Magnetic Levitation Belt Conveyor Control System Based on Multi-Sensor Fusion

Belt conveyors are critical in coal transportation systems; however, they are supported by rollers, which incur high energy costs. Magnetic levitation support is adopted. The measurement of a magnetic levitation air gap is important, and a displacement sensor is usually used in real-time. To address...

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Bibliographic Details
Published inApplied sciences Vol. 13; no. 13; p. 7513
Main Authors Hu, Kun, Jiang, Hao, Zhu, Qinqin, Qian, Wangqian, Yang, Jinhan
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.07.2023
Subjects
Algorithms
belt conveyor
Belt conveyors
Cameras
Coal mining
Coal transport
Control systems
Control theory
Conveying machinery
Data integration
Electromagnets
Energy consumption
Equilibrium
Error reduction
Extended Kalman filter
Image processing
maglev air gap
Magnetic levitation
Magnetic levitation systems
Mathematical models
multi-sensor fusion
Multisensor fusion
Photoelectricity
Position sensing
Power supply
Sensors
Support systems
Systems stability
Transportation systems
Vision systems
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Summary:Belt conveyors are critical in coal transportation systems; however, they are supported by rollers, which incur high energy costs. Magnetic levitation support is adopted. The measurement of a magnetic levitation air gap is important, and a displacement sensor is usually used in real-time. To address the problems of poor linearity and low measurement stability of single displacement sensors in magnetic levitation systems, a magnetic levitation support system based on multi-sensor data fusion is proposed. First, an industrial camera sensor is used to collect images, and the suspended air gap is obtained through image processing. Second, using an extended Kalman filter, the photoelectric position sensor and industrial camera sensor are fused to the suspension air gap value in the process of suspension. The electromagnet current is controlled by the PID (proportion integration differentiation) control algorithm, and experiments are carried out. Experimental results showed that, compared with an estimation of the suspension state of two sensors before and after EKF fusion, the root mean square error is reduced by 0.0597 and 0.0081, respectively, compared with the measurement value of a single sensor. Moreover, the fusion data were more robust, thereby meeting the requirements of magnetic levitation control.
ISSN:2076-3417
2076-3417
DOI:10.3390/app13137513