Research On Vehicle Positioning System And Trajectory Tracking Control Based On Multi-sensor Fusion

The emergence of multi-sensor fusion technology has greatly promoted the development of on-board positioning technology.In the past,the use of GPS or inertial navigation systems for positioning alone has passed.In the field of unmanned driving,data fusion between GPS and inertial navigation equipment can Greatly improve positioning accuracy.There are many data fusion methods.In this paper,Kalman filtering is used.Because of its transitivity,timeliness,and accuracy,it is widely used.Based on this,the GPS / IMU combined positioning filtering algorithm has matured on this basis.This filtering algorithm has multiple coupling methods,such as loose combination,tight combination,and deep combination.This article uses loose combination.The basic idea is to use the GPS and inertial navigation two positioning systems as subsystems,which work separately.Pass the respective resolved positioning results to the Kalman filter,take the difference between them,and then calculate the error of the inertial navigation system by the error model,and finally use the corrected inertial navigation output result as the output of the entire system To optimize results.After that,the modified smart car was used as the carrier,and the GPS and inertial navigation data were collected and processed for simulation experiments.Finally,real vehicle experiments were performed based on the extended Kalman filter algorithm.The simulation experiment results show that the fusion positioning error is better than that of GPS alone The errors in the x,y,and z directions have been reduced.In actual vehicle experiments,the latitude and longitude errors are basically controlled at the centimeter level,which indicates that in the integrated navigation and positioning system,the Kalman filter algorithm effectively reduces the three directions in the plane coordinates error.For the research of trajectory tracking control,it is mainly by controlling the driving and steering system of the vehicle to make the vehicle follow the desired trajectory.Contains longitudinal speed and lateral path following control.This paper first establishes the vehicle kinematics model,which can basically reflect the characteristics and performance of the vehicle.The design process of the MPC controller is introduced in detail.Starting from theoretical knowledge such as basic concepts,the prerequisites for writing control algorithms are deduced.The linear time-varying model predictive controller designes simulation experiments with different parameters worth predicting the time domain and controlling the time domain to determinethe optimal value.The trajectory tracking simulation experiments are performed under different vehicle speeds to verify the effectiveness of the algorithm.In addition,it also lays the algorithm foundation for the real vehicle test.The trajectory acquisition on campus also verifies the accuracy and real-time performance of the combined positioning device in this paper,and makes the final trajectory tracking real vehicle experiment successfully completed.