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Partial unknown parallel control method and system for system model

A system model and parallel control technology, applied in the direction of comprehensive factory control, adaptive control, neural learning method, etc., can solve the problem that the parallel control method cannot directly apply the system model

Pending Publication Date: 2022-08-02
GUANGDONG UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The present invention solves the defect that the existing parallel control method cannot be directly applied to a system whose system model is partially unknown and has relatively large application limitations

Method used

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  • Partial unknown parallel control method and system for system model
  • Partial unknown parallel control method and system for system model

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Embodiment 1

[0044] This embodiment proposes a parallel control method oriented to a partially unknown system model, refer to figure 1 , figure 1 A flow chart for a parallel control method for which the system model is partially unknown, including the following steps:

[0045] S1: Build an RBF neural network, use the RBF neural network to approximate the unknown part of the system model, and reconstruct the system model to obtain a reconstructed system model.

[0046] S2: Construct a sliding mode function of the reconstructed system model.

[0047] S3: Construct a parallel controller based on the RBF neural network and the sliding mode function, and use the parallel controller to control the system to run to an equilibrium state. The equilibrium state means that the derivative of the system state x is equal to 0, that is, the system does not change after running to this state, and reaches stability.

[0048] Since the system model is partially unknown, in order to obtain better performa...

Embodiment 2

[0052] see figure 2 , figure 2 In order to face the schematic diagram of the parallel control method for which the system model is partially unknown, this embodiment proposes a parallel control method for which the system model is partially unknown, including the following steps:

[0053] S1: Build an RBF neural network, use the RBF neural network to approximate the unknown part of the system model, and reconstruct the system model to obtain a reconstructed system model.

[0054] In this embodiment, the expression of the system model is as follows:

[0055]

[0056] in, is the n-dimensional system state vector, is the n-dimensional system control vector, is an unknown part of the system, f(x) in this embodiment is an unknown continuous-time state function, and f(0)=0, f(x) is Lipschitz continuous.

[0057] In this embodiment, for the unknown part of the system model, the number of nodes in the input layer, the hidden layer and the output layer is determined, and a G...

Embodiment 3

[0095] This embodiment proposes a parallel control system for which the system model is partially unknown, such as Figure 4 shown, Figure 4 It is the architecture diagram of the parallel control system for which the system model is partially unknown, including: RBF neural network building module, system reconstruction module, sliding mode function design module, parallel controller building module and control module.

[0096] In the specific implementation process, the RBF neural network building module determines the number of nodes in the input layer, hidden layer and output layer for the unknown part of the system model, selects the Gaussian function as the activation function, and sets the input layer and the hidden layer. The weight matrix between them is the unit matrix, and then the RBF neural network is constructed.

[0097] The system reconstruction module uses the RBF neural network to approximate the unknown part of the system model, reconstructs the system model...

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Abstract

The invention provides a parallel control method and system oriented to partial unknown of a system model, and the method comprises the steps: constructing an RBF neural network, approaching an unknown part of the system model through the RBF neural network, reconstructing the system model, and obtaining a reconstructed system model; constructing a sliding mode function according to the reconstruction system model; and constructing a parallel controller based on the RBF neural network and the sliding mode function, and controlling the system to run to a balanced state by using the parallel controller. According to the method, the RBF neural network approximates the unknown part of the system model to obtain the complete reconstruction system model, and the corresponding parallel controller is designed to control the system, so that the problem that a control signal is difficult to generate when the system state cannot be obtained under the condition that the part of the system model is unknown is effectively solved, and the reliability of the system is improved. The method can be widely applied to an actual system with an unknown system model part.

Description

technical field [0001] The invention relates to the field of intelligent control, and more particularly, to a parallel control method and system for which the system model is partially unknown. Background technique [0002] In today's industrial control systems, most system control problems are analyzed through state feedback control methods, that is, by designing a state feedback controller to form a closed-loop system, the control law is a function of the system state. analysis. The traditional state feedback controller is only related to the system state, but has nothing to do with the nature of the controller, resulting in a large change of the control signal with the system state, which brings great difficulties to the implementation of the controller, and the control signal is generated passively. It is difficult to generate control signals when the state of the system is not available. [0003] There is a parallel control method for an intelligent workshop, which pe...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G05B13/04G06N3/04G06N3/08
CPCG05B13/042G06N3/08G06N3/044Y02P90/02
Inventor 刘德荣林锦全王永华赵博
Owner GUANGDONG UNIV OF TECH