Exploiting Cyclic Angle-Dependency in a Kalman Filter-Based Torque Estimation on a Mechatronic Drivetrain

Torsional vibrations play a critical role in the design and operation of a mechanical or mechatronic drivetrain due to their impact on lifetime, performance, and cost. A magnetic spring allows one to reduce these vibrations and improve the actuator performance yet introduces additional challenges on...

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Bibliographic Details
Published in:Actuators 2022-01, Vol.11 (2), p.35
Main Authors: Van der Veken, Thijs, Croes, Jan, Kirchner, Matteo, Baake, Jonathan, Desmet, Wim, Naets, Frank
Format: Article
Language:English
Subjects:
Accelerometers
Actuators
Coders
Equilibrium
Extended Kalman filter
Friction
Inertia
input estimation
Kalman filter
Load
magnetic spring
mechatronics
Parameter estimation
Parameter identification
Powertrain
Random walk
Torque
Variance
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Summary:Torsional vibrations play a critical role in the design and operation of a mechanical or mechatronic drivetrain due to their impact on lifetime, performance, and cost. A magnetic spring allows one to reduce these vibrations and improve the actuator performance yet introduces additional challenges on the identification. As a direct torque measurement is generally not favourable because of its intrusive nature, this paper proposes a nonintrusive approach to identify torsional load profiles. The approach combines a physics-based lumped parameter model of the torsional dynamics of the drivetrain with measurements coming from a motor encoder and two MEMS accelerometers in a combined state/input estimation, using an augmented extended Kalman filter (A-EKF). In order to allow a generic magnetic spring torque estimation, a random walk input model is used, where additionally the angle-dependent behaviour is exploited by constructing an angle-dependent estimate and variance map. Experimental validation leads to a significant reduction in bias in the load torque estimation for this approach, compared to conventional estimators. Moreover, this newly proposed approach significantly reduces the variance on the estimated states by exploiting the angle dependency. The proposed approach provides knowledge of the torsional vibrations in a nonintrusive way, without the need for an extensive magnetic spring torque identification. Further, the approach is applicable on any drivetrain with angle-dependent input torques.
ISSN:2076-0825
2076-0825
DOI:10.3390/act11020035