scholarly journals Application of Fuzzy Neural Network PID In Laser-guided Mortar Projectile

2021 ◽  
Vol 2113 (1) ◽  
pp. 012078
Author(s):  
Yang Song ◽  
Fangxiu Jia ◽  
Xiaoming Wang ◽  
Dingming Meng ◽  
Lei Zhuang
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Control System ◽  
Pid Control ◽  
Control Algorithm ◽  
Fuzzy Neural Network ◽  
Electromechanical Actuator ◽  
Fuzzy Neural ◽  
The Mathematical Model ◽  
Neural Network Pid

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.

The Application Research of PLC Temperature Control System Based on Fuzzy Neural Network PID

2013 ◽  
Vol 340 ◽  
pp. 517-522
Author(s):  
Yong Wei Lu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Temperature Control ◽  
Control Algorithm ◽  
Fuzzy Neural Network ◽  
Experimental Result ◽  
Temperature Control System ◽  
Pid Algorithm ◽  
Fuzzy Neural ◽  
Neural Network Pid

it is hard to establish the accurate model by using the traditional PID algorithm, and hard to adjust the system parameter in nonlinear system. In order to solve this problem, this paper proposed PID algorithm based on Fuzzy Neural Network. This algorithm combined PID algorithm, fuzzy control algorithm and neural network algorithm together, and formed one kind intelligent control algorithm. This paper designed and researched this algorithm, and applied in the PLC temperature control system. The experimental result indicated that the fuzzy neural network PID controller improved the controller quality, conquered some question such as variable parameter and nonlinear, and enhanced the systems robustness.

Beer Fermentation Temperature Control Curve Optimization Based on the Fuzzy - Neural Network PID Control Algorithm

2013 ◽  
Vol 846-847 ◽  
pp. 325-328
Author(s):  
Xian Qiu Xu
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Control Algorithm ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Response Speed ◽  
Integral Control ◽  
Beer Fermentation ◽  
Fuzzy Neural ◽  
Neural Network Pid

An auto-control model is presented to the process of beer fermentation, which has the characteristics such as time-varying, inertia, time-delay and nonlinear. The traditional PID control is difficult to accurately control. This paper according to the beer fermentation of problems puts forward a new control algorithm: fuzzy-neural network PID control algorithm. The fuzzy logistic differential control and intelligent integral control were supplemented into the fuzzy set-point weight tuning, so that the insufficiency of original PID control method was effectively improved. The advantages of this control algorithm are not only constitute a simple, small static error, dynamic response speed but also the ability to learn, etc. Therefore it not only can strengthen robust and intelligence of the system, but also make design simple and easily be required.

Research on Heavy Rail Pre-Bending Vehicle Control Technology Based on Fuzzy Neural Network

2011 ◽  
Vol 201-203 ◽  
pp. 2379-2384
Author(s):  
Zhi Jun Xu ◽  
Guo Fu Yin
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Algorithm ◽  
Fuzzy Neural Network ◽  
Vehicle Control ◽  
Dynamic Tracking ◽  
Bending Process ◽  
Fuzzy Neural ◽  
Neural Network Pid ◽  
Heavy Rail

The natural cooling after the rolling of the 100-meter-long rail tends to curve the rail from the bottom to the head. Based on the analysis of the rail pre-bending process, this thesis proposes the implementation plan of the pre-bending vehicle control system. The position loop uses fuzzy neural network regulator and the speed loop uses PB-based neural network PID regulator. Also the thesis gives the corresponding control algorithm. The control equipment uses Siemens advanced SIMATIC PCS7 process control system. Through experimental analysis, the dynamic tracking performance and the static accuracy of the system are guaranteed and the vehicle is accurately placed as expected.

Research on Control System for Sluice Gate Flow Based on Fuzzy Neural Network PID

2012 ◽  
Vol 198-199 ◽  
pp. 1779-1782
Author(s):  
Guo Huan Lou ◽  
Kang Wei Li
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Irrigation System ◽  
Good Control ◽  
Control Effect ◽  
Fuzzy Neural ◽  
On Line ◽  
Neural Network Pid

The control of water level and flow for channel irrigation system has nonlinear, time-varying and uncertainty characteristics. It is difficult to get satisfactory effect with traditional PID control. Aim at these features, this paper introduces a control method based on fuzzy neural network PID. This method both has advantage of PID control and has ability of fuzzy neural network self-learning and processing quantitative data. The control method can adjust the parameters of gate flow on-line quickly and efficiently and has good control effect and precision. The simulation results show the validity and correctness of the control method.

scholarly journals Design of an Intelligent Greenhouse Remote Control System Based on a Fuzzy Neural Network

2021 ◽  
Vol 15 (2) ◽  
pp. 243-248
Author(s):  
Yajuan Jia ◽  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Remote Control ◽  
Pid Control ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Fuzzy Pid Control ◽  
Remote Control System ◽  
Temperature And Humidity ◽  
Fuzzy Neural

There are many changing factors in a greenhouse, and the traditional control method has been unable to obtain a good control effect. In this study, focusing on the fuzzy neural network (FNN), the principles of two control methods and the advantages of their combination were analyzed, an intelligent remote control system for a greenhouse based on the FNN that controls the temperature and humidity was designed, and a simulation experiment was performed in the Simulink environment. The results demonstrated that compared with the traditional proportion, integration, differentiation (PID) control system and the genetic algorithm + fuzzy PID control system, the FNN-based system designed in this study achieved better performance in temperature and humidity control. The temperature error of the FNN-based system was smaller than 1◦C, the humidity error was approximately 2%, and the change in the error values was stable. The experimental results verify the reliability of the FNN and provide some theoretical basis for the intelligent control of greenhouses.

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