Study on Integrated Control of AFS and DYC for Active Obstacle Avoidance

In order to realize the obstacle avoidance and improve the vehicle’s yaw stability, a chassis integrated controller which cooperated Active Front Steering (AFS) and Direct Yaw Moment Control (DYC) is designed based on model predictive control (MPC) theory. It takes the corrective steering angle and desired yaw moment as inputs, and composes the driver-vehicle-road closed-loop system with the driver model. Performance of the controller is demonstrated as the continuous obstacle avoidance case. The simulation results show that the integrated controller can make the vehicle avoid the obstacle on both sides of the road and improve the yaw stability of the vehicle. Introduction Chassis integrated control has been received widespread attention from academe and industrial field. Through the integration we can give full play to the advantages of each control system. There are many references about the integrated control of steering and braking. In [1], a control integrated AFS and ABS is designed by fuzzy logic theory. In [2], the lateral dynamic control is regarded as a MIMO control problem. And the integrated control is designed by feedback linearization. In [3], the hierarchical control strategy is used in the joint simulation of electric power steering and ABS. In [4], the integrated controller of AFS and DYC is designed by guaranteed cost control theory. In [5], the integrated controller is designed based on the model-match-control (MMC) method. The MPC theory is a model-based optimal control strategy. Its algorithm has three characteristics: prediction model, rolling optimization and feedback correction. Because of good control effect, strong robustness, and can systematically deal with the various constraints and non-linearity of the control object, the MPC has become a new hotspot in the control research, especially for the vehicle dynamics problems. The papers [6-12] use the MPC theory to study the path planning and tracking control of intelligent vehicle. Meanwhile with the radar, camera and GPS gradually introduced into the Autonomous Vehicle, the car will be able to take the initiative to avoid obstacles on the road. In [13, 14], a linear robust MPC is designed by using the invariant set theory to solve the uncertainty of the driver model in the process of active obstacle avoidance. In [15], the unscented Kalman filter theory is used to calculate the vehicle state transfer in the predicted time domain concerning the random interference. During the design of the active obstacle avoidance controller, we should also consider the handling stability. In [16-18], the vehicle is kept at a steady state by giving a soft constraint to the tire slip angle and increasing the penalty function in the objective function. The disadvantage is the soft constraint is usually conservative in reality. In [19-21], the yaw moment control is used to control the slip angle by distributing the yaw moment generated by the longitudinal force on both sides to improve the handling stability. Theoretically, the linearity range of the tire longitudinal force is greater than that of the lateral force. We can still maintain the vehicle's handling stability, even if the tire lateral force tends to saturation through the yaw moment control. Therefore, this paper uses integration theory of AFS and DYC to design the controller, and improve the vehicle’s stability in the process of active obstacle avoidance by constraining the yaw moment.