A study of bicycle dynamics via system identification approaches

Abstract

This study investigates bicycle dynamic properties by using system identification approaches. The non-linear bicycle model with configuration parameters from a previously developed benchmark model is studied. The roll angle of the bicycle is controlled at different speeds to generate input–output data including steering torque, roll, and steering angles. The collected data are then used to identify the one-input two-output linear model by a prediction-error identification method using parameterization in canonical state-space form derived as Whipple's model. Simulations are used to verify the accuracy of the generated linear model. Numerous properties for various speed ranges are discussed from the pole and zero locations of the identified linear model. The system stability, limit-cycle phase portraits of the roll and steering angles, and the non-minimum phase property of the non-linear system are further investigated and compared with the corresponding linearized results from previous studies in the literature.

Keywords:

• bicycle dynamics
• bicycle model
• system identification
• pole location
• limit cycle

Acknowledgment

The authors thank the National Science Council of the Republic of China, Taiwan, for financially supporting this study under the project no. NSC 96-2221-E-212-027.