David" last="Gualandris">David Gualandris</name><name sortKey="Harchaoui, Zaid" sort="Harchaoui, Zaid" uniqKey="Harchaoui Z" first="Zaid" last="Harchaoui">Zaid Harchaoui</name><name sortKey="Le Riche, Rodolphe" sort="Le Riche, Rodolphe" uniqKey="Le Riche R" first="Rodolphe" last="Le Riche">Rodolphe Le Riche</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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