| The information of vehicle state is the vehicle velocity, yaw and the tire forces that reflect all aspect of vehicle motion and is important to vehicle simulation, handling evaluation, control system design, and safety measures. Especially in recent years, the application of advanced control devices and assistant systems which have been suggested aiming to improve the vehicle drivability and safety is more and more commonly, such as anti-locking brake systems (ABS), traction control systems (TCS) and electronic stability program (ESP), safety margin determination and collision avoidance systems that increase safety and improve handling characteristics. So the knowledge of the vehicle state is essential to those systems, and is also the premises of closed-loop control. Because of the complexity of the vehicle control systems and it is well known that parts of vehicle state information can be measured in use of the multiple sensors, but the sensors are expensive, and vehicle state information is impacted by system noise and observation noise during vehicle running. So it is difficult to find an accuracy and low-cost way for the measurement of vehicle state parameter and for the accurate vehicle control.With the automobile dynamics simulation, this paper describes soft measurement technique and the use of Kalman filter in the state estimation scheme to resolve the measurement inadequacy of vehicle state parameter. And with soft measurement technique and vehicle dynamic model, vehicle state (such as yaw rate, longitudinal velocities, lateral velocities and so on) estimation algorithm is proposed based on Kalman filter. The state estimation scheme improves the measurement inadequacy of vehicle state parameter and makes a contribution to accurate real-time vehicle control.The main work of this paper has three components:Study the theory of the soft measurement technique and the Kalman filter (KF) algorithm. With the state estimation theory that based on soft... |
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