Research On Collision Avoidance Control System Of Intelligent Vehicle Lateral Active Steering

The vehicle collision avoidance control system is an important technology to assist the driver to drive safely,and it is of great significance to protect the driving safety by actively changing the vehicle motion state to avoid or reduce the collision injury.Therefore,this paper designs an intelligent vehicle lateral active steering collision avoidance control system for the destabilization problem in the collision avoidance process,which improves the active safety of driving,and its main contents include the development of collision avoidance control strategy,the observation of key vehicle state parameters and the design of vehicle lateral stabilizer.Firstly,a steering lane change trajectory tracking control strategy is developed based on MPC(Model Predictive Control).The strategy uses a three-degree-of-freedom vehicle dynamics model and a magic tire dynamics model as the prediction base model,combined with a vehicle minimum lane change safety distance model and a fifth-order polynomial reference lane change path to achieve active vehicle steering lane change by controlling the front wheel turning angle and tracking the desired collision avoidance trajectory quickly and smoothly.Second,based on the four-wheel seven-degree-of-freedom vehicle dynamics model and the EKF(extended Kalman filter)algorithm,an observer of the center-of-mass lateral eccentricity is established to estimate the center-of-mass lateral eccentricity in real time and provide real-time accurate state parameters for achieving the stability control of the vehicle.Again,the transverse stability controller with two layers is established with the angular velocity of the transverse pendulum and the lateral deflection angle of the center of mass estimated by the observer as the control parameters: the upper layer controller outputs the additional transverse moment based on two different control strategies: first,the output additional transverse moment is calculated by the ANFIS neural network inference algorithm,and the parameters of the controller network are optimized by online training using the BP(back propagation)algorithm;Second,the output additional swing moment is calculated based on the fuzzy inference algorithm.The lower-level controller uses the control distribution algorithm to complete the inter-wheel distribution of braking torque,correct the vehicle instability state and reduce the trajectory deviation.Finally,the joint simulation environment of Matlab/Simulink and Car Sim is set up and different simulation conditions are designed to verify the effectiveness of the steering lane change trajectory tracking control strategy and the lateral stability controller.The simulation results show that the designed control strategy can effectively reduce the trajectory tracking deviation,and the ANFIS-based stability controller can further correct the trajectory deviation during the steering lane avoidance process and improve the lateral stability compared with the stability controller using fuzzy control strategy.