Research On Lateral And Longitudinal Tracking Control Of Electric Intelligent Vehicle

With the advent of the era of big data and artificial intelligence,the automobile industry has gradually ushered in new changes.Various emerging technologies have been applied to automobiles to continuously promote the development of automobiles in the direction of environmental protection and intelligence.Among them,unmanned driving technology has become a current research hotspot because of its broad development prospects.It replaces the ’ human ’ part of the traditional human-vehicle-roadtransportation system,effectively avoids the uncertainty of human beings,and is of great significance for improving ride safety and improving the traffic environment.Tracking control,as the underlying part of unmanned driving technology,is the basis for realizing unmanned driving of vehicles.Efficient tracking control methods are very important for realizing unmanned driving of vehicles.Based on this,this thesis mainly studies the path tracking control strategy.The main work is as follows :(1)Build a vehicle system model,establish a three-degree-of-freedom nonlinear dynamic model,The semi-empirical tire model based on the magic formula is used to describe the mechanical properties of the tire.The dynamic model is simplified based on the linear tire model and the small angle assumption,and compared with the Carsim vehicle model.The results show that the three-degree-of-freedom dynamic model established in this paper has high accuracy.(2)Based on the model predictive control theory and fuzzy control theory to study the lateral tracking control strategy,linearize and discretize the three-degree-of-freedom dynamic model in turn to obtain the lateral control prediction model,construct the objective function,and design the relevant parameter variables according to the stability requirements Constraint conditions,and solve the optimal solution by quadratic programming method.Considering the impact of the predicted time domain on the tracking performance,the adaptive tracking control strategy based on the predicted time domain based on vehicle speed and curvature changes is studied,and the tracking performance of the controller is verified by using the double shifting line as the reference trajectory.In this case,compared with the controller with fixed prediction time domain,the tracking accuracy and stability of the predictive time domain adaptive controller are improved.(3)The longitudinal tracking controller is designed based on the PID control theory,using a hierarchical control strategy.The upper controller calculates the desired acceleration based on the target vehicle speed and the actual vehicle speed,and the lower controller calculates the drive/brake torque required to reach the desired vehicle speed based on the desired acceleration,so as to complete the tracking of the target vehicle speed.To address the problem of poor tracking accuracy of the controller at different target speeds,genetic algorithm is introduced to optimize the PID control parameters,and the simulation results show that the optimized controller can not only keep up with the target speed faster but also has smaller tracking error at different speeds.(4)The reference speed is designed according to the curvature and road surface adhesion coefficient,and the transverse-longitudinal cooperative control system is designed by combining transverse control and longitudinal control with the speed as the coupling point,and the results show that the cooperative controller designed in this thesis can realize the tracking of vehicle speed and reference trajectory at the same time,and the tracking accuracy and stability are improved compared with the traditional cooperative controller under different road surface conditions.