Research On Trajectory Tracking Control For Autonomous Driving Vehicles

In order to tackle the increasingly serious traffic,environment and energy problems and guarantee the sustainable development of auto industry,more and more people have been focusing on autonomous-driving technology.As a result,many scientific research institutions and enterprises have been established.This would be a positive trend to actively promote the vigorous development of the technology.The autonomous driving technology represents future tendency of the auto industry.In addition,it is an emerging industry that can be regarded as new revolution of science and technology,incorporating many advanced technologies such as wireless communication,intelligent interconnection,environment detection and dynamic control of vehicles,etc.Therefore,it is greatly important to put more attention on studying autonomous driving technology,and is of great strategic significance to promote transformation and upgrading of the auto industry.The topic of this thesis mainly concentrates on one of the key technologies-the study of trajectory tracking control.In this regard,the paper first elaborates the research background,significance and the present development situation,then concretely analyzes the trajectory tracking control of the existing research methods.Meanwhile,the existing drawbacks are concluded and some improving measures are proposed.As the complexity of the vehicle system and surrounding driving circumstances,one single kinematics model with non-holonomic constraint fundamentally cannot ensure the stability of the autonomous vehicle.Hence,it is necessary to establish tire model according to MF formula and then implement simulation under different conditions;In addition,tire model and the vehicle dynamics model are used to act as basic system model of this paper.During control algorithm study,the priority is to apply traditional fuzzy control algorithm to track the desired trajectory by adopting system model and fuzzy logic controller based on expert experience.Considering the limitations and drawbacks of fuzzy controller that is based on experience,this paper illustrates PSO intelligent optimization algorithm to optimize the parameters of the fuzzy controller and achieve adaptive fuzzy control.Also,the compute process is briefly introduced.This method,however,does not consider the relationship between the vehicle and the surrounding circumstance,including the constraints of the actuator and the wheel-ground adhesion factors,will result in poor tracking effect when vehicles encounter extreme conditions(with max error=0.2m).Therefore,in order to increase the performance of trajectory tracking control algorithm of autonomous driving vehicles,the paper further studies the MPC trajectory tracking control algorithm.Model predictive equation is established according to the vehicle system in Chapter two,the controller equations are also resolved and optimized.In view of the drawbacks of the non-holonomic constraint model,the constraint conditions with the tire side-slip angle and the coefficient of friction with the ground are added to the controller.The final step is to simulate and compare two control methods that have been proposed in this paper.The results convey that two algorithms can accurately track the desired trajectory at low & intermediate velocity and error is very small(approximately 0.3m).While,there still exists small error when vehicle making turns at high velocity.The error that utilizing FLC only reaches 1.2m and it decreases to 0.5m after using PSO to optimize parameters.Meanwhile,the MPC algorithm minimize error to 0.3m.In conclusion,the improved algorithms possess sound tracking performance and can satisfy the requirement of trajectory tracking control of autonomous driving vehicles.