neural network pid
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2021 ◽  
Vol 2113 (1) ◽  
pp. 012078
Author(s):  
Yang Song ◽  
Fangxiu Jia ◽  
Xiaoming Wang ◽  
Dingming Meng ◽  
Lei Zhuang

Abstract Based on the high control performance requirement of laser-guided mortar control system, the permanent magnet synchronous motor (PMSM) is adopted in this paper as the electromechanical actuator of the system, the mathematical model of the motor is analyzed, and the vector control technology is adopted to achieve precise control of position, speed and torque of the electromechanical actuator. Aiming at the characteristics of non-linearity, strong coupling and large parameter changes of the system in flight, an improved fuzzy neural network PID control method is proposed by combining the classical PID control algorithm with fuzzy control and neural network control algorithm to realize the real-time tuning and optimization of PID parameters. The mathematical model of the electromechanical actuator control system is established and simulated. The results show that the fuzzy neural network PID control has good tracking performance, small amplitude error, and strong adaptability to load changes.


2021 ◽  
Author(s):  
Qing Wang ◽  
Zongquan Jiang ◽  
Yuanchang He ◽  
Lili Zhou ◽  
Li Geng

2021 ◽  
pp. 338-348
Author(s):  
Moudao Li ◽  
Jun Wang ◽  
Ruiquan Lin ◽  
Sheng Liu

Author(s):  
Mohamed Benrabah ◽  
Kamel Kara ◽  
Oussama AitSahed ◽  
Mohamed Laid Hadjili

Author(s):  
Jingtian Xu ◽  
yanli qiao

Abstract: The Hanqu Joint Station of the Dingbian Oil Production Plant of Yanchang Oilfield Co., Ltd is located at the edge of the desert in northern China. the bad field conditions and strong sandstorm, the hardware of computer monitoring system of Joint Station is often damaged. At the same time, the core equipment of the joint station three-phase separator oil chamber liquid level is hard to achieve high precision constant value control, the general control algorithm is difficult to meet the control requirements.This paper proposed a design scheme of a oilfield joint station computer monitoring system based on the Siemens S7-300 PLC, the hardware of the monitoring system adopts the redundancy scheme of dual monitoring computers, dual programmable logic controllers (PLCs), and dual industrial Ethernet. The BP neural network PID control algorithm was used to realize constant value control of the oil chamber liquid level of the three-phase separator of the core equipment of the joint station,and realized high control precision. The monitoring system could well adapt to the harsh environment of the scene, and showed high reliability and efficiency.


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