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Neural network integral sliding mode control method for electro-hydraulic power-assisted steering system

A power steering, neural network technology, applied in the field of neural network integral sliding mode control of electro-hydraulic power steering system, can solve problems such as restricting control performance

Active Publication Date: 2019-06-14
FUZHOU UNIVERSITY
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Problems solved by technology

[0004] However, although the integral sliding mode method can effectively solve the highly nonlinear problem of the electro-hydraulic power steering system, the system mathematical model expression required for the controller setting severely restricts the further improvement of the control performance

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  • Neural network integral sliding mode control method for electro-hydraulic power-assisted steering system

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[0105] In order to make the features and advantages of this patent more obvious and easy to understand, the following specific examples are given together with the accompanying drawings and described in detail as follows:

[0106] In this embodiment, the following parameters are taken in the simulation system to model the electro-hydraulic power steering system:

[0107] Such as figure 1 , figure 2 As shown, in the electro-hydraulic power steering system, the length of the steering knuckle arm is m=0.36m, the distance between the steering cylinder action point and the kingpin is n=0.21m, the distance between the two kingpins of the single shaft is B=2.0596m, and the tie rod The length L=1.8854m, the angle between the steering arm and the axle beam γ=76°, the equivalent moments of inertia J of the left and right tires and their auxiliary structures L =J R =143.1kg·m 2 , the equivalent damping coefficient C of the left and right tires and other structures L =C R =4×10 3 ...

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Abstract

The invention provides a neural network integral sliding mode control method for an electro-hydraulic power-assisted steering system. The method comprises the steps of building a mathematical model ofthe electro-hydraulic power-assisted steering system; and obtaining an adaptive RBF neural network integral sliding mode controller designed based on a sliding mode method and an intelligent controltheory. A nonlinear integral sliding mode technology is adopted as a basic control method; the switching performance of the technology ensures that a control system has strong robustness for parameteruncertainty and external interference; a dynamic behavior of the electro-hydraulic power-assisted steering system is approached in real time in combination with an adaptive RBF neural network method;and the designed control method not only does not need to deduce a precise mathematical expression suitable for the design of the controller, but also does not need the measurement of a pump source pressure, a working pressure and resistance moments of left and right tires. Finally, the designed neural network integral sliding mode control method has strong robustness for model uncertainty and external time-varying interference, and can timely and accurately track a given expected instruction of the electro-hydraulic power-assisted steering system.

Description

technical field [0001] The invention relates to the technical field of electro-hydraulic power steering control, in particular to a neural network integral sliding mode control method for an electro-hydraulic power steering system. Background technique [0002] With the continuous improvement of low-speed flexibility and high-speed stability requirements of heavy-duty vehicle steering systems, electro-hydraulic power steering systems are widely used in heavy-duty vehicles due to their fast dynamic response and large output force / torque. However, the electro-hydraulic power steering system is a complex and representative system, usually composed of steering mechanism, valve-controlled dual steering cylinder and tire steering dynamics. In addition, the coupling relationship between the various parts of the entire system further increases the complexity of the control system, and the model uncertainty and external unknown interference also make it more difficult for the actual ...

Claims

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Application Information

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IPC IPC(8): G05B13/04
Inventor 杜恒王琳陈锦达陈赛李雨铮
Owner FUZHOU UNIVERSITY
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