System for controlling roll stability of high-clearance vehicle based on grey prediction fuzzy PID algorithm

A technology of stability control and gray prediction model, which is applied to controllers with specific characteristics, electric controllers, etc., can solve the problems of difficult control systems and difficult establishment of accurate mathematical models for system characteristic information, so as to improve accuracy and improve The effect of PID controller and the effect of adapting to complex working environment

Pending Publication Date: 2019-05-10
SHIHEZI UNIVERSITY
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Problems solved by technology

For complex nonlinear and time-varying uncertain systems, the characteristic information of the system is uncertain and it is difficult to establish an accurate mathematical model to describe the behavior of the system. Gray predictive control theory can be well applied to the control problems of such systems, but How to combine it with other control algorithms and apply it to the integrated control of the control system is a difficult point in research and application

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  • System for controlling roll stability of high-clearance vehicle based on grey prediction fuzzy PID algorithm
  • System for controlling roll stability of high-clearance vehicle based on grey prediction fuzzy PID algorithm
  • System for controlling roll stability of high-clearance vehicle based on grey prediction fuzzy PID algorithm

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Embodiment 1

[0037] Embodiment 1: In this embodiment, a high ground clearance vehicle roll stability control system based on gray prediction fuzzy PID algorithm is applied to the operation stability control of high ground clearance agricultural vehicles. The control system mainly includes vehicle load, Hydraulic actuator, roll angle sensor, grey prediction model, fuzzy control model and PID controller;

[0038] Such as figure 1 As shown, the principle of the control system in the present invention is as follows:

[0039] The roll angle sensor obtains the high ground clearance vehicle load centroid roll angle signal w(t) at the sampling time t and transmits it to the gray prediction model for relevant calculations;

[0040] The gray prediction model obtains the predicted value of the roll angle w(p) after calculation and compares it with the expected value of the system roll angle w0 to obtain the difference e;

[0041] After the fuzzy control model receives the signal input of the difference e, it...

Embodiment 2

[0044] Embodiment 2: In this embodiment, the gray prediction model in the high ground clearance vehicle roll stability control method based on the gray prediction fuzzy PID algorithm is used to cumulatively calculate the system roll angle measurement value w(t) and output the corresponding prediction value w(p), the algorithm flow chart is as figure 2 As shown, the specific algorithm flow is carried out as follows:

[0045] Step 1. Obtain the vehicle load centroid roll angle signal w(t) according to the roll angle sensor, and accumulatively collect 4 data signals for calculation, which is used to establish a four-dimensional innovation GM(1,1) predictive model, and the set can be measured The original data sequence of the system roll angle output time is as follows:

[0046] (1)

[0047] Step 2. Since the original data sequence is the gray information data sequence of the system, it can be generated by accumulating the data sequence once (1-AGO), as shown in formula (2):

[0048] ...

Embodiment 3

[0060] Embodiment 3: In this embodiment, the fuzzy control model in the high ground clearance vehicle roll stability control method based on the gray prediction fuzzy PID algorithm mainly includes the determination of the membership function and the fuzzy inference rules.

[0061] The membership function curve is like image 3 As shown, the roll angle error e and roll angle velocity error ec are set as the input language variables of the system, and the PID control parameter adjustments Δkp, Δki, and Δkd are used as the output language variables of the system to determine the domain of system input and output language variables; system input The fuzzy subsets of output language variables are defined as {NB, NM, NS, ZO, PS, PM, PB}, where the language variables are NB (negative large), NM (negative medium), NS (negative small), ZO (Zero), PS (positive small), PM (positive middle), PB (positive big).

[0062] The formulation of fuzzy inference rules mainly relies on the influence of ...

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Abstract

The invention relates to a system for controlling the roll stability of a high-clearance vehicle based on a grey prediction fuzzy PID algorithm. The invention belongs to the field of agricultural carrier operation engineering and agricultural vehicle stability control, relates to a grey prediction, fuzzy control and PID control theory, and researches and developments a method for controlling the roll stability of a high-clearance agricultural vehicle. For the characteristic that the system has the nonlinearity, the complexity and the time-varying uncertainty when the high-clearance vehicle isoperated, by utilization of the advanced control characteristic of a grey prediction model, an active torque control system for mass centre side inclination angle control of a vehicle load is established based on a fuzzy PID control algorithm; the current state value of the vehicle is measured through a sensor; after the current state value of the vehicle is sent to an electronic control unit, a corresponding control instruction is generated, so that the space attitude of the vehicle load is adjusted; the control system can be well suitable for a complex working environment; and a solid foundation is provided for developing a relatively diversified agricultural vehicle stability control system.

Description

Technical field [0001] The invention belongs to the field of agricultural vehicle application engineering and agricultural vehicle stability control, and specifically relates to gray prediction, fuzzy control and PID control theories, and develops a high ground clearance agricultural vehicle roll stability control method, aiming at the high ground clearance vehicle operation system It has the characteristics of nonlinearity, complexity and time-varying uncertainty, using the "advanced control" characteristics of the gray prediction model, combining the advantages of fuzzy control algorithm and traditional PID control, establishing a high ground clearance vehicle roll stability control model, using the center of mass The roll angle and the roll angle speed are used as system state variables to implement feedback to generate the active moment of load space attitude control. The present invention can well adapt to complex working environments and provide a solid foundation for the d...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B11/42
Inventor 冯静安余希胜王卫兵郭祖扬王麒淦张鹏王伟军任志端申团辉
Owner SHIHEZI UNIVERSITY
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