| With the increase of car ownership,traffic accidents occur frequently,and the traffic safety problem has been paid more and more attention by various countries.At present,the field of automotive active safety technology is one of the hot spots of intelligent vehicle research,among which the active collision avoidance control system is the focus of attention,through the study of this system can effectively reduce the traffic accident rate,reduce people’s direct property losses,improve the active safety and stability of vehicles.Considering that steering collision avoidance is more effective than braking collision avoidance under dangerous conditions of high speed and low road adhesion coefficient,this paper takes steering collision avoidance of intelligent vehicles as the research object,based on the Model Predictive Control(MPC)principle,The trajectory tracking problem of intelligent vehicle and local collision avoidance trajectory planning in the course of trajectory tracking are studied to effectively achieve better tracking and ensure steering collision avoidance safety,improve the road traffic environment.Firstly,the trajectory tracking controller is designed based on vehicle dynamics model and model predictive control algorithm.Considering the real-time performance of trajectory tracking control,a simplified three-degree-of-freedom monorail dynamic model is selected as the controller vehicle model.Considering the vehicle dynamics and the vehicle tire deflection characteristics,the front wheel Angle was taken as the control output,the constraint conditions and the objective function were set,and the trajectory tracking control problem was transformed into a quadratic programming problem to solve the optimal control quantity.The co-simulation platform of Carsim and Simulink was used to build the MPC trajectory tracking controller,and it was proved that the MPC controller with side Angle constraint has better tracking accuracy and control stability under low adhesion and different speeds.At the same time,the sliding mode controller is designed and compared with the MPC controller,which highlights the superiority of the MPC controller in trajectory tracking control at different speeds.Secondly,considering the influence of time domain parameters of MPC control algorithm on trajectory tracking control,an adaptive strategy of time domain parameters of MPC controller is proposed to improve the performance of the controller.The relationship between time domain parameters and vehicle speed was simulated on the established MPC,and the optimal time domain parameters under different speeds were determined by TOPSIS method.Add time domain parameter function to MPC controller to realize adaptive time domain parameter selection.The control precision and stability of the control process of the improved MPC controller were tested by the co-simulation platform.The results show that the controller with adaptive parameters has smaller lateral error and better vehicle stability.Then,a local dynamic collision avoidance trajectory planner for intelligent vehicles is designed.In order to effectively avoid the collision between vehicles and obstacles in the course of trajectory tracking,the point mass model is used as the vehicle state model of the planner,and the local dynamic programming of collision avoidance trajectory is studied based on the collision avoidance function and MPC algorithm.The collision avoidance function is designed based on the comprehensive description of the collision avoidance risk by the distance between the vehicle and the obstacle,the speed,the Angle between the vehicle and the obstacle and the weight of the penalty function.The local collision avoidance trajectory planner is designed by combining the nonlinear MPC algorithm,and the planning problem is transformed into a quadratic programming problem,which can solve the collision avoidance trajectory.Finally,the adaptive MPC trajectory tracking controller and trajectory planner are integrated into a steering collision avoidance control system,and the control model is built on the co-simulation platform.The design of collision avoidance control system is tested under different speed,different road adhesion conditions and different obstacle working conditions.It is proved that the system can control the vehicle to avoid obstacles while controlling the trajectory tracking. |
