Research On Intelligent Vehicle Trajectory Tracking Control Algorithm

With the development of the automobile industry,the number of vehicles owned by residents is increasing.In order to alleviate the problems of road congestion and frequent traffic accidents caused by the increase in the number of vehicles,the research on autonomous driving of intelligent vehicles has become the focus of social and academic circles.As a key technology to realize autonomous driving of intelligent vehicles,trajectory tracking control has important practical significance for solving road congestion and traffic accidents.Since the vehicle is a multi-constraint body,the model predictive control(MPC)algorithm can deal with the constraints in the vehicle well,so it can be applied to the trajectory tracking control of intelligent vehicles.Based on the MPC algorithm,this paper studies the trajectory tracking control of intelligent vehicles.(1)The basis of model predictive control algorithm,the selection of vehicle model and how to apply model predictive control to intelligent vehicle trajectory tracking control are introduced in detail.According to the needs of the system model,the geometry principle is applied to the process of establishing the vehicle kinematics model,and the model is simplified accordingly.The nonlinear vehicle model is linearized and discretized to make the operator easy to solve.The objective function is established according to the requirements of the controller.The effectiveness of the trajectory tracking model controller based on vehicle kinematics is verified by Car Sim and Simulink co-simulation platform.(2)A dynamic model is needed to describe the vehicle motion when the vehicle is running at high speed.Under the assumption of small angle steering,combined with the magic tire formula,a three-degree-of-freedom dynamic model of vehicle based on linear tire is established.In order to reduce the complexity of the model and the amount of calculation,the vehicle model needs to be simplified reasonably,and the linearized error model is studied.In order to improve the vehicle driving stability under the condition of low road friction coefficient,a series of constraints are added to the vehicle dynamics model.The stability of the controller is analyzed and proved according to Lyapunov theory.The vehicle model is established in Car Sim,and the co-simulation platform is used to verify that the designed controller can show good tracking effect under different vehicle speeds and different ground friction coefficients.(3)Considering the driving conditions of the vehicle in complex environment,the obstacle avoidance trajectory tracking control is studied.Design obstacle avoidance planning and trajectory tracking control system.The perception module transmits the obstacle information to the trajectory planning controller,which is used to calculate the solution in combination with the lane change strategy to obtain the temporary obstacle avoidance trajectory.The temporary obstacle avoidance trajectory is sent to the trajectory tracking controller,and the front wheel angle is output to control the vehicle.A variable time domain adaptive model predictive control algorithm is designed.The influence of the predictive time domain on the controller in MPC is studied.The adaptive predictive time domain-speed control law is developed by the relationship between the predictive time domain and the vehicle speed.The control law can make the controller calculate the optimal predictive time domain in real time.The variable speed driving condition is set up,and the joint simulation platform is built.The obstacle avoidance trajectory tracking simulation is carried out on the dry road.The results show that the vehicle can not only avoid obstacles to achieve autonomous driving,but also has good tracking accuracy and driving stability.