Vehicle running state nonlinear robust estimation method based on sliding mode observer

Abstract

The invention provides a vehicle running state nonlinear robust estimation method based on a sliding mode observer. The method comprises the following steps of: determining an automotive nonlinear kinetic model closer to the actual situation aiming at high mobility running conditions and complex variable road environments, designing the corresponding sliding mode observer, establishing external input quantity and measurement information of the sliding mode observer system by using low-cost vehicle-mounted wheel speed and steering wheel turning angle sensors, and thus implementing estimation of multiple running states of an automobile by a sliding mode observer estimation recursion algorithm. The method has the characteristics of strong anti-interference capacity, high precision, low cost and the like, and is easy to implement.

Description

technical field [0001] The invention relates to a method for nonlinear robust estimation of vehicle operating state based on a sliding mode observer. Accurate and reliable operating states under complex and changeable road conditions, these states can be used for the related control of automobile active safety, the method of the invention has remarkable characteristics such as strong anti-interference ability, high precision, good real-time performance and low cost, and belongs to automobile Active safety measurement and control field. Background technique [0002] In order to prevent road traffic accidents, automotive active safety technology has developed rapidly in recent years. Automobile active safety technology can prevent accidents before they happen and actively avoid accidents, which has become one of the most important development directions of modern automobiles. The current common active safety technologies mainly include anti-lock braking system (ABS), vehicle...

AI Technical Summary

Problems solved by technology

One is to use low-cost on-board sensors (such as inertial sensors and wheel speed sensors, etc.) to perform simple mathematical calculations on the measured signals to obtain relevant vehicle operating conditions. This method is low in cost, but due to poor accuracy of low-cost sensors And the calculation process is too simple and there are large measurement errors, thus affecting the control effect

The second is to use high-precision sensors to directly measure the running status of the relevant vehicles (such as using a photoelectric five-wheel instrument or a high-precision global navigation satellite system GNSS, especially a high-precision global positioning system GPS, etc.). This method has high precision but is expensive. Expensive and cannot be widely used

However, the model methods proposed so far are mainly based on the kinematics model of the car or the dynamic model with more linear assumptions on the vehicle or tires, and often do not take into account the changes in the external road environment. Better estimation results and accuracy can be obtained when there is less external environmental interference, but under high maneuvering conditions and complex and changeable road environments, it is difficult to reflect the real dynamic behavior and conditions of the vehicle, resulting in low estimation accuracy or even divergence, and is resistant to Interference performance needs to be improved

Images