DocumentCode :
32091
Title :
Traction-Control-Oriented State Estimation for Motorcycles
Author :
Corno, Matteo ; Panzani, Giulio ; Savaresi, Sergio M.
Author_Institution :
Dipt. di Elettron. e Inf., Politec. di Milano, Milan, Italy
Volume :
21
Issue :
6
fYear :
2013
fDate :
Nov. 2013
Firstpage :
2400
Lastpage :
2407
Abstract :
This brief addresses two estimation problems relevant to traction control for motorcycles: longitudinal vehicle velocity estimation and wheelie (i.e., front wheel lifting off the ground during acceleration) detection. Two methods to estimate the vehicle body velocity are discussed and compared: a complementary filter and a Kalman filter. The Kalman filter reduces the noise affecting the estimate of the longitudinal vehicle velocity by an order of magnitude without introducing any phase lag. Furthermore, a wheelie detection algorithm is developed. The approach is based on the fault detection paradigm and detects wheelies in 70 ms. Both methods are computationally efficient and industrially viable. Track tests on an instrumented sport motorcycle are employed to illustrate and validate the methods.
Keywords :
Kalman filters; fault diagnosis; motorcycles; state estimation; traction; vehicle dynamics; Kalman filter; comple- mentary filter; fault detection paradigm; instrumented sport motorcycle; longitudinal vehicle velocity estimation; traction-control-oriented state estimation; vehicle body velocity; wheelie detection algorithm; Acceleration; Kalman filters; Motorcycles; Sensor fusion; Vehicle dynamics; Wheels; Motorcycle dynamics; sensor fusion; traction control (TC); wheel-slip estimation; wheelie detection;
fLanguage :
English
Journal_Title :
Control Systems Technology, IEEE Transactions on
Publisher :
ieee
ISSN :
1063-6536
Type :
jour
DOI :
10.1109/TCST.2013.2238539
Filename :
6422362
Link To Document :
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