Abstract
This paper presents a novel diagonal recurrent neural network hybrid controller based on the shared memory of real-time database structure. The controller uses Data Engine (DE) technology, through the establishment of a unified and standardized software architecture and real-time database in different control stations, effectively solves many problems caused by technical standard, communication protocol, and programming language in actual industrial application: the advanced control algorithm and control system co-debugging difficulties, algorithm implementation and update inefficiency, and high development and operation and maintenance costs effectively fill the current technical gap. More importantly, the control algorithm development uses a unified visual graphics configuration programming environment, effectively solving the problem of integrated control of heterogeneous devices; and has the advantages of intuitive configuration and transparent data processing process, reducing the difficulty of the advanced control algorithms debugging in engineering applications. In this paper, the application of a neural network hybrid controller based on DE in motor speed measurement and control system shows that the system has excellent control characteristics and anti-disturbance ability, and provides an integrated method for neural network control algorithm in a practical industrial control system, which is the major contribution of this article.
Highlights
In the complicated industrial systems, the control object model of the system is often unpredictable because of the frequent interference of the external environment, the input condition variable, and the high coupling degree and time variation
This study proposes a novel Diagonal Recurrent Neural Network (DRNN) hybrid controller based on real-time database structure of memory, using the Data Engine (DE) technology to build the algorithm execution mechanism, innovatively put forward the concept of advanced control execution carrier transformation, and realize the implementation of new techniques of neural network control algorithm in low computing performance controllers
Objective of the Control Method industrial control system: advanced control is integrated into the configuration software IAPlogic of thecontrol currentsystem advanced control uses independent software system, through of theMost industrial based on thetechnology unified real-time shared memory by advanced control implementing data communication with the system, and the advanced control algorithm configuration components, the management, and configuration of advanced control components are and its execution often present the ‘black box’ feature, makes itinterface difficultisfor users in to in accordance withprocess the conventional control components
Summary
In the complicated industrial systems, the control object model of the system is often unpredictable because of the frequent interference of the external environment, the input condition variable, and the high coupling degree and time variation. This study proposes a novel Diagonal Recurrent Neural Network (DRNN) hybrid controller based on real-time database structure of memory, using the DE technology to build the algorithm execution mechanism, innovatively put forward the concept of advanced control execution carrier transformation, and realize the implementation of new techniques of neural network control algorithm in low computing performance controllers. Through a lot of high speed measurement and controlled experimentation, it is proven that the neural network control configuration components integrated into the configuration software can better improve the advanced control strategies and control station of the communication efficiency, improve the real-time performance of the control strategy of online operation, and provide scientific validation experiments for practical engineering application.
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