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A Multidirectional Approach for Networked Large-Scale Control Systems

A control system and large-scale technology, applied in the field of parameter identification, can solve problems such as slow convergence speed, affecting identification efficiency, and large amount of calculation, and achieve the effect of reducing resource consumption

Active Publication Date: 2022-03-25
JIANGNAN UNIV
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  • Application Information

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Problems solved by technology

Traditional identification algorithms such as the least squares method (Least Squares, LS) and the gradient method (Gradient Iterative, GI) are aimed at the parameter identification of large-scale systems. When the least squares algorithm is applied to the parameter identification of large-scale systems, it is necessary to calculate high-order The inverse of the matrix leads to a large amount of calculation and affects the identification efficiency; when the least squares algorithm updates the system parameters, it needs to assume that there is an analytical solution to the derivative function equation of the cost function. This strong assumption limits the scope of use of the least squares algorithm ; The gradient algorithm only uses one direction each time the parameters are updated, and the two connected directions are orthogonal, so its convergence speed is very slow

Method used

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Embodiment 1

[0045] refer to figure 1 , which is the first embodiment of the present invention, this embodiment provides a multi-directional method for network large-scale control systems, including:

[0046] S1: Acquisition system control data constructs source direction vector. It should be noted that,

[0047] For the following network large-scale control systems:

[0048]

[0049] Among them: y(t) is the output of the system, v(t) is the noise of the system, and obeys the Gaussian distribution with zero mean and variance σ, φ i (t), i=1,...,n is a scalar composed of input u(1),...,u(t) and output y(1),...,y(t-1), a 1 ,...,a n is the parameter to be identified of the system, and T is the transformation rank of the matrix.

[0050] Collect L sets of input, output and noise data, and define:

[0051] Y(L)=[y(1),y(2),...,y(L)] T ∈R L

[0052] Φ(L)=[φ T (1), φ T (2),...,φ T (L)] T ∈R L×2n

[0053]

[0054] V(L)=[v(1),v(2),…,v(L)] T ∈R L

[0055] in is the informati...

Embodiment 2

[0086] refer to Figure 2-5 , is the second embodiment of the present invention, and what this embodiment is different from the first embodiment is that in order to better verify and explain the technical effect adopted in the method of the present invention, the means of scientific demonstration is used to verify that the method has real effect.

[0087] refer to figure 2 , the present embodiment models a three-capacity water tank, and the water tank model has ten parameters, namely θ=[a 1 ,a 2 ,a 3 ,a 4 ,a 5 ] T =[0.8,0.9,0.4,0.27,0.12] T , where q represents the dripping speed, that is, the input of the three-capacity water tank system, H 1 is the liquid level of the first tank of the three-capacity water tank, C 1 is the flow rate in the first tank, which serves as the input to the second tank, H 2 is the liquid level of the second tank, C 2 is the flow rate in the second tank, which serves as the input to the third tank, H 3 is the liquid level of the third t...

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Abstract

The invention discloses a multi-direction method for a large-scale network control system, which includes collecting system control data to construct a source direction vector; according to the source direction vector, using a Schmitt orthogonalization method to obtain multiple orthogonal directions; Computing a corresponding step size using the plurality of orthogonal directions; updating system parameters in combination with the plurality of orthogonal directions and the step size. The method of the invention can avoid solving the solution of the derivative function equation; convert the high-order matrix inversion into the low-order matrix inversion, thereby reducing the resource consumption of the entire identification process; the speed can be adaptively adjusted, the more the number of directions, the faster the speed, and the direction When the number is 1, it is equivalent to the gradient algorithm, and when the number of directions is equal to the parameter dimension, it is equivalent to the least squares algorithm.

Description

technical field [0001] The invention relates to the technical field of parameter identification, in particular to a multi-directional method for network large-scale control systems. Background technique [0002] With the rapid development of Internet of Things technology, industrial control systems collect signals through sensors and transmit signals through the network to realize mutual connection and communication. The scale of control systems is getting larger and larger, and high-level systems are needed to describe their dynamic processes. Traditional identification algorithms such as the least squares method (Least Squares, LS) and the gradient method (Gradient Iterative, GI) are aimed at the parameter identification of large-scale systems. When the least squares algorithm is applied to the parameter identification of large-scale systems, it is necessary to calculate high-order The inverse of the matrix leads to a large amount of calculation and affects the identificat...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/16G06F17/11G06F17/18G06F30/20G16Y20/10G16Y40/30
CPCG06F17/16G06F17/11G06F17/18G06F30/20G16Y20/10G16Y40/30
Inventor 陈晶浦琰胡满峰毛亚文
Owner JIANGNAN UNIV
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