Nonlinear MIMO (multiple input multiple output) system-based decoupling control method and device

A multiple-input multiple-output, decoupling control technology, applied in the field of decoupling control methods and devices, can solve problems such as inability to meet control accuracy and control effects

Inactive Publication Date: 2013-11-20
NORTHEAST GASOLINEEUM UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] With the development of control research, a single control strategy will inevitably have shortcomings when controlling the system, and it can no longer meet the requirements for control accuracy and control effec...

Method used

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  • Nonlinear MIMO (multiple input multiple output) system-based decoupling control method and device
  • Nonlinear MIMO (multiple input multiple output) system-based decoupling control method and device
  • Nonlinear MIMO (multiple input multiple output) system-based decoupling control method and device

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Experimental program
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Embodiment 1

[0051] For a 2-input 2-output system, if the nonlinear system Σ 1 transfer function can be used Σ 1 : G ( s ) = G 11 G 12 G 21 G 22 , The input is U=[u 1 u 2 ] T , the output is Y=[y 1 the y 2 ] T , and its coupling is as figure 1 shown, then the inverse system of the system Σ 2 : g ( s ) = g 11 ...

Embodiment 2

[0053] In this embodiment, the implementation of the neural network inverse model is described. Proceed as follows:

[0054] (1) Determine the structure of the neural network inverse model;

[0055] (2) Obtain training samples of the neural network;

[0056] Determine the appropriate sampling frequency and amount of sampled data. let f s is the sampling frequency, τ s is the sampling time, and N is the number of samples sampled. The maximum frequency that can be covered in the sampled data is ω max = 2π·f s / 2=π / τ s ; The frequency resolution (minimum frequency) in the sampled data is ω min =2π·(f s / 2) / (N / 2)=2π / Nτ s . According to the actual requirements of the system, ω can be determined max and ω min , so as to determine τ s and N, to preprocess the sample data and eliminate bad data.

[0057] (3) The construction of the training sample set of neural network;

[0058] The sampling and preprocessed results are combined into neural network training samples to fo...

Embodiment 3

[0098] like figure 2 As shown, it is a decoupling control device based on a nonlinear multiple-input multiple-output MIMO system consistent with the present invention, such as figure 2 1 is the decoupling control device.

[0099] The input module is used to receive input signals, and is also used to generate sampling signals for the neural network module and the neural network inverse module;

[0100] A neural network module, which is used to construct a neural network model;

[0101] A neural network inverse module, which is used to construct a neural network inverse model;

[0102] A control module, which is used to generate a control signal to perform decoupling control on the controlled object;

[0103] The delay module is used to delay the signal.

[0104] Wherein, the input signal is input to the neural network inverse module;

[0105] Inputting the output signal of the neural network inverse module into the control module and the neural network module;

[0106] ...

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Abstract

The invention provides a nonlinear MIMO (multiple input multiple output) system-based decoupling control method and a nonlinear multiple input multiple output system-based decoupling control device. The device comprises an input module, a neural network module, a neural network inverse module, a control module and a delay module, wherein an output signal of the neural network inverse module is input to the control module and the neural network module; when the control module and the neural network module have the same input signal, output signals of the control module and the neural network module are input into an output module; the output module generates a disturbing signal according to the output signals of the control module and the neural network module; and after being delayed, the disturbing signal is input into the neural network inverse module, and after being processed by the control module, the disturbing signal is input into the output module. By the method and the device, the speed and the stability of decoupling control on a nonlinear MIMO system are improved.

Description

technical field [0001] The invention relates to the technical field of power system control, in particular to a decoupling control method and device based on a nonlinear multiple-input multiple-output MIMO system. Background of the invention [0002] In the actual industrial production process, especially in the power system, the control objects are unknown and time-varying. If they cannot be properly controlled, it will cause huge hidden dangers in terms of economic benefits and personal safety. threaten. Therefore, with the development of control theory, a variety of control methods and new control strategies are also flourishing: some are improved on the classic control methods; some are new methods that are immature in application; , which takes its essence and minimizes its disadvantages as a composite control scheme. [0003] When controlling thermal objects, many control schemes have been tried, such as: PID control and parameter self-tuning, using neural network to...

Claims

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Application Information

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IPC IPC(8): G05B13/00
Inventor 段玉波高丙坤刘继承徐建军
Owner NORTHEAST GASOLINEEUM UNIV
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