Research on the Fuzzy Neural Network PID Control of Load Simulator Based on Friction Torque Compensation

2014 ◽  
Author(s):  
Zhisheng Ni ◽  
Mingyan Wang
Keyword(s):  
Neural Network ◽  
Pid Control ◽  
Fuzzy Neural Network ◽  
Friction Torque ◽  
Fuzzy Neural ◽  
Load Simulator ◽  
Neural Network Pid

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 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 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.

scholarly journals Fuzzy Neural Network PID–based constant deceleration compensation device for the brakes of mining hoists

2020 ◽  
Vol 12 (7) ◽  
pp. 168781402093756
Author(s):  
Chi Ma ◽  
Suzhi Tian ◽  
Xinming Xiao ◽  
Yuqiang Jiang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Lower Cost ◽  
Constant Torque ◽  
Fuzzy Neural ◽  
Safety And Reliability ◽  
Complete Set ◽  
Compensation Device ◽  
Short Time ◽  
Neural Network Pid

In comparison with constant torque brakes, constant deceleration brakes are more advantageous for the safety of mining hoists, but complete set of such products manufactured by big companies are not what ordinary mining enterprises can afford. As an alternative solution, this article develops a constant deceleration compensation device, which adds the function of constant deceleration brake onto the original brakes. Control strategy based on Fuzzy Neural Network PID is investigated and simulated with the combination of AMEsim and Simulink. An actual device is built and tested in real industrial field. The application illustrates the feasibility of this constant deceleration compensation device, which can achieve constant decelerations within a very short time. This device will prevent dangerous decelerations from happening to hoists at a much lower cost, and greatly improve the safety and reliability of mining hoists.

Torque Control of Servo Load Simulator with Generalized Dynamic Fuzzy Neural Network Based on Grey Prediction

2011 ◽  
Vol 148-149 ◽  
pp. 707-712
Author(s):  
Li Wang ◽  
Lin Fang Qian ◽  
Qi Guo
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Learning Algorithm ◽  
Torque Control ◽  
Grey Prediction ◽  
Neural Network Controller ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Load Simulator ◽  
Load Simulation

Considering the testing requirements of dynamically loaded about servo system in weapons, a load simulator is presented in this paper and the transfer function of “extraneous torque" is obtained. In order to curb the amplitude of extra torque and achieve dynamic load simulation, this paper proposes a grey prediction-based fuzzy neural network controller, which selects Generalized Dynamic Fuzzy Neural Network as the learning algorithm and selects the ε-completeness as a criterion to determine the width of Gaussian functions. Simulation and experimental results show that the proposed torque controller can significantly reduce the amplitude of the extra torque.

Sign in / Sign up

Export Citation Format

Share Document