Abstract
In this paper a concept for nonlinear robust model-based estimation of fundamental dynamic quantities of a motorcycle is presented. A nonlinear, robust estimation algorithm is used, which is based on a nonlinear principal dynamics model of the motorcycle. The principal model on which the observer design is based, is the so-called sliding plane motorcycle. The observer performance is shown in a simulation study. The sliding plane motorcycle model is then supplemented by the suspension dynamics. The resulting model accounts for in-plane motion of the motorcycle and is based on an estimated roll angle. The observer provides estimates for the pitch motion of the motorcycle. Roll, yaw and pitch rates are acquired with an inertial measurement unit and the steering angle and wheel velocities are also available as measured variable. Furthermore, a static motor model provides the rear wheel torque and the suspension stroke is measured with linear encoders. The presented approach is implemented based on the state space model. The pitch dynamics estimation algorithm is evaluated with measurement data. The results show the performance of the estimator for different driving maneuvers of the motorcycle.
Original language | English |
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Publication status | Published - 2023 |
Event | SAE 27th Small Powertrains and Energy Systems Technology Conference, SETC 2023 - Minneapolis, United States Duration: 31 Oct 2023 → 2 Nov 2023 |
Conference
Conference | SAE 27th Small Powertrains and Energy Systems Technology Conference, SETC 2023 |
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Country/Territory | United States |
City | Minneapolis |
Period | 31.10.2023 → 02.11.2023 |
Keywords
- motorcycle dynamics
- robust state estimation