Research On Intelligent Vehicle Path Tracking Control Based On Curvature Optimization

Intelligent vehicle has great advantages in traffic safety and travel convenience,and has become the focus of vehicle engineering research at present.Path tracking,as one of the key technologies of intelligent vehicle,has attracted wide attention from experts and scholars at home and abroad.However,the existing path tracking research is often limited to the improvement of tracking accuracy,ignoring the changes of body stability during tracking process.Most of the accuracy optimization is achieved by improving the control algorithm,and less is done from the perspective of control model.In view of the above shortcomings,a path tracking controller based on curvature optimization is designed in this paper,which improves the tracking effect on the basis of ensuring the smooth driving of the vehicle.The main research contents are as follows:Firstly,a three-degree-of-freedom dynamic model including lateral,yaw and roll directions was selected as the controlled object,and a state-space model was introduced to analyze the stability,controllability and observability of the dynamic model under the statespace model.According to the established dynamic model,yaw angular velocity,lateral acceleration and roll angle are selected as body stability evaluation parameters,and the comparison simulation between state space equation and differential equation model is carried out to verify the accuracy of the model under state space modeling.Kinematic model of preaiming error for tracking error calculation is also introduced.Secondly,in order to verify the tracking effect of the designed path tracking control system,a comparative simulation analysis is carried out.The simulation results under different operating conditions demonstrate the effectiveness of the fuzzy sliding mode controller in path tracking control.At the same time,two common optimization algorithms are selected for comparison and analysis,in which the genetic algorithm is selected as the curvature optimization algorithm in this paper and optimized simulation is carried out based on it.The simulation results show that the curvature optimization controller effectively improves the tracking accuracy of the fuzzy sliding mode controller.The body stability constraints can also ensure the smooth driving of the vehicle during the optimization process,and ultimately achieve the comprehensive improvement of tracking accuracy and body stability.Thirdly,in order to verify the tracking effect of the designed path tracking control system,a comparative simulation analysis is carried out.The simulation results under different operating conditions show the effectiveness of the basic fuzzy sliding mode controller in path tracking control.At the same time,two common optimization algorithms are selected for comparison and analysis.Ultimately,genetic algorithm is selected as the curvature optimization algorithm in this paper and optimized simulation is carried out based on it.The simulation results show that the curvature optimization controller effectively improves the tracking accuracy of the fuzzy sliding mode controller.The body stability constraints can also ensure the smooth driving of the vehicle during the optimization process,and ultimately achieve the comprehensive improvement of tracking accuracy and body stability.Finally,in order to verify the path tracking effect and optimization performance under physical controller,Hardware-in-the-Loop test was carried out,and the corresponding controlled model and control system were loaded through NI real-time simulation platform and D2 P controller.The experimental results of path tracking controller show that the tracking effect in physical controller is basically the same as that in simulation.Meanwhile,the curvature optimization results in the experiment show that the tracking accuracy has been greatly improved and the lateral deviation after optimization is as low as 0.05 m,which verifies the effectiveness of curvature optimization design.In addition,the stability constraints control of the algorithm can also be triggered in time under extreme conditions.