Method for Stabilizing the Operation of Synchronous Machines Using a Virtual Load Sensor

Kryukov, O. V.; Gulyaev, I. V.; Teplukhov, Dmitriy

doi:10.3103/s1068371219070083

Cited by 22 publications

(8 citation statements)

References 2 publications

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“…Some functions of the system. Neural networks have some characteristics that are particularly suitable for control, such as nonlinear processing capability, parallel processing capability, learning capability, and adaptive capability [8].…”

Section: Introductionmentioning

confidence: 99%

Online Control Method of Small- and Medium-Sized Electromechanical Equipment Based on Deep Neural Network

Yang¹,

Deng²,

Zhizheng³

2022

Journal of Robotics

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In order to improve the online control effect of small- and medium-sized electromechanical equipment, this paper studies the online control method of small- and medium-sized electromechanical equipment combined with the deep audit network. Moreover, starting from the signal basis of online control signal recognition and channel coding recognition of electromechanical equipment, this paper determines the type of online control signal and channel coding type of electromechanical equipment required for the experiment. This paper compares the signals under different types of online control signals for electromechanical devices and clarifies the differences in amplitude, frequency, phase, and spectrum in time-domain waveforms. In addition, through the elaboration of different channel coding theories, this paper clarifies the differences in coding methods. Through the experimental research, it can be seen that the online control effect of the small- and medium-sized electromechanical equipment based on the deep neural network proposed in this paper is very good.

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Section: Introductionmentioning

confidence: 99%

Online Control Method of Small- and Medium-Sized Electromechanical Equipment Based on Deep Neural Network

Yang¹,

Deng²,

Zhizheng³

2022

Journal of Robotics

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“…e centralized body control system greatly strengthens the control functions of humanization, comfort, and safety, and the fault diagnosis of the electric equipment of the whole vehicle is also simplified. Reasonable use, under the premise of the same technical requirements, can reduce the overall cost of the body control system [8]. Of course, there are downsides to centralized body control systems.…”

Section: Introductionmentioning

confidence: 99%

Application of Intelligent Signal Reflection in the Communication Model of an Electronic Controller

Lan¹

2022

Journal of Robotics

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This paper analyses the automatic control effect of an electronic controller, combined with artificial intelligence technology in order to improve the automatic control of an electronic controller. In order to improve the automatic control effect of the electronic controller, a novel centralized intelligent reflective surface-assisted millimeter-wave computational imaging scheme based on pixel block division and block sparse signal recovery is proposed in this paper. Meanwhile, this paper proposes a fast block-sparse Bayesian learning algorithm. It combines the GAMP algorithm with machine learning, so it can achieve similar performance with much lower computational complexity than the traditional block sparse Bayesian learning algorithm. The simulation clustering study shows that the electronic controller automation system based on artificial intelligence technology proposed in this paper can effectively improve the control effect of the electronic controller.

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“…Starting from its own stability, reliability, power supply, data acquisition accuracy, etc. [16]; in addition to sensors, there must be special requirements for the customization of the control cabinet, because there is a pressure difference in the mine, the selection of the control cabinet is also a challenge [17].…”

Section: Introductionmentioning

confidence: 99%

Verification of the Improvement Effect of Artificial Intelligence Computer Control Technology on Electrical Automation Control

2022

Advances in Multimedia

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In order to improve the effect of electrical automation control, this paper combines the intelligent computer technology to construct the electrical automation control system, explores the principle of the active disturbance rejection controller, and applies it to the induction motor vector control system. The active disturbance rejection controller estimates the state variables of the system and their changing trends in real time through the extended state observer, compensates them by nonlinear feedback method, and provides appropriate control signals. In addition, this paper builds the induction motor active disturbance rejection control system based on ant colony algorithm and conducts a lot of simulation analysis. The research shows that the electrical automatic control system based on the intelligent computer algorithm proposed in this paper has a good electrical automatic control effect.

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Method for Stabilizing the Operation of Synchronous Machines Using a Virtual Load Sensor

Cited by 22 publications

References 2 publications

Online Control Method of Small- and Medium-Sized Electromechanical Equipment Based on Deep Neural Network

Online Control Method of Small- and Medium-Sized Electromechanical Equipment Based on Deep Neural Network

Application of Intelligent Signal Reflection in the Communication Model of an Electronic Controller

Verification of the Improvement Effect of Artificial Intelligence Computer Control Technology on Electrical Automation Control

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