DocumentCode :
110738
Title :
A Smoothed GMS Friction Model Suited for Gradient-Based Friction State and Parameter Estimation
Author :
Boegli, Max ; De Laet, Tinne ; De Schutter, Joris ; Swevers, Jan
Author_Institution :
Dept. of Mech. Eng., Katholieke Univ. Leuven, Leuven, Belgium
Volume :
19
Issue :
5
fYear :
2014
fDate :
Oct. 2014
Firstpage :
1593
Lastpage :
1602
Abstract :
This paper presents a model that closely approximates the generalized Maxwell-Slip (GMS) model, a multistate friction model known to describe all essential friction characteristics in presliding and sliding motion. In contrast to the GMS model, which consists of a switching structure to accommodate for its hybrid nature, the new model, referred to as the smoothed GMS (S-GMS) model, consists of an analytic set of differential equations. Such a model is well suited for gradient-based state and parameter estimation, as in the extended Kalman filter (EKF) or in moving horizon estimation. Similar to the GMS model, the S-GMS model is a multistate model that also describes all essential friction characteristics. Moreover, the S-GMS model description includes the single-state LuGre model and Elastoplastic model as special cases. This paper also discusses the implementation of the EKF estimator for the S-GMS friction model and compares its performance to the LuGre model in joint state and parameter estimation.
Keywords :
Kalman filters; differential equations; elastoplasticity; nonlinear filters; nonlinear systems; parameter estimation; sliding friction; state estimation; EKF estimator; S-GMS friction model; differential equations; elastoplastic model; extended Kalman filter; friction characteristics; generalized Maxwell-Slip model; gradient-based friction state; joint state estimation; moving horizon estimation; multistate friction model; parameter estimation; presliding motion; single-state LuGre model; smoothed GMS friction model; switching structure; Analytical models; Force; Friction; Mathematical model; Parameter estimation; Smoothing methods; Vectors; Friction; Kalman filter; state and parameter estimation;
fLanguage :
English
Journal_Title :
Mechatronics, IEEE/ASME Transactions on
Publisher :
ieee
ISSN :
1083-4435
Type :
jour
DOI :
10.1109/TMECH.2013.2288944
Filename :
6675071
Link To Document :
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