Anti-wheelie systems for high-performance motorcycles: A Nonlinear Model Predictive Control approach

作     者：Caiaffa, Luca Maran, Fabio Furlan, Matteo Peron, Stivi Beghi, Alessandro Bruschetta, Mattia 

作者机构：Univ Padua Dept Informat Engn Padua Italy Piaggio & Co Pisa Italy 

出 版 物：《CONTROL ENGINEERING PRACTICE》 (Control Eng. Pract.)

年 卷 期：2025年第157卷

核心收录：

学科分类：0711[理学-系统科学] 0808[工学-电气工程] 07[理学] 08[工学] 070105[理学-运筹学与控制论] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0701[理学-数学] 071101[理学-系统理论] 

基　　金：Research reported in this manuscript was supported by Piaggio & C. Piaggio & C. made available materials and people who contributed to the design  collection  and analysis of the data 

主　　题：Anti-wheelie systems Nonlinear Model Predictive Control Vehicle control Transportation systems Advanced Riding Assistance Systems (ARAS) 

摘      要：The global proliferation of Powered Two-Wheel (PTWs) underscores the need for increasingly effective active safety systems in motorcycles. Among others, the Anti-wheelie (AW) system is one of the most peculiar and safety-critical, aiming at limiting front wheel lift, preventing from possible vehicle instability, loss of control, and, in general, increased accident risk for motorcyclists. In this paper, an AW system based on an always-active, closed-loop control action that relies on a refined vehicle dynamics model is proposed. A Nonlinear Model Predictive Control strategy is leveraged to track an optimal pitch angle, ensuring maximum acceleration while maintaining safe interaction with the rider by constraining the reduction in applied torque. The control system is implemented on Raspberry Pi hardware, coupled to the vehicle s Electronic Control Unit (ECU). Preliminary tuning was conducted in a high-fidelity co-simulation environment, and experimental tests were conducted with a sport-commercial vehicle showing satisfactory control performance even in extreme maneuvers. The effectiveness of the control action is further validated through suspension travel measurements and feedback from professional test drivers.