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On-line identification control method and system for dredger mud pump speed based on neural network

A technology of neural network and control method, which is applied in the field of online identification control method and system of dredger mud pump speed, which can solve the problems of inability to deal with offline identification and slow convergence speed.

Active Publication Date: 2021-08-06
SHANGHAI JIAOTONG UNIV
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AI Technical Summary

Problems solved by technology

[0003] Aiming at the shortcomings of the existing neural network system such as slow convergence speed and inability to deal with uncertain systems in offline identification, the present invention proposes an online identification control method and system for dredger mud pump speed based on neural network, which solves the problem of cutter suction dredger The problem of precise control of the speed of the mud pump has achieved the goal of precise construction and energy-saving transportation

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  • On-line identification control method and system for dredger mud pump speed based on neural network
  • On-line identification control method and system for dredger mud pump speed based on neural network
  • On-line identification control method and system for dredger mud pump speed based on neural network

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Embodiment Construction

[0020] In this embodiment, Matlab is used to simulate the physical model of the mud pump, and a required speed target is customized.

[0021] This embodiment relates to a neural network-based online identification control method for dredging equipment, which specifically includes the following steps:

[0022] Step 1, give the number of identification points N n and polynomial number N m and the number of terms in the Taylor expansion N a , N b

[0023] In this embodiment, the simulation object model expression is used: Objective function: y i =0.25sin[π(i-1) / 180] as an example, assign N n =N a =N b = 2,N m = 3;

[0024] Step 2: Give Top N m The input value and the corresponding output value at +1 control time point. In this example, the initial input value is assigned 1.9702 times the expected output value. The assignment is shown in Table 1 below.

[0025] Table 1, initial values

[0026] Simulation serial number Control input (u) output value (y) ...

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Abstract

An on-line identification control method for dredger mud pump speed based on weights directly determined by neural network. Firstly, a photoelectric encoder is used to detect the actual speed of the mud pump, and the corresponding mud pump drive frequency converter input target speed is recorded; then a forward neural network is established. The network controller uses the detected actual speeds of several mud pumps and the corresponding input target speeds as the input and output data of the neural network, and calculates and determines the weights of the neural network through the method of directly determining the weights; finally, according to the actual working conditions The target speed is determined by the trained forward neural network controller to determine the required speed control amount of the dredge pump, and is converted into a speed control command and sent to the drive frequency converter to realize online control; the invention solves the problem of the dredger pump speed The precise control problem has achieved the goal of precise construction and energy-saving transportation.

Description

technical field [0001] The invention relates to a technology in the field of artificial intelligence application, in particular to a method and system for online identification and control of the speed of a dredger dredge pump based on a neural network. Background technique [0002] Offline identification is obviously unable to deal with uncertain systems and systems whose system characteristics change with time. Especially for the dredger dredging system, as the excavation depth or width changes, the soil properties will change accordingly, the resistance characteristics of the pipeline will also change, and the characteristics of the dredge pump will also be affected. Therefore, the previous offline identification model has long been consistent with the actual production model. lost pair. Contents of the invention [0003] Aiming at the shortcomings of the existing neural network system such as slow convergence speed and inability to deal with uncertain systems in offli...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): F04D15/00
CPCF04D15/0066F05D2270/304F05D2270/709F05D2270/71
Inventor 李铭志何炎平谷孝利赵永生刘亚东黄超
Owner SHANGHAI JIAOTONG UNIV
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