Intelligent dual-integral sliding mode control method for electronic throttle based on observer

Abstract

The invention discloses an observer-based intelligent dual-integral sliding-mode control method for an electronic throttle valve. The method comprises the following steps: adopting an extended state observer, carrying out quantity x2 estimation on the opening change of the throttle valve according to the actual opening theta (x1) of the throttle valve, and obtaining the estimated value x2 of the opening change quantity of the throttle valve; combining sliding-mode control with a neural network through the Lyapunov stability theorem to obtain an intelligent dual-integral sliding-mode control law and an adaptive disturbance law, and carrying out control on the opening of the electronic throttle value and compensation on disturbance; carrying out adaptive design on the parameters of an intelligent dual-integral sliding-mode controller through a BP neural network, wherein the inputs of the BP neural network are the error e and error change e of the actual opening of the throttle valve and the expected opening of the throttle valve, and the outputs of the BP neural network are respectively taken as control gains kd, kp and ki; outputting the control input voltage u through the intelligent dual-integral sliding-mode controller to control a direct-current motor of the electronic throttle valve for controlling the opening of the electronic throttle valve. The method guarantees the precision of the electronic throttle valve for tracking expected input.

Description

technical field [0001] The invention belongs to an electronic throttle control method of an automobile engine, in particular to an observer-based electronic throttle intelligent double-integral sliding mode control method. Background technique [0002] As an important component of the engine intake system, the electronic throttle valve can control the air-fuel ratio of the engine by adjusting the air flow into the cylinder. In addition, precise electronic throttle control can not only improve the fuel economy and emission performance of the car, but also improve the driver's operating performance of the car, which has an important impact on the ride comfort of the car. In addition, due to the complex nonlinear coupling of the electronic throttle, such as stick-slip friction nonlinearity, return spring nonlinearity and gear gap nonlinearity, it is extremely difficult to accurately control the electronic throttle. Therefore, the electronic throttle control algorithm The resea...

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Problems solved by technology

[0003] In recent years, many scholars at home and abroad have conducted many related researches on electronic throttle, and achieved certain results. Unfortunately, some of the control strategies have not fully considered the nonlinear characteristics of electronic throttle, resulting in poor controller accuracy. Can meet the requirements, making it difficult for the controller to effectively control the throttle

Aiming at the nonlinearity of friction and return spring, Deur et al. [1] designed an optimized PID control algorithm, which compensated the influence of friction and return spring nonlinearity by designing a feedback compensator. Unfortunately, the algorithm did not give how to Select the compensator parameters, and need to identify the parameters of the control object

Inspired by the literature [1], Yuan et al. [2] introduced the neural network combined with PID control into the electronic throttle control, realized the precise control of the electronic throttle and completed the adaptive adjustment of PID parameters and the identification of object parameters. It is worth pointing out that the influence of the return spring preload torque on the electronic throttle control is not considered in this paper.

Subsequently, Sheng et al. [3] used hierarchical fuzzy PID control to control the electronic throttle, and used the fruit fly optimization algorithm to optimize the design of the controller parameters. The shortcoming is that Sheng et al. detailed description

However, in the fuzzy design of nonlinear hysteresis characteristics, the design of fuzzy rules is too simple to effectively describe the hysteresis characteristics, resulting in the feedforward compensator not being able to accurately compensate it

The above studies focus on a certain nonlinear characteristic in the electronic throttle control system, weakening the influence of other nonlinear characteristics, disturbances and uncertain factors on the controller design; Moments for detailed characterization

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