Spacecraft's automatic landing control based on online tracing identification method of neural network

Mapping Intimacies ◽

10.1117/12.775005 ◽

2007 ◽

Author(s):

Jianling Zhang ◽

Jinwen An

Keyword(s):

Neural Network ◽

Identification Method ◽

Automatic Landing ◽

Landing Control

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Automatic Landing Control System Design Using Adaptive Neural Network and Its Hardware Realization

IEEE Systems Journal ◽

10.1109/jsyst.2011.2134490 ◽

2011 ◽

Vol 5 (2) ◽

pp. 266-277 ◽

Author(s):

Jih-Gau Juang ◽

Li-Hsiang Chien ◽

F Lin

Keyword(s):

Neural Network ◽

Control System ◽

System Design ◽

Control System Design ◽

Adaptive Neural Network ◽

Automatic Landing ◽

Hardware Realization ◽

Landing Control

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Application of time delay neural network to automatic landing control

Proceedings of the International Conference on Control Applications ◽

10.1109/cca.2002.1040177 ◽

2003 ◽

Author(s):

Jih-Gau Juang ◽

Hao-Hsiang Chang

Keyword(s):

Neural Network ◽

Automatic Landing ◽

Landing Control

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Formal Verification of Helicopter Automatic Landing Control Algorithm in Theorem Prover Coq

10.23940/ijpe.18.09.p2.19471957 ◽

2018 ◽

Author(s):

Xi Chen

Keyword(s):

Formal Verification ◽

Control Algorithm ◽

Theorem Prover ◽

Automatic Landing ◽

Landing Control

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External Fault Identification Method of High Voltage Transmission Line Based on Partial Supervision Convolutional Neural Network

2020 IEEE International Conference on High Voltage Engineering and Application (ICHVE) ◽

10.1109/ichve49031.2020.9279639 ◽

2020 ◽

Author(s):

Xianzhe Wei ◽

Wenbin Zhao ◽

Wu Lu

Keyword(s):

Neural Network ◽

Transmission Line ◽

Convolutional Neural Network ◽

High Voltage ◽

Fault Identification ◽

High Voltage Transmission Line ◽

Identification Method ◽

High Voltage Transmission ◽

Partial Supervision

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Damage identification method of prestressed concrete beam bridge based on convolutional neural network

Neural Computing and Applications ◽

10.1007/s00521-020-05052-w ◽

2020 ◽

Author(s):

Sanqiang Yang ◽

Yong Huang

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Prestressed Concrete ◽

Concrete Beam ◽

Damage Identification ◽

Identification Method ◽

Prestressed Concrete Beam ◽

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A dynamic load identification method for rock roadheaders based on wavelet packet and neural network

2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA) ◽

10.1109/iciea.2015.7334193 ◽

2015 ◽

Author(s):

Mu-qin Tian ◽

Wei Wang ◽

Jian-cheng Song ◽

Yuan Song ◽

Lin Yan ◽

...

Keyword(s):

Neural Network ◽

Dynamic Load ◽

Wavelet Packet ◽

Load Identification ◽

Identification Method

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Black-Box Modeling of Ship Maneuvering Motion Using System Identification Method Based on Neural Network

10.1115/1.0001288v ◽

2021 ◽

Author(s):

HONGWEI HE ◽

Zaojian Zou

Keyword(s):

Neural Network ◽

System Identification ◽

Black Box ◽

Ship Maneuvering ◽

System Identification Method ◽

Identification Method ◽

Maneuvering Motion

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Automatic Landing Control of Unmanned Helicopter on Ship Deck with Heave Motion Using Robust Output Regulation

2018 37th Chinese Control Conference (CCC) ◽

10.23919/chicc.2018.8482701 ◽

2018 ◽

Author(s):

Shuai Mi ◽

Geng Lu ◽

Zongying Shi ◽

Yisheng Zhong

Keyword(s):

Output Regulation ◽

Unmanned Helicopter ◽

Automatic Landing ◽

Robust Output Regulation ◽

Heave Motion ◽

Landing Control

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CAT III Autoland Control Laws Design Based on Multi-Objective Optimization

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.452-453.548 ◽

2012 ◽

Vol 452-453 ◽

pp. 548-552 ◽

Author(s):

Hui Jie Li ◽

Ling Yu Yang ◽

Gong Zhang Shen

Keyword(s):

Monte Carlo Simulation ◽

Monte Carlo ◽

Multi Objective Optimization ◽

Control Laws ◽

Multi Objective ◽

Automatic Landing ◽

Simulation Results ◽

Free Parameters ◽

Structure Measurement ◽

Landing Control

The CAT III longitudinal automatic landing control laws based on multi-objective optimization is discussed. Firstly summarized the CAT III airworthiness criteria and transformed into the specifications of control system. The configuration of the longitudinal automatic landing controllers is proposed secondly and multi-objective optimization is used to tradeoff free parameters of the controllers. The Monte Carlo simulation results show the designed control laws fulfill the CAT III requirements, when there are uncertainties of structure, measurement error and disturbances.

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Wind Shear Encountered Landing Control Based on CMACs

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.284-287.2351 ◽

2013 ◽

Vol 284-287 ◽

pp. 2351-2355 ◽

Author(s):

Jih Gau Juang ◽

Chung Ju Cheng ◽

Teng Chieh Yang

Keyword(s):

Intelligent Control ◽

Adaptive Learning ◽

Learning Rule ◽

Lyapunov Theory ◽

Intelligent Control System ◽

Automatic Landing ◽

Control Scheme ◽

Shear Condition ◽

Landing Control

This paper presents an intelligent control scheme that uses different cerebellar model articulation controllers (CMACs) in aircraft automatic landing control. The proposed intelligent control system can act as an experienced pilot and guide the aircraft landed safely in wind shear condition. Lyapunov theory is applied to obtain adaptive learning rule and stability analysis is also provided. Furthermore, the proposed controllers are implemented in a DSP. The simulations by MatLab are demonstrated.

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