Model predictive control for vehicle steering using road information in the local frame
Typ
Examensarbete för masterexamen
Program
Systems, control and mechatronics (MPSYS), MSc
Publicerad
2019
Författare
Mowitz, Love
Vu, Nam
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
This thesis was motivated by Chalmers formula student driverless 2019 (CFSD19) project, with the aim to
deliver a self-driving formula race car and compete in the Formula Student Czech competition. In this thesis, a control strategy to control the lateral motion of the CFSD19 car was designed and simulated. The control
algorithm is based on the model predictive control (MPC) framework. The simulation used a single track
dynamic model (also known as the bicycle model) for simulating the car's motion, and a kinematic model,
linearized around the currently sampled state at every sampling update, for the controller. The goal of the
controller is to steer the car, so that it can track a given path in the global frame, using only track information
observed by the car in its local frame. The performance of the controller is evaluated by measuring the lateral
deviation of the car from the track. Simulation shows that the car is able to track the path with acceptable
lateral deviation, and the control scheme can be executed fast enough for real-time application.
Beskrivning
Ämne/nyckelord
Model predictive control , MPC , Successive linearization