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Study of Rider Model for Motorcycle Racing Simulation
Published January 24, 2020 by Society of Automotive Engineers of Japan in Japan
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Various rider models have been proposed that provide control inputs for the simulation of motorcycle dynamics.
However, those models are mostly used to simulate production motorcycles, so they assume that all motions are in the linear region such as those in a constant radius turn. As such, their performance is insufficient for simulating racing motorcycles that experience quick acceleration and braking.
Therefore, this study proposes a new rider model for racing simulation that incorporates Nonlinear Model Predictive Control. In developing this model, it was built on the premise that it can cope with running conditions that lose contact with the front wheels or rear wheels so-called "endo" and "wheelie", which often occur during running with large acceleration or deceleration assuming a race. For the control inputs to the vehicle, we incorporated the lateral shift of the rider's center of gravity in addition to the normally used inputs such as the steering angle, throttle position, and braking force.
We compared the performance of the new model with that of the conventional model under constant radius cornering and straight braking, as well as complex braking and acceleration in a single (hairpin) corner that represented a racing run.
The results showed that the new rider model outperformed the conventional model, especially in the wider range of running speed usable for a simulation. In addition, we compared the simulation results for complex braking and acceleration in a single hairpin corner produced by the new model with data from an actual race and verified that the new model was able to accurately simulate the run of actual MotoGP riders.
CitationNishimura, M., Tezuka, Y., Picotti, E., Bruschetta, M. et al., "Study of Rider Model for Motorcycle Racing Simulation," SAE Technical Paper 2019-32-0572, 2020.
Data Sets - Support Documents
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- T. Katayama A. Aoki & T. Nishimi Control Behavior of Motorcycle Riders. Vehicle System Dynamics International Journal of Vehicle Mechanics and Mobility 17 1988 4 211 229
- Yoshitaka Tezuka , Tsutomu Tamashima , Wasaku Hosoda , Shunichi Miyagishi , Toyokazu Nakamura , Hiroto Yoshiki Motorcycle Dynamic Simulation Model Incorporating Actual Rider Behavior Data Honda R&D Technical Review 19 1 2007
- R.S. Sharp The Stability and Control of Motorcycles Journal of Mechanical Engineering Science. 2007 13 5 316 329
- Alessandro Saccon , John Hauser , Ruggero Frezza Control of a Bicycle Using Model Predictive Control Strategy IFAC Nonlinear Control Systems, Volume 37 13 2004 633 638
- Ruggero Frezza , Alessandro Beghi , Alessandro Saccon Model Predictive for Path Following with Motorcycles: Application to the Development of the Pilot Model for Virtual Prototyping 43rd IEEE Conference on Decision and Control 2004
- Stuart Rowell , Atanas A. Popov , Jacob P. Meijaard Model Predictive Control Techniques for Motorcycle Rider Control IFAC Nonlinear Control Systems 40 10 2007 571 578
- Alessandro Saccon , John Hauser , Alessandro Beghi . A Virtual Rider for Motorcycles: An Approach Based on Optimal Control and Maneuver Regulation IEEE, 2008 3rd International Symposium on Communications, Control and Signal Processing
- C.C. Chen and L. Shaw On Receding Horizon Feedback Control 1982 Automatica 18 3 349 352
- Stephen H. Lane , Robert F. Stengel Nonlinear Inverse Dynamics Control Laws - A Sampled Data Approach Conference: American Control Conference 1987
- B. Jakubczyk Feedback Linearization of Discrete-time Systems Journal Systems & Control Letters. 1987 9 5 411 416
- M. Kiehl Parallel Multiple Shooting for the Solution of Initial Value Problem Parallel Computing 20 3 1994 275 295
- Moritz Diehl , H. Georg Bock , Johannes P Schöder , Rolf Findeisen , Zoltan Nagy , Frank Allgöwer Realtime Optimization and Nonlinear Model Predictive Control of Processes Governed by Differential-algebraic equations Journal of Process Control 12 4 2002 577 585
- Moritz Diehl , H. J. Ferreau , N. Haverbeke Efficient Numerical Methods for Nonlinear MPC and Moving Horizon estimation Lalo Magni , Davide Martino Raimondo , Frank Allgöwer Nonlinear Model Predictive Control Springer 2009 391 417
- Yutao Chen , Gianluca Frison , Niels van Duijkeren , Mattia Bruschetta Alessandro Beghi Moritz Diehl Efficient Partial Condensing Algorithms for Nonlinear Predictive Control with Partial Sensitivity Update IFAC 50-20 2018 406 411
- Diehl , Moritz , Rolf Findeisen , Frank Allgöwer , Hans Georg Bock Nominal stability of real-time iteration scheme for nonlinear model predictive control IEE Proceedings-Control Theory and Applications 152 3 2005 296 308
- VI-GRADE gmbh http://www.vi-grade.com/
- Yoshitaka Tezuka , Hidefumi Ishii , Satoru Kiyota Application of the magic formula tire model to motorcycle maneuverability analysis JSAE Review 22 3 July 2001 305 310