Model-free adaptive control method based on basic loop of ore grinding process

A model-free self-adaptive, control method technology, applied in the direction of self-adaptive control, general control system, control/regulation system, etc., can solve the problems of precociousness, weak local search ability, falling into local optimum, etc., and achieve rapid error fluctuation. The effect of range, good tracking, faster response time

Active Publication Date: 2019-01-22
HEBEI UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Using GWO to optimize MFAC parameters, the whole process has the advantages of simple process, few parameter settings, and fast optimization, but it is prone to disadvantages such as premature, weak local search ability, and easy to fall into local optimum when solving optimization problems.

Method used

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  • Model-free adaptive control method based on basic loop of ore grinding process
  • Model-free adaptive control method based on basic loop of ore grinding process
  • Model-free adaptive control method based on basic loop of ore grinding process

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0128] With the sampling period T = 20 seconds, the control system is discretized by the c2d function in Matlab for simulation experiments:

[0129] y(k)=1.323y(k-1)-0.4346y(k-2)+0.0647u(k-7)-0.009u(k-8) (21)

[0130] In order to balance the optimization accuracy and operation speed of the gray wolf optimization algorithm, the population size is selected as 100, the number of algorithm iterations is set as 50, and the population dimension is 4 (corresponding to the four control parameters of λ, μ, ρ and η in the IMFAC algorithm), The parameter setting range is λ∈[0.01,2], μ∈[0.1,1], ρ∈[0.01,2], η∈[0.1,1]. The expected output value of the cyclone feed concentration is set to y * =1, the value of ε is 10 -5 , the input and output data of the initial hydrocyclone to the ore concentration control system can be obtained from formula (21), and the initial value of I / O is u(1)=u(2)=u(3)=u(4)=u(5 )=u(6)=u(7)=u(8)=0, y(1)=y(2)=0, φ(1)=1, φ(2)=1.

[0131] From equations (1) and (21)...

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Abstract

The invention discloses a model-free adaptive control method based on a basic loop of an ore grinding process. The control method is applied to a cyclone ore feeding concentration control loop in theore grinding process; a grey wolf optimization algorithm is combined with model-free adaptive control; and meanwhile, the model-free adaptive control algorithm and the grey wolf optimization algorithmare improved. The related parameters of the IMFAC algorithm are optimized by adopting the IGWO algorithm, so that the control precision of the IMFAC algorithm and the optimal value of parameter selection are guaranteed; the cyclone ore feeding concentration is ensured to be stabilized near an expected value, so that the cyclone ore feeding concentration in the ore grinding process can be better tracked; and the method is better in control effect, wider in applicability and relatively strong in robustness. In the actual ore grinding process, the link of manual parameter adjustment is removed,so that the control process is more efficient, and the applicability is wider.

Description

technical field [0001] The invention belongs to the technical field of automatic control, in particular to a model-free self-adaptive control method based on the basic circuit of the grinding process. Background technique [0002] The ore grinding process is an important link in the production process of the concentrator. The ore particle size directly affects the final ore quality and metal recovery rate. It is necessary to control the particle size within the process standard to ensure the production efficiency and economic benefits of the concentrator. The basic loop control of the grinding process directly affects the final particle size of the grinding process, but there are many factors affecting the grinding and grading process and there are a large number of uncertain factors, equipment wear and external interference lead to strong nonlinearity of the system, ore hardness, ball mill speed and other factors The time-varying nature of the system is strong, and there is...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/02
CPCG05B13/024
Inventor 张燕陈慧丹李梵茹梁秀霞周颖贾巧娟
Owner HEBEI UNIV OF TECH
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