Abstract
This paper addresses the problem of stabilizing an electric cargo bike. For most control objectives, it suffices to consider a cargo bike as a two-wheeler. However, in addition to the challenges posed to the control of traditional two-wheelers, electric cargo bikes also have the issue of the cargo load, which can significantly influence the driving behaviour. Hence, detection and estimation of the mass, position and inertial properties of the cargo load become important. Here, a Kalman filter based algorithm which estimates these parameters online is presented. For the estimation, measurements of the force exerted by the load are recorded using force sensors installed under the load. Along with these, roll angle and roll acceleration are also measured. The estimated values are then used by an adaptive model predictive controller (MPC) to adjust the model-parameters and stabilize a cargo bike while following a set trajectory.
Zusammenfassung
Dieser Beitrag behandelt die Stabilisierung eines elektrischen Lastenrads. Für die meisten Regelungsaufgaben ist es ausreichend, ein Lastenrad als gewöhnliches Zweirad anzunehmen. Zusätzlich zu den Herausforderungen einer Regelung klassischer Zweiräder sind elektrische Lastenräder jedoch dem Einfluss der Last ausgesetzt, die das Fahrverhalten maßgeblich beeinflussen kann. Daher ist die Berücksichtigung und Schätzung der Masse, Position und Trägheitseigenschaften der Last wichtig. Hierzu wird ein auf einem Kalman-Filter basierender Algorithmus vorgestellt, der diese Parameter online schätzt. Für diese Schätzung werden Kraftmessungen von Sensoren aufgenommen, die unter der Last montiert sind, sowie der Rollwinkel und die Rollwinkelbeschleunigung gemessen. Die Schätzergebnisse werden anschließend in einer adaptiven modellprädiktiven Regelung (MPC) zur Anpassung der Modellparameter verwendet, um ein Lastenrad, welches einer Solltrajektorie folgt, zu stabilisieren.
Award Identifier / Grant number: AZ 3-4332.62-MRM/2
Funding statement: We gratefully acknowledge the funding by the State Ministry of Economic Affairs Baden-Württemberg for this research within the project ZEC-Bike (AZ 3-4332.62-MRM/2).
About the authors
M. Sc. Suvrath Pai is a researcher at the Institute of Measurement, Control, and Microtechnology at Ulm University, working towards his PhD degree in the field of stability and control of electric bikes.
Benedikt Neuberger is a Masters student at the Institute of Measurement, Control, and Microtechnology at Ulm University, working on his master thesis in the field of cargo bikes stability.
Dr.-Ing. Michael Buchholz is a Research Group Leader and Lecturer („Akademischer Oberrat“) at the Institute of Measurement, Control, and Microtechnology at Ulm University. His main research interests comprise connected and automated driving, system identification and diagnosis, electric mobility, and mechatronical systems.
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