Abstract
Active steering technology is a key technology for automatic driving vehicles to achieve route tracking and obstacle avoidance and risk avoidance, and its performance will affect the stability control of the vehicle. For solving the stability control issues of vehicles, which have uncertainty in model and robustness in system, this paper proposes an active steering control method based on the receding horizon control model. It calculates the optimal control law by this method by using the real-time vehicle state so that it can compensate for the uncertainty caused by model mismatch, interference, etc. The design of the controller is implemented by using the yaw rate deviation of the vehicle as the input of the receding horizon linear quadratic controller model and then inputting the calculated superposition angle into the vehicle model in real time. We built a Simulink control model to implement co-simulation with CarSim to verify the control effect of the controller. In addition, we built a steering hardware-in-the-loop platform based on the LabVIEW RT system. The experimental results show that the active steering system adopting a receding horizon control method had better system robustness and robust stability.
Highlights
As an important component of vehicle stability control, the active steering system can determine the conditions that will occur according to the driving state of the vehicle
The results show that the receding horizon controller had a faster response and better control effect than the proportion integration differentiation (PID) controller
It can effectively track the yaw rate for the vehicle provided with PID control, the three conditions, from which we can see that the vehicle without control had a large delay in the route tracking when it passed through the double-lane-change route, it formed a steeper route with a larger magnitude, and the vehicle was in a state of instability
Summary
As an important component of vehicle stability control, the active steering system can determine the conditions that will occur according to the driving state of the vehicle. It is difficult to obtain optimal PID parameters and its robustness for all operating conditions is not good These active steering controls based on the classical control theory are unable to inhibit the uncertainty of the steering system and the interference caused by nonlinear factors. By taking into consideration factors such as model uncertainties, various external disturbances, sensor noise, etc., Xu Zhijiang designed the yaw rate robust controller of systems based on μ comprehensive robust control, which can effectively improve the active safety and operating stability of vehicles [21]. In the process of actual use, it could improve the robustness of the system, had a good effect on the suppression of external noise, and could better guarantee the stability of the vehicle
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