Research On Adaptive Cruise Control For An Intelligent Vehicle Based On Model Predictive Control

As an important part of Advanced Driving Assistant System(ADAS),Adaptive Cruise Control(ACC)system can effectively reduce the driver’s operating intensity,improve the driving safety,comfort and fuel economy of the vehicle,which has been widely concerned by domestic and foreign engine manufacturers and research institutions in recent years.In this paper,considering the influence of sensor noise and environmental interference on ACC system,as well as the tracking performance,safety and comfort of vehicle driving,an ACC system based on model predictive control(MPC)algorithm of Kalman filter is proposed.At the same time,on the basis of the existing ACC system,this paper also puts forward the control method of autonomous tracking combined with ACC system,that is,to realize the autonomous tracking and driving of vehicles,which mainly includes the following research contents:First,according to the principle of vehicle dynamics,this paper establishes the differential equation of vehicle motion,takes the real vehicle test platform as the target vehicle,and establishes the longitudinal,transverse and yaw transport considering the vehicle body in the MATLAB/Simulink simulation platform A seven degree of freedom vehicle model with four wheels rotating.Through the serpentine condition test,and the comparison test results verify the accuracy and effectiveness of the built vehicle model,which lays the foundation for the follow-up ACC system control algorithm research and design.Then,according to the characteristics of longitudinal car following,this paper establishes the longitudinal car following model of ACC system,and selects the longitudinal acceleration of the car,the relative speed between the car and the front car and the distance error as the state variables.Considering the influence of sensor noise and environment disturbance on ACC system,Kalman filter is used to reduce the noise of state variables input to MPC controller.In the design of MPC controller,tracking performance,ride comfort and safety are selected as the performance indexes and system constraints of MPC controller to achieve multi-objective optimization of ACC system.In MPC controller,feedback correction mechanism is used to improve the prediction model,reduce the influence of parameter uncertainty and external interference on the model,and introduce relaxation factor to soften and expand the constraint conditions,expand the feasible solution area,and finally transform the optimization objective of ACC system into a constrained quadratic programming problem by solving the expected longitudinal acceleration of vehicles The vehicle longitudinal motion control is realized.The validity of the proposed MPC controller based on Kalman filter is verified by simulation and real vehicle test.The experimental results show that the MPC controller based on Kalman filter can effectively reduce the noise of the variables with sensor noise.At the same time,it has good performance in longitudinal tracking performance,safety and ride comfort,especially in safety and ride comfort.The control effect of MPC controller based on Kalman filter is better than that without Kalman filter Better brake.This paper proposes a high-performance and low-cost intelligent vehicle autonomous tracking control scheme for the real vehicle test platform,including collecting and sensing vehicle driving trajectory based on the vehicle positioning/inertial navigation system,and completing coordinate transformation and trajectory extraction;designing trajectory reference point preview method and PID horizontal and vertical control method,and realizing based on the vehicle open horizontal and vertical control execution interface Accurate,fast and reliable vehicle steering and acceleration and deceleration motion control,to achieve the vehicle’s independent tracking control.Finally,combining the independent tracking control and ACC system control to realize the independent tracking and following of vehicles In the tracking process,when the target vehicle appears in front of the vehicle,the host vehicle can adjust its speed according to the relative motion state of the target vehicle to ensure the longitudinal safety of the vehicle in the tracking process.Through the real vehicle test,the feasibility of the control scheme of vehicle independent tracking is verified.The test results show that the vehicle can drive autonomously along the preset trajectory,and the current target vehicle can drive stably following the preceding vehicle.