GA-Based Fuzzy Neural Network for Adaptive Fuzzy Modeling and Control

1998 ◽  
Author(s):  
Tongjian Chen ◽  
Yonghong Peng ◽  
Weiqiang Xie ◽  
Hongming Deng
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Knowledge Base ◽  
Fuzzy Neural Network ◽  
Adaptive Fuzzy Control ◽  
Fuzzy Reasoning ◽  
Reasoning Process ◽  
Adaptive Fuzzy ◽  
Fuzzy Neural

Abstract Fuzzy logic theory has provided a model-free tool to develop intelligent control system for complex industrial processes by means of simulating the fuzzy reasoning process of human being. However, the performance of such a control system depends on the knowledge base (control rules and membership functions of fuzzy sets). For the control of complex industrial process in which the dynamic parameters of process is time-varying and non-linear, it is necessary to modify and optimize the knowledge base on-line. Adaptive fuzzy control provides a efficient approach for this objective. In this paper, a new fuzzy neural network (FNN) and an adaptive learning mechanism based on genetic algorithm has been proposed for modeling the fuzzy reasoning process and constructing an efficient adaptive fuzzy control systems. Experiment results show that the FNN is capable of modeling complex functions and simulating fuzzy reasoning process of human being.

Research on Intelligent Variable Frequency of Air-Conditioner Control System Based on Fuzzy Control

2011 ◽  
Vol 110-116 ◽  
pp. 4076-4084
Author(s):  
Hai Cun Du
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Neural Network ◽  
Air Conditioner ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller ◽  
Fuzzy Neural ◽  
Fuzzy Control Strategy

In this paper, we determine the fuzzy control strategy of inverter air conditioner, the fuzzy control model structure, the neural network and fuzzy control technology, structural design of the fuzzy neural network controller as well as the neural network predictor FNNC NNP. Simulation results show that the fuzzy neural network controller can control the accuracy greatly improved the compressor, and the control system has strong adaptability to achieve a truly intelligent; model of the controller design and implementation of technology are mainly from the practical point of view, which is practical and feasible.

Robust control for permanent magnet in-wheel motor in electric vehicles using adaptive fuzzy neural network with inverse system decoupling

2018 ◽  
Vol 42 (3) ◽  
pp. 286-297 ◽  
Author(s):  
Yong Li ◽  
Bohan Zhang ◽  
Xing Xu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Permanent Magnet ◽  
Fuzzy Neural Network ◽  
Speed Control ◽  
Inverse System ◽  
Adaptive Fuzzy Neural Network ◽  
Adaptive Fuzzy ◽  
Speed Control System ◽  
Fuzzy Neural

To eliminate the chattering phenomenon and effectively enhance the robustness and dynamic response of the speed control system of a permanent magnet in-wheel motor (PMIWM), a novel decoupling approach is proposed. The speed control system of the PMIWM is analyzed and modeled. By introducing the inverse model into the original PMIWM system, a new decoupling pseudo-linear system is established. A control method based on adaptive fuzzy neural network (AFNN) is investigated to obtain an accurate speed trajectory. The inverse system control approach is introduced into the AFNN-based control system. The PMIWM speed is decoupled completely by the proposed adaptive fuzzy neural network inverse (AFNNI) method. Experiments are carried out on a hardware-in-the-loop (HIL) test bench. Compared with traditional PID control scheme, the proposed AFNNI control strategy can realize a better speed control performance and ensure the robust stability of the PMIWM, even though the motor may suffer from both sudden change in velocity and severe variation under different drive cycles.

Fuzzy neural network control algorithm for asymmetric building structure with active tuned mass damper

2020 ◽  
Vol 26 (21-22) ◽  
pp. 2037-2049
Author(s):  
Xiao Yan ◽  
Zhao-Dong Xu ◽  
Qing-Xuan Shi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Linear Quadratic Regulator ◽  
Tuned Mass Damper ◽  
Linear Quadratic ◽  
Mass Damper ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Damper Control

Asymmetric structures experience torsional effects when subjected to seismic excitation. The resulting rotation will further aggravate the damage of the structure. A mathematical model is developed to study the translation and rotation response of the structure during seismic excitation. The motion equations of the structures which cover the translation and rotation are obtained by the theoretical derivations and calculations. Through the simulated computation, the translation and rotation response of the structure with the uncontrolled system, the tuned mass damper control system, and active tuned mass damper control system using linear quadratic regulator algorithm are compared to verify the effectiveness of the proposed active control system. In addition, the linear quadratic regulator and fuzzy neural network algorithm are used to the active tuned mass damper control system as a contrast group to study the response of the structure with different active control method. It can be concluded that the structure response has a significant reduction by using active tuned mass damper control system. Furthermore, it can be also found that fuzzy neural network algorithm can replace the linear quadratic regulator algorithm in an active control system. Because fuzzy neural network algorithm can control the process on an uncertain mathematical model, it has more potential in practical applications than the linear quadratic regulator control method.

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