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Nonlinear neural network optimizing PID control method for temperature of electric heating furnace

A neural network, electric heating furnace technology, applied in the field of automation, can solve the problems of complex mechanism, difficult to determine the model, difficult to meet the system control requirements and so on

Inactive Publication Date: 2017-08-15
HANGZHOU DIANZI UNIV
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AI Technical Summary

Problems solved by technology

[0002] The PID control strategy has been widely used in various industrial processes due to its simple algorithm, good stability, and high reliability. It is difficult to determine, so it is difficult to meet the control requirements of the system using conventional PID control and parameter tuning methods

Method used

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  • Nonlinear neural network optimizing PID control method for temperature of electric heating furnace
  • Nonlinear neural network optimizing PID control method for temperature of electric heating furnace
  • Nonlinear neural network optimizing PID control method for temperature of electric heating furnace

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Embodiment Construction

[0061] Take the performance evaluation of temperature control in an electric heating furnace as an example:

[0062] The heating process of the electric heating furnace is a typical control process with large inertia and time delay. The control method is the duty cycle of the electric heating furnace. The method proposed in this paper is used to control the electric heating furnace specifically.

[0063] The concrete implementation method of the inventive method comprises:

[0064] Step 1, the training of RBF neural network, specifically:

[0065] 1.1 First collect the input and output data of the electric heating furnace control system, and use the data to train the RBF neural network. System performance index function J RBF for:

[0066]

[0067] Among them, y(k), y p (k) respectively represent the actual output value of the electric heating furnace control system and the output value predicted by the forecast model at time k.

[0068] 1.2 Adjust the parameters of th...

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Abstract

The invention discloses a nonlinear neural network optimizing PID control method for the temperature of an electric heating furnace. An RBF neural network is trained offline according to historical input and output information of an electric heating furnace control system, neural network related parameters are obtained, and a trained neural network serves as a prediction model of the system; and the RBF neural network is combined with the PID controller, and the RBF neural network is used to self-set the parameters of the PID controller online. Thus, the difficulty in establishing a nonlinear electric heating furnace model is overcome, the RBF neural network is used to self-set the parameters of the PID controller, and the problem that the electric heating furnace control system is hard to set the parameters of the PID controller in the practical control process is solved.

Description

technical field [0001] The invention belongs to the technical field of automation and relates to a nonlinear neural network optimization PID control method for the temperature of an electric heating furnace. Background technique [0002] The PID control strategy has been widely used in various industrial processes due to its simple algorithm, good stability, and high reliability. It is difficult to determine, so it is difficult to meet the control requirements of the system using conventional PID control and parameter setting methods. [0003] Considering that the neural network is an interdisciplinary subject, and it can fully approximate the complex nonlinear relationship, and has the characteristics of learning and adapting to the dynamic characteristics and fault tolerance of the uncertain system, so the present invention considers the use of the RBF neural network to control the electric heating furnace. The system establishes a predictive model, and adjusts the parame...

Claims

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 张日东房涛
Owner HANGZHOU DIANZI UNIV
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