Kaicheng Xu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Kaicheng Xu has a total of 8 co-authors. Here are the top co-authors by number of shared publications:
- Qixun Zhou
School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
2 shared publications - Guanghui Du
School of Electrical and Control Engineering, Xian University of Science and Technology, Xian, China
1 shared publication - Hao Gong
School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
1 shared publication - Kaiying Gao
School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
1 shared publication - Keke Shi
School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
1 shared publication - Wei Liu
School of Electrical and Control Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
1 shared publication
Latest Publications
- Open Access
- 16 September 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (18) , 8343
Optimized Multi-Motor Power Control Strategy for Distributed Permanent Magnet Direct Drive Belt Conveyors
To address the problem of energy wastage in traditional long-distance belt conveyors with low carrying capacity due to the excessive power installation of a single drive motor, a model of a distributed permanent magnet direct drive belt conveyor driven by multiple small power motors is proposed. A power regulator based on material carrying capacity is designed for energy saving. The minimum number of motors is set as the adjustment target. The number of running motors is adjusted, and the remaining motor power is distributed. The speed constraints of multiple motors are analyzed. The speed difference between adjacent motors is used as feedback to construct a ring coupling speed controller. This realizes the coordinated operation of the distributed permanent magnet direct drive belt conveyor. Co-simulations with AMESIM (version 2020.1) and MATLAB (version 2019b) are conducted to verify the system’s stability. The power regulator optimizes motor distribution under varying conditions, reducing operational losses. The experimental results confirm the effectiveness of the proposed power control strategy.
- 16 September 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (18) , 8343
Open AccessOptimized Multi-Motor Power Control Strategy for Distributed Permanent Magnet Direct Drive Belt Conveyors
To address the problem of energy wastage in traditional long-distance belt conveyors with low carrying capacity due to the excessive power installation of a single drive motor, a model of a distributed permanent magnet direct drive belt conveyor driven by multiple small power motors is proposed. A power regulator based on material carrying capacity is designed for energy saving. The minimum number of motors is set as the adjustment target. The number of running motors is adjusted, and the remaining motor power is distributed. The speed constraints of multiple motors are analyzed. The speed difference between adjacent motors is used as feedback to construct a ring coupling speed controller. This realizes the coordinated operation of the distributed permanent magnet direct drive belt conveyor. Co-simulations with AMESIM (version 2020.1) and MATLAB (version 2019b) are conducted to verify the system’s stability. The power regulator optimizes motor distribution under varying conditions, reducing operational losses. The experimental results confirm the effectiveness of the proposed power control strategy.
- Open Access
- 5 September 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (17) , 7930
Radial Basis Function Neural Network and Feedforward Active Disturbance Rejection Control of Permanent Magnet Synchronous Motor
A composite control strategy is proposed to improve the position-tracking performance and anti-interference capabilities of permanent magnet synchronous motors (PMSMs). This strategy integrates an active disturbance rejection controller (ADRC) and a radial basis function neural network (RBFNN) with feedforward control. Initially, the flexibility and robustness of the ADRC are utilized in the position loop control. Subsequently, the parameters of the extended state observer (ESO) within the ADRC are optimized, benefiting from the fast convergence speed and optimal approximation provided by the RBFNN. To further enhance the dynamic tracking performance, a differential feedforward link is introduced between the desired speed and the output signal. The simulation and experimental results demonstrate that when the expected electrical angle inputs are sinusoidal and pulse signals, the incorporation of the feedforward link and the adjustment of parameters in the ADRC lead to improved position-tracking capabilities and greater adaptability to load disturbances.
- 5 September 2024
- Published by MDPI AG in Applied Sciences
- Vol. 14 (17) , 7930
Open AccessRadial Basis Function Neural Network and Feedforward Active Disturbance Rejection Control of Permanent Magnet Synchronous Motor
A composite control strategy is proposed to improve the position-tracking performance and anti-interference capabilities of permanent magnet synchronous motors (PMSMs). This strategy integrates an active disturbance rejection controller (ADRC) and a radial basis function neural network (RBFNN) with feedforward control. Initially, the flexibility and robustness of the ADRC are utilized in the position loop control. Subsequently, the parameters of the extended state observer (ESO) within the ADRC are optimized, benefiting from the fast convergence speed and optimal approximation provided by the RBFNN. To further enhance the dynamic tracking performance, a differential feedforward link is introduced between the desired speed and the output signal. The simulation and experimental results demonstrate that when the expected electrical angle inputs are sinusoidal and pulse signals, the incorporation of the feedforward link and the adjustment of parameters in the ADRC lead to improved position-tracking capabilities and greater adaptability to load disturbances.