Research On Intelligent Vehicle Path Tracking Control Based On GPS/INS Integrated Navigation

The speed of development of the automobile industry is very amazing.And the develackiopment of intelligent vehicles is the inevitable trend of today’s automotive industry.The key parts of intelligent vehicle include environment perception,path planning and path trng.The reason for the increased interest in the study of intelligent vehicle technology is that people pay more and more attention to traffic safety.Research on intelligent driverless cars can not only comply with the needs of the development trend of The Times,but also reduce the occurrence of traffic accidents.At present,intelligent driving technology mainly relies on GPS and inertial navigation system for positioning.When GPS works alone,although its precision is high,its anti-interference ability is weak.However,although the inertial navigation system has strong antiinterference ability,its low accuracy makes it have certain limitations in vehicle positioning.In order to solve the impact of non-gaussian noise caused by environmental noise,this paper proposes a navigation and positioning method based on the information fusion of the iterative kalman auxiliary particle filtering algorithm(APF-IKF).The APF-IKF algorithm optimizes the distribution function by introducing the latest observation results,solves the particle attenuation and particle degeneracy,and effectively eliminates the effect of non-Gaussian noise.It effectively improves the estimation accuracy of INS error,thus significantly improving the accuracy of integrated navigation.This paper builds a combined navigation error model and introduces the APF-IKF algorithm.Through the error value of the current time k,the estimated value and the measured value,this algorithm estimates the optimal error value at the time k+1.It compensates the optimal amount to the information value from the INS calculation to obtain the final output.By this means,the external disturbance in measurement value and the model error information can be effectively utilized to improve the accuracy of integrated navigation.In order to verify the effectiveness and the superiority of the algorithm of this paper,the MATLAB/Simulink error model is built,the double antenna differential GPS is taken as the path reference,the positioning effects of single INS,non-differential GPS and different algorithm INS/GPS are compared,and the experimental result are analyzed.In this paper,a two-degree-of-freedom model,a tire model and a two-lane road model are established by referring to the actual vehicle state when the vehicle is running normally on the road.In this paper,the dynamics of the vehicle modeling,through learning and analysis of the model prediction algorithm prediction and optimization part.Thus,the path tracking controller of intelligent vehicle is designed based on this algorithm,and the controller model is built in MATLAB/Simulink.The simulation results of the controller at low speed(20km/h)and medium-high speed(70km/h)were analyzed by setting up the carsim-simulink co-simulation.And compared with the industrial commonly used PID algorithm to verify the control effect of the controller.Finally,in order to further verify the actual control effect of the controller designed in this paper,this paper took the intelligent vehicle as the research platform and selected a section of LiaoCheng city in shandong province as the test site.The simulated MPC established by Simulink was downloaded to the dSPACE controller for real vehicle verification,and the performance of the controller was verified.