A Novel Architecture for Civil Aviation Aircraft Intelligent Landing Using Dual Fuzzy Neural Network

2013 ◽  
Vol 313-314 ◽  
pp. 1385-1388
Author(s):  
Kai Jun Xu
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Networks ◽  
Network Control ◽  
Civil Aviation ◽  
Pitch Control ◽  
Roll Control ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Landing Control ◽  
Neural Network Controllers

This paper presents a novel architecture of intelligent landing control of an airplane using dual fuzzy neural networks, including roll control, pitch control and altitude hold control. The neural network control has been implemented in MATLAB and the data for training have been taken from Flight Gear Simulator. The flight performance has been shown in the Flight Gear Simulator. The objective is to improve the performance of conventional landing, roll, pitch and altitude hold controllers. Simulated results show that control for different flight phases is successful and the neural network controllers provide the robustness to system parameter variation.

Decisional Autonomy of Approach and Landing Phase for Civil Aviation Aircraft using Dual Fuzzy Neural Network

2012 ◽  
Vol 476-478 ◽  
pp. 936-939 ◽  
Author(s):  
Kai Jun Xu
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Gradient Descent ◽  
Fuzzy Neural Network ◽  
Error Function ◽  
Civil Aviation ◽  
Learning Rates ◽  
The Neural Network ◽  
Automatic Landing ◽  
Fuzzy Neural

This paper presents the dual fuzzy neural network, designed the decisional autonomy flight controller for civil aviation aircraft in approach and landing phase. Real-time learning method was applied to train the neural network using the gradient-descent of an error function to adaptively update weights. Adaptive learning rates were obtained through the analysis of Lyapunov stability to guarantee the convergence of learning. Conventional automatic landing system (ALS) can provide a smooth landing, which is essential to the comfort of passengers. However, these systems work only within a specified operational safety envelope. When the conditions are beyond the envelope, such as turbulence or wind shear, they often cannot be used. The objective of this paper is to investigate the use of dual fuzzy neural network in ALS and to make that system more intelligent.

Fuzzy Neural Network Based Coal-Rock Interface Recognition

2010 ◽  
Vol 44-47 ◽  
pp. 1402-1406
Author(s):  
Jian Jun Shi ◽  
La Wu Zhou ◽  
Ke Wen Kong ◽  
Yi Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Neural Networks ◽  
Signal Process ◽  
Network Training ◽  
The Neural Network ◽  
Fuzzy Neural ◽  
Rock Interface ◽  
Coal Rock ◽  
Hidden Layer

. In the coal-rock interface recognition (CIR) technology, signal process and recognition are the key parts. A method for CIR based on BP neural networks and fuzzy technique was proposed in this paper. By using the trail-and-error, the hidden layer dimension of the network was decided. Also the network training and weight modification were studied. In order to get a higher identification ratio, fuzzy neural networks (FNN) based data fusion was studied. For CIR, the structure and algorithm of FNN were determined. The results indicated that the test data can be used to train and simulate with the neural network and FNN. And the proposed method can be used in CIR with a higher recognition ratio.

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.

scholarly journals Lookup Table and Neural Network Hybrid Strategy for Wind Turbine Pitch Control

2021 ◽  
Vol 13 (6) ◽  
pp. 3235
Author(s):  
J. Enrique Sierra-García ◽  
Matilde Santos
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Control Strategy ◽  
Neural Control ◽  
Mapping Function ◽  
Energy System ◽  
Lookup Table ◽  
Hybrid Strategy ◽  
Pitch Control ◽  
The Neural Network

Wind energy plays a key role in the sustainability of the worldwide energy system. It is forecasted to be the main source of energy supply by 2050. However, for this prediction to become reality, there are still technological challenges to be addressed. One of them is the control of the wind turbine in order to improve its energy efficiency. In this work, a new hybrid pitch-control strategy is proposed that combines a lookup table and a neural network. The table and the RBF neural network complement each other. The neural network learns to compensate for the errors in the mapping function implemented by the lookup table, and in turn, the table facilitates the learning of the neural network. This synergy of techniques provides better results than if the techniques were applied individually. Furthermore, it is shown how the neural network is able to control the pitch even if the lookup table is poorly designed. The operation of the proposed control strategy is compared with the neural control without the table, with a PID regulator, and with the combination of the PID and the lookup table. In all cases, the proposed hybrid control strategy achieves better results in terms of output power error.

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