Rider control in bicycling is modeled by first adding the rider as a passive mechanism to the Whipple bicycle model. Next, for the rider control model a linear PID controller with and without delay is assumed, where the control inputs are the bicycle roll and steer angle with their higher derivatives, and the control output is the action-reaction steer torque applied by the rider at the handle bars. The experimental data is obtained from riding a bicycle on a narrow treadmill while applying an intermitted lateral perturbation by means of an impulse force applied at the seat post. The experiments are conducted in both the stable and the unstable forward speed range. After some filtering, a parametric control model is fitted to the data. Finally, the gains of this control model are used to identify the specific optimal control LQR cost function which the rider is using to control the bicycle on the treadmill at the various forward speeds.
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ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference
October 17–19, 2012
Fort Lauderdale, Florida, USA
Conference Sponsors:
- Dynamic Systems and Control Division
ISBN:
978-0-7918-4531-8
PROCEEDINGS PAPER
Rider Optimal Control Identification in Bicycling
A. L. Schwab,
A. L. Schwab
Delft University of Technology, Delft, The Netherlands
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P. D. L. de Lange,
P. D. L. de Lange
Delft University of Technology, Delft, The Netherlands
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Jason K. Moore
Jason K. Moore
University of California, Davis, Davis, CA
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A. L. Schwab
Delft University of Technology, Delft, The Netherlands
P. D. L. de Lange
Delft University of Technology, Delft, The Netherlands
Jason K. Moore
University of California, Davis, Davis, CA
Paper No:
DSCC2012-MOVIC2012-8587, pp. 201-206; 6 pages
Published Online:
September 17, 2013
Citation
Schwab, AL, de Lange, PDL, & Moore, JK. "Rider Optimal Control Identification in Bicycling." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 3: Renewable Energy Systems; Robotics; Robust Control; Single Track Vehicle Dynamics and Control; Stochastic Models, Control and Algorithms in Robotics; Structure Dynamics and Smart Structures; Surgical Robotics; Tire and Suspension Systems Modeling; Vehicle Dynamics and Control; Vibration and Energy; Vibration Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 201-206. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8587
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