Self-learning inverse model control method of electronic throttle

A technology of electronic throttle and control method, which is applied in the direction of electrical control, neural learning method, adaptive control, etc., can solve problems such as complex controller design, and achieve the effects of overcoming nonlinearity, good control performance, and good control effect

Inactive Publication Date: 2010-01-20
HUNAN UNIV
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  • Abstract
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Problems solved by technology

At the same time, the electronic throttle also has time-varying conditions, which makes the design of the controller more complicated

Method used

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  • Self-learning inverse model control method of electronic throttle
  • Self-learning inverse model control method of electronic throttle
  • Self-learning inverse model control method of electronic throttle

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Embodiment 1

[0030] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0031] 1. Determine the structure of the controller.

[0032] The RBF neural network is an artificial neural network model that uses local receptive fields to perform function mapping based on the knowledge of biological local adjustment and overlapping receptive regions. It has the characteristics of optimal approximation and global approximation, and is a nonlinear modeling model. An effective tool in and control. The RBF neural network is usually defined as any point x in space to a certain center c i A monotonic function of the Euclidean distance between . The commonly used radial basis function here is the Gaussian function, whose form is

[0033] k i ( x ) = exp ( - ...

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Abstract

The invention discloses a self-learning inverse model control method of an electronic throttle. The control method comprises two parts: 1) taking a radial basic function neural network as a recognizer to create the non-linear model of the electronic throttle; 2) taking another RBF neural network to serve as an inverse model controller to obtain an appropriate control amount. The two RBF neural networks adopt a self-learning strategy which comprises an off-line learning step and an on-line learning step. In off-line learning, the two RBF neural networks adjust and optimize network weighting parameters according to on-site sample data; after the off-line learning is completed, an on-line learning algorithm is adopted to adjust and optimize the network weighting parameters of the two RBF neural networks, and then the control performance can meet the requirements. The invention can overcome the difficulties of nonlinearity, time varying and the like in electronic throttle control and improve the control effect and performance.

Description

technical field [0001] The invention belongs to the technical field of electronic throttle control of automobile engines, in particular to a self-learning inverse model control method of the electronic throttle. Background technique [0002] In a car engine, the function of the throttle is to control the intake flow of the engine through a certain angle switch, which directly affects the combustion process of the engine and determines the operating conditions of the engine. In actual operation, the driver often operates the accelerator pedal to control the throttle opening. However, there are two ways to connect the accelerator pedal and the throttle valve: rigid connection and flexible connection. The traditional accelerator adopts a rigid connection, that is, a mechanical connection method that connects the accelerator pedal and the throttle valve through a pull rod or a cable transmission, so the throttle opening depends entirely on the position of the accelerator pedal,...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/02F02D41/00G06N3/04G06N3/08
Inventor 王耀南袁小芳张辉吴亮红
Owner HUNAN UNIV
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