Electronic throttle valve adaptive backstepping control method based on Romberg sliding-mode observer

Abstract

The invention discloses an electronic throttle valve adaptive backstepping control method based on a Romberg sliding-mode observer, and relates to the technical field of automobile electronic control. Firstly, in order to solve the problem that the change of the opening degree of an electronic throttle valve can not be measured, the change is estimated through the Romberg sliding-mode observer and based on the electronic throttle valve state equation; secondly, approaching is conducted on the nonlinear unknown quantity-gear clearance torque through the approaching characteristic of an RBF neural network; finally, a control law, an RBF network weight renewing law and an interference adaptive law are designed based on the Lyapunov stability theory and through combination with the nonlinear backstepping control method. By means of the method, the problem that nonlinear factors and some parameters easily change over time during electronic throttle valve control can be well solved, and the control effect and dynamic response performance are further improved.

Description

technical field [0001] The invention relates to the field of automobile electronic control, in particular to an automobile engine electronic throttle control (ETC) method, which is an adaptive backstepping control method based on a Lunberg sliding mode observer. Background technique [0002] With the continuous development of the automobile industry and the continuous improvement of people's requirements for automobile performance, compared with traditional mechanically connected throttle valves, electronic throttle valves have been widely used. Its control structure is as follows: figure 1 shown. The traditional throttle valve is controlled by the driver operating the accelerator pedal mechanically connected to the throttle valve plate. The throttle valve opening completely depends on the driver's operation intention, which is likely to cause the emission to fail to meet the standard, and the driver's mistake Operation will bring safety hazards to driving. The electronic ...

AI Technical Summary

Problems solved by technology

Wang Yaonan and others used the neural network to control the electronic throttle, and realized the self-adaptability of the system through the self-learning ability of the network. The disadvantage is that the neural network controller has a large learning capacity, which makes it difficult to be run in the microcontroller [9, 10]

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