Model Predictive Control Framework for Improving Vehicle Cornering Performance Using Handling Characteristics
This work addresses the complexity and dependency on road friction in vehicle stability control, offering a simpler approach for production vehicles.
The paper proposes a model predictive control strategy that exploits the natural understeer characteristics of production vehicles to improve cornering performance, eliminating the need for road friction information and reference yaw rate. Simulations demonstrate effectiveness across various test scenarios.
This paper proposes a new control strategy to improve vehicle cornering performance in a model predictive control framework. The most distinguishing feature of the proposed method is that the natural handling characteristics of the production vehicle is exploited to reduce the complexity of the conventional control methods. For safety s sake, most production vehicles are built to exhibit an understeer handling characteristics to some extent. By monitoring how much the vehicle is biased into the understeer state, the controller attempts to adjust this amount in a way that improves the vehicle cornering performance. With this particular strategy, an innovative controller can be designed without road friction information, which complicates the conventional control methods. In addition, unlike the conventional controllers, the reference yaw rate that is highly dependent on road friction need not be defined due to the proposed control structure. The optimal control problem is formulated in a model predictive control framework to handle the constraints efficiently, and simulations in various test scenarios illustrate the effectiveness of the proposed approach.