Motorcycles must be designed for safety and long operation. Front suspension systems must in turn be safe and able to operate for long service lives. Challenges to achieving safe and long service lifetimes include designing components (rims, axles, forks, etc.) to withstand various loading conditions not just once but numerous times as a matter of fatigue life. An already developed CAD model of a motorcycle suspension was taken and optimized for various loading conditions. These conditions included static loading, braking, cornering, and wheelie and front impact loads. In all cases, front impact load was the critical loading condition when FEA in SolidWorks Simulation was conducted for the components. All components were then optimized to handle the impact load by changing geometry until safety factors of 4.0 ± 0.25 were achieved. Components were then analyzed for fatigue life, with all steel and magnesium components having infinite predicted fatigue lives and all aluminum components having fatigue lives predicted with corrected S-N curves created for up to 500 million loading cycles. The design was optimized with all components becoming improved for stress compliance, with room for improvement existing in both defining loads for analysis and developing more accurate and rigorous fatigue life models.
This thesis focuses on self-stabilization of a motorcycle using an active control momentum gyroscope (CMG) and validation of this multi-degree-of-freedom system’s mathematical model. Physical platform was created to mimic the simulation as accurately as possible and all components used were justified. This process involves derivation of a 3 Degree-of-Freedom (DOF) system’s forward kinematics and its Jacobian matrix, simulation analysis of different controller algorithms, setting the system and subsystem specifications, and real system experimentation and data analysis.
A Jacobian matrix was used to calculate accurately decomposed resultant angular velocities which are used to create the dynamics model of the system torque using the Euler-Lagrange method. This produces a nonlinear second order differential equation that is modeled using MATLAB/Simulink. PID, and cascaded feedback loop are tested in this Simulink model. Cascaded feedback loop shows most promises in the simulation analysis. Therefore, system specifications are calculated according to the data produced by this controller method. The model validation is executed using the Vicon motion capture system which captured the roll angle of the motorcycle. This work contributes to creating a set of procedures for creating a validated dynamic model for a CMG stabilized motorcycle which can be used to create variants of other self-stabilizing motorcycle system.
