Emery, Ashley F.Lu, Jing2021-03-192021-03-192021-03-192020Lu_washington_0250O_22435.pdfhttp://hdl.handle.net/1773/46843Thesis (Master's)--University of Washington, 2020This paper presents vehicle dynamic control algorithms for a dual-motor RWD electric racing car of UW Formula Motorsports. Team-31 vehicle was designed for human driver only, so the team proposed an PID (proportional-integral-derivative) based yaw rate and slip ratio control on dynamic bicycle or four-wheel vehicle dynamics model. The algorithm aimed to improve acceleration behavior in straight line event and cornering maneuverability. It will also serve as the low-level stability control for the future autonomous racing car. Team-32 aimed to compete in FSG driverless events in 2022 ~ 2023, so the control system should merge into autonomous driving hardware platform with a MPC (model predictive control) based trajectory tracking algorithm. Vehicle behavior and control effectiveness were analyzed using MATLAB Simulink, and hopefully would be validated in HIL (Hardware-in-loop) setup or RC car in the future. In Section 2: Vehicle handling and performance, the author explained the derivation of vehicle lateral and longitudinal dynamics model, critical parameters for cornering behavior evaluation and their effects on turn stability. Analysis of a traditional torque vectoring and traction control strategy was explained under steady states condition. In section 3: Model predictive control for autonomous driving, an optimization-based trajectory tracking method was introduced based on kinematic vehicle model. The controller behavior was evaluated under simulated competition events. Section 4: Hardware implementation covered key points in modeling and configurating critical actuators and sensors for vehicle test preparation.application/pdfen-USCC BY-SAautonomousdriverlessformulatorque vectoringtraction controlvehicle dynamicsMechanical engineeringAutomotive engineeringSystems scienceMechanical engineeringThesis