Research On Tracking Control Of Autonomous Vehicle Under Complicated Conditions

At present,autonomous driving technology is applied to smart cars,public transportation,express vehicles,industrial applications and other fields.Its main development directions include: the assisted driving technology for complex low-speed driving environments(such as mines,construction sites,etc)and autonomous driving technology for high longitudinal driving speeds.The complex trajectory,large curvature changes,and simple kinematics of low-speed automatic driving vehicles,as well as the uncertainty,nonlinearity,and time-varying characteristics of high-speed automatic driving vehicles,could cause the significant enhancement of nonlinear characteristics of vehicle dynamics.Moreover,due to the above characteristics,the adaptation and robustness of system modeling and its algorithms should be further improved.For the studied of autonomous vehicle motion control,the conventional operating conditions is changed to complex dynamic operating conditions.How to solve the complex scenes including the complex curvature or low-adhesion road trajectory tracking is one of hot research issues.At present,there are few researches on trajectory tracking control in complex working conditions such as low adhesion roads and high-speed driving,which obviously cannot meet the control requirements of autonomous vehicles.The key technologies of autonomous vehicles include the environmental information fusion perception,combined positioning and high-precision maps,intelligent motion planning and decision-making,and vehicle motion control.Based on the planning output of the planning layer and feedback of the vehicle's state,the vehicle motion control can control the actions of the vehicle chassis actuators.Thus,the autonomous vehicle can track the desired trajectory smoothly,safely,and accurately.As the core issue of autonomous vehicles,the performance of motion control directly affects the driving safety and users' riding experience,which has strong theoretical research significance and engineering application value.Therefore,how to improve the accuracy and safety control algorithm of the trajectory tracking of autonomous vehicles with large curvature changes in complex paths,low adhesion roads,and high speed limit conditions will be very helpful for the tracking,economy,comfort and safety of autonomous driving vehicle,which has great academic value and engineering significance.Aiming at the problem of low-speed driving in a complex environment and high-speed driving,this thesis uses the model predictive control algorithms to improve the trajectory tracking accuracy of autonomous vehicles.A variable model predictive control algorithm under low-speed driving environments is developed.The model predictive controller for driving control under the complex conditions and high-speed environments is also studied.Moreover,the high-speed vehicle trajectory tracking coupling control algorithm and actual vehicle verification test are also investigated.The main research contents of the thesis are listed as follows:1.The vehicle kinematics and dynamics models of the vehicle trajectory tracking under complex limiting conditions is established,which is defined as the basis of the trajectory tracking controller model;the results from established model is compared with those from Carsim model to verify the accuracy of the kinematics and dynamics models.The error values of the kinematics and dynamic model and the Carsim model is compared to obtain the applicable speed boundary value of each model.2.The effect factors of the trajectory tracking accuracy and the stability of the model predictive controller are studied.Based on the vehicle's two-degree-of-freedom kinematics model,an improved vehicle trajectory tracking algorithm is proposed;and an optimized control target is constructed,where the control increment and state increment are defined as variables.The influences of the terminal constraint function and relaxation factor on the infeasible solution phenomenon in the model predictive control process are considered,an infeasible solution processing method is proposed.The influence factors of the trajectory tracking error of the vehicle lateral controller are studied.Then,the influences of the initial vehicle heading angle and sampling time on the vehicle trajectory tracking error are obtained.The stability boundary of the vehicle's?-r phase plane is analyzed to provide a safety constraint basis for the design of the model predictive controller.3.The influences of the road curvature and vehicle longitudinal speed on the vehicle trajectory tracking control are studied.By using the basic ideas of segmentation and differentiation,a variable model trajectory tracking method based on the model predictive control is proposed.This control method can adopt the linear or non-linear predictive control methods based on the road curvature.The algorithm can improve the tracking accuracy.At the same time,it considers the trajectory tracking accuracy and real-time performance.The robustness of this algorithm is verified by the simulation results under the double-line shifting working conditions and arbitrarily complex trajectory working conditions.4.The trajectory tracking controller under high-speed driving conditions is designed.The trajectory tracking control algorithm for high-speed vehicles on the wet and slippery roads is proposed.It can ensure the tracking performance and stability of the vehicle.A multi-objective collaborative control evaluation index is constructed.The control constraints are applied on the control cost function.The effects of the road adhesion coefficient,driving speed and controller parameters on the performance of the vehicle trajectory tracking control under the double shifting working conditions and S-shaped working conditions are studied.The PID and control parameters are constructed under the slippery road surface and low controller hardware level conditions.The MPC joint control method makes up for the shortcomings of the simple MPC control algorithm.5.An autonomous driving vehicle experimental platform is presented.The campus site is defined as the experimental environment.A real vehicle verification study of the autonomous vehicle trajectory tracking is carried out.Then,the effectiveness,tracking accuracy and stability of the vehicle trajectory tracking algorithm based on the model predictive control is verified under the straight lines,arcs and lane changes conditions.