| Since the birth of the automobile,it is self-evident that it has brought convenience to human development,but the following is the high traffic accident rate,which has caused great damage to the safety of human life and property.In the "person vehicle road" driving environment,human driver’s misoperation is the main cause of tragedy,and the auxiliary driving or automatic driving technology in intelligent vehicles can help or replace the driver to drive the vehicle,greatly reducing the number of traffic accidents caused by the driver.Therefore,the research of intelligent vehicle technology has great practical significance.Motion control is one of the key issues in the field of intelligent vehicle automatic driving,perception fusion and decision planning are inseparable from motion control,which is a hot issue in the research field of intelligent vehicle core technology,its research types mainly include horizontal control,vertical control and horizontal and vertical integrated control.As the vehicle itself is a complex system with high nonlinearity,parameter uncertainty and large hysteresis,how to overcome the above characteristics and establish an effective and reliable motion control system is the key problem.In order to solve this problem,this paper studies the lateral controller,longitudinal controller and lateral longitudinal motion integrated controller of intelligent vehicle,and carries out simulation verification and real vehicle test on the designed controller.The main work of this paper is as follows:(1)The system model and related setting modules are established.The relevant reference coordinate system of intelligent vehicle is established;under certain simplified assumptions,the force condition of vehicle and the kinematic geometric relationship of visual preview are analyzed,and the vehicle two degree of freedom dynamic model and visual preview error model are established,and the two models are combined as the prediction model of preview MPC lateral feedback controller;it also introduces the setting module of preview distance and expected speed on straight road and curve road and the calculation method of road curvature,which lays a foundation for the design and combination of transverse and longitudinal controllers.(2)The design and Simulation of preview MPC lateral controller are completed.The principle of MPC algorithm is introduced and deduced,including prediction model,rolling optimization and feedback correction.In order to effectively eliminate vehicle swing caused by continuous curvature change,a lateral feedforward controller is established based on real-time vehicle speed and road curvature obtained from preview;Taking full advantage of MPC algorithm’s multi constraint processing,MPC lateral feedback controller is established based on preview lateral deviation and heading deviation;MPC parameter adaptation is carried out based on different speed ranges,which reduces the influence of longitudinal speed on lateral control effect and improves the robustness of controller to vehicle speed;Finally,simulation verification of Preview MPC lateral motion controller is carried out,the results show that compared with the traditional MPC lateral controller,the control accuracy is improved and the driving stability is enhanced.(3)The genetic optimization longitudinal fuzzy controller is established and optimized.Considering the subjectivity and limitation of the traditional longitudinal fuzzy controller,it is difficult to achieve the optimal control performance,genetic algorithm is used to optimize the membership function,control rules,quantitative factor and scale factor,which improves the control performance.The simulation results show that the longitudinal fuzzy controller optimized by genetic algorithm can quickly eliminate the initial speed deviation,and has better tracking effect on the expected speed and more reasonable control quantity.(4)The combination and verification of lateral controller and longitudinal controller are completed.In order to realize effective control of vehicle lateral and longitudinal motion,longitudinal speed is taken as the starting point,the expected speed is set by using the road adhesion coefficient and the road curvature obtained from the preview,the lateral motion control effect is improved by tracking the expected speed.The simulation results show that the preview model can effectively identify the road curvature,and the controller sets the preview distance and the expected speed according to the curvature and the road adhesion coefficient,fully considering the influence of the two on the control effect;the integrated controller has good tracking effect,smooth steering,orderly coordination of the throttle and brake,no simultaneous operation,and good driving stability.(5)The real vehicle verification of the integrated controller is carried out.In order to verify the performance of the integrated controller when the actual vehicle is on the real road,a section of path including straight road and curve with different curvature is selected as the expected trajectory,and the existing intelligent electric vehicle platform is used for real vehicle verification.The results show that the tracking effect of the integrated controller is good and reliable,and the reason for the difference between the real vehicle test results and the simulation results is analyzed. |
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