scholarly journals Single-Phase Photovoltaic Grid-Connected Inverter Based on Fuzzy Neural Network

2021 ◽  
Vol 25 (3) ◽  
pp. 310-316
Author(s):  
Shenping Xiao ◽  
Zhouquan Ou ◽  
Junming Peng ◽  
Yang Zhang ◽  
Xiaohu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Single Phase ◽  
Fuzzy Inference ◽  
Learning Ability ◽  
Proportional Integral ◽  
Fuzzy Neural ◽  
Grid Connected Inverter ◽  
Self Learning

Based on a single-phase photovoltaic grid-connected inverter, a control strategy combining traditional proportional–integral–derivative (PID) control and a dynamic optimal control algorithm with a fuzzy neural network was proposed to improve the dynamic characteristics of grid-connected inverter systems effectively. A fuzzy inference rule was established after analyzing the proportional, integral, and differential coefficients of the PID controller. A fuzzy neural network was applied to adjust the parameters of the PID controller automatically. Accordingly, the proposed dynamic optimization algorithm was deduced in theory. The simulation and experimental results showed that the method was effective in making the system more robust to external disruption owing to its excellent steady-state adaptivity and self-learning ability.

The Newly AND-OR FNN Modeling and Application

2012 ◽  
Vol 433-440 ◽  
pp. 846-852
Author(s):  
Jiang Hua Sui ◽  
Qiang Ma
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Fuzzy Inference ◽  
Self Organization ◽  
Rule Base ◽  
The Novel ◽  
Fuzzy Neural ◽  
Ship Control ◽  
Self Learning ◽  
Better Than

The novel multilayer feed-forward AND-OR fuzzy neural network (AND-OR FNN) is proposed in this paper. The main feature is shown not only in reducing the input space by special inner structure of neurons, but also auto-extracting the rules by the structure self-organization and parameter self-learning. The equivalent is proved that the network structure and fuzzy inference. The whole structure of network is optimized by genetic algorithm to extract if-then rules. This designing approach is employed to modeling an AND-OR FNN controller for ship control. Simulated results demonstrate that the number of rule base is decreased remarkably and the performance is much better than ordinary fuzzy control, illustrate the approach is practicable, simple and effective.

Self-Learning Control of PMA-Actuated Knee-Joint Rehabilitation Training Device Based on Fuzzy Neural Network

2011 ◽  
Vol 467-469 ◽  
pp. 1645-1650
Author(s):  
Xiao Li ◽  
Xia Hong ◽  
Ting Guan
Keyword(s):  
Neural Network ◽  
Knee Joint ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Learning Control ◽  
Training Device ◽  
Rehabilitation Training ◽  
Fuzzy Neural ◽  
Self Learning

To solve the problem of the delay, nonlinearity and time-varying properties of PMA-actuated knee-joint rehabilitation training device, a self-learning control method based on fuzzy neural network is proposed in this paper. A self-learning controller was designed based on the combination of pid controller, feedforward controller, fuzzy neural network controller, and learning mechanism. It was applied to the isokinetic continuous passive motion control of the PMA-actuated knee-joint rehabilitation training device. The experiments proved that the self-learning controller has the properties of high control accuracy and unti-disturbance capability, comparing with pid controller. This control method provides the beneficial reference for improving the control performance of such system.

scholarly journals Online Tuning of PID Controller Using a Multilayer Fuzzy Neural Network Design for Quadcopter Attitude Tracking Control

2021 ◽  
Vol 14 ◽  
Author(s):  
Daewon Park ◽  
Tien-Loc Le ◽  
Nguyen Vu Quynh ◽  
Ngo Kim Long ◽  
Sung Kyung Hong
Keyword(s):  
Neural Network ◽  
Network Design ◽  
Attitude Control ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Learning Ability ◽  
System Stability ◽  
Effective Control ◽  
Fuzzy Neural ◽  
Online Tuning

This study presents an online tuning proportional-integral-derivative (PID) controller using a multilayer fuzzy neural network design for quadcopter attitude control. PID controllers are simple but effective control methods. However, finding the suitable gain of a model-based controller is relatively complicated and time-consuming because it depends on external disturbances and the dynamic modeling of plants. Therefore, the development of a method for online tuning of quadcopter PID parameters may save time and effort, and better control performance can be achieved. In our controller design, a multilayer structure was provided to improve the learning ability and flexibility of a fuzzy neural network. Adaptation laws to update network parameters online were derived using the gradient descent method. Also, a Lyapunov analysis was provided to guarantee system stability. Finally, simulations concerning quadcopter attitude control were performed using a Gazebo robotics simulator in addition to a robot operating system (ROS), and their results were demonstrated.

Study of the Engineering Vehicles of Automatic Transmission Shift Schedule Based on Fuzzy Neural Network

2012 ◽  
Vol 591-593 ◽  
pp. 1720-1723 ◽  
Author(s):  
Yong Jing Huang ◽  
Jin Yao ◽  
Jia Hua Han ◽  
Di Wu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Automatic Transmission ◽  
Learning Ability ◽  
Shift Schedule ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Set Up ◽  
Takagi Sugeno ◽  
Self Learning

By combining the powerful self-learning ability of the neural network and the characteristic that the fuzzy control is designed based on the strategical rules of knowledge and language, this paper put forward the strategy of engineering vehicles' automatic transmission shift. According to a large number of experimental data, as well as the drivers' experience and the experts' profession, this paper put forward the strategy of engineering vehicles’ automatic transmission shift. The neural network model is set up based on Takagi-Sugeno and the factual cases are used to train and exam by MATLB, the simulation result showed that this method is feasible and meet the shift requirement, as it can accelerate effectively the establishment of the rules and reduce the set up time. The shift schedule can reflect precisely the actual out put target gear and meet the shift requirement.

Diagnose Expert System of Engine Based on Fuzzy Neural Network

2012 ◽  
Vol 588-589 ◽  
pp. 1472-1475
Author(s):  
Miao Tian
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Expert System ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural Networks ◽  
Learning Ability ◽  
Fuzzy Neural ◽  
Enhance Efficiency ◽  
Self Learning

Engine has a high chance of failure, it usually accounts for about 40% of vehicle failures. Study expert system of engine fault diagnosises that it can locate fault timely and accurately, and enhance efficiency. However, the traditional expert system has shortcomings so as inefficient inference and poor self-learning capability. The fuzzy logic and traditional neural networks are combined to form fuzzy neural networks, they are established a model of fuzzy neural network (FNN) of fault diagnosis, and that the model is applied to engine fault diagnosis, complementary advantages, to effectively enhance efficiency of inference and self-learning ability, its performance is higher than the traditional BP network.

Self-Learning Control of Hydraulic Injection Molding Machine Based on Fuzzy Neural Network

2011 ◽  
Vol 66-68 ◽  
pp. 1117-1121
Author(s):  
Xiao Li ◽  
Xiao Hua Yang
Keyword(s):  
Neural Network ◽  
Injection Molding ◽  
Pid Controller ◽  
High Speed ◽  
Fuzzy Neural Network ◽  
Learning Control ◽  
Molding Machine ◽  
Injection Molding Machine ◽  
Fuzzy Neural ◽  
Self Learning

Considering the nonlinear and time-variable characteristics of the injection cylinder system of hydraulic injection molding machine (HIMM), a self-learning control method based on fuzzy neural network is proposed in this paper. A self-learning controller was designed based on the combination of PID controller, fuzzy neural network controller, and learning mechanism. It was applied to the position control of injection cylinder. The experimental results show that the controller has the property of higher position tracking accuracy under the high speed and variable track movement of injection cylinder, compared with PID controller. The conclusion of this research can provide the beneficial reference for designing high speed and high precision HIMM.

Research on Neural Network Control Technology for Satellite-Based Laser Communication Tracking Turntable

2014 ◽  
Vol 945-949 ◽  
pp. 2266-2271
Author(s):  
Li Hua Wang ◽  
Xiao Qiang Wu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Pid Controller ◽  
Dynamic Performance ◽  
Mathematic Model ◽  
Network Control ◽  
Learning Ability ◽  
Space Environment ◽  
Laser Communication ◽  
Self Learning

In space laser communication tracking turntable work environment characteristics, we design a neural network PID control system which makes the system’s parameter self-tuning. The control system cans self-tune parameters under the changes of the object’ mathematic model, it solves the problem for the control object’s model changes under the space environment. It also looks for method for optimum control through the function of neural network's self-learning in order to solve the problem of the precision’s decline which arouse from vibration and disturbance. The simulation experiments confirmed the self-learning ability of neural network, and described the neural PID controller dynamic performance is superior to the classical PID controller through the output characteristic curves contrast.

Development and Research of Intelligent PID Controller Based on Fuzzy Neural Network

2014 ◽  
Vol 644-650 ◽  
pp. 341-345
Author(s):  
Ming Jun Li ◽  
Hua Tian ◽  
Xiao Jing Guo
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Pid Controller ◽  
Fuzzy Neural Network ◽  
Network System ◽  
Validity And Reliability ◽  
Fuzzy Neural ◽  
Neural Network System ◽  
Intelligent Pid ◽  
Matlab Programming

In this paper, intelligent fuzzy control theory is introduced in the model of neural network algorithm, and the neural network system is improved by the PID controller, which has realized the feedback and adjustment function of neural network system, and has made the reaction of the system be more accurate and stable. In order to verify the validity and reliability of the designed intelligent control PID algorithm based on the fuzzy neural network in this paper, the algorithm is carried on the programming by using Matlab programming software, and the control process of PID is calculated by NNbox simulation toolbox, at last, it has obtained the curve of PID control response changing over time. From the response curve, it can be seen that after the PID proportional coefficient is regulated by using fuzzy neural network intelligent control algorithm, it can quickly and steadily obtain the control curve, which has realized better intelligent control effect, and has provided technical reference for the research of intelligent PID controller.

Fuzzy Neural Network Hybrid Learning Control on AUV

2012 ◽  
Vol 468-471 ◽  
pp. 1732-1735
Author(s):  
Jing Zhao ◽  
Zhao Lin Han ◽  
Yuan Yuan Fang
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Autonomous Underwater Vehicle ◽  
Learning Algorithm ◽  
Hybrid Learning ◽  
Network Control ◽  
Learning Ability ◽  
B Spline ◽  
Hybrid Learning Algorithm ◽  
Fuzzy Neural

A novel controller based on the fuzzy B-spline neural network is presented, which combines the advantages of qualitative defining capability of fuzzy logic, quantitative learning ability of neural networks and excellent local controlling ability of B-spline basis functions, which are being used as fuzzy functions. A hybrid learning algorithm of the controller is proposed as well. The results show that it is feasible to design the fuzzy neural network control of autonomous underwater vehicle by the hybrid learning algorithm.

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