Geometric analysis of flight control command for tactical missile guidance

2001 ◽  
Vol 9 (2) ◽  
pp. 234-243 ◽  
Author(s):  
C.-Y. Kuo ◽  
D. Soetanto ◽  
Ying-Chwan Chiou
Keyword(s):  
Flight Control ◽  
Geometric Analysis ◽  
Missile Guidance ◽  
Control Command

scholarly journals Synthesis and Verification of Finite-Time Rudder Control with GA Identification for Electric Rudder System

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1379
Author(s):  
Zhihong Wu ◽  
Ruifeng Yang ◽  
Chenxia Guo ◽  
Shuangchao Ge ◽  
Xiaole Chen
Keyword(s):  
Control System ◽  
Los Angeles ◽  
Flight Control ◽  
Finite Time ◽  
Static Characteristic ◽  
Physical Parameters ◽  
Flight Control System ◽  
Control Command ◽  
Control Accuracy

The electric rudder system (ERS) is the executive mechanism of the flight control system, which can make the missile complete the route correction according to the control command. The performance and quality of the ERS directly determine the dynamic quality of the flight control system. However, the transient and static characteristic of ERS is affected by the uncertainty of physical parameters caused by nonlinear factors. Therefore, the control strategy based on genetic algorithm (GA) identification method and finite-time rudder control (FTRC) theory is studied to improve the control accuracy and speed of the system. Differently from the existing methods, in this method, the difficulty of parameter uncertainty in the controller design is solved based on the ERS mathematical model parameter identification strategy. Besides, in this way, the performance of the FTRC controller was verified by cosimulation experiments based on automatic dynamic analysis of mechanical systems (ADAMS) (MSC software, Los Angeles, CA, USA) and matrix laboratory (MATLAB)/Simulink (MathWorks, Natick, MA, USA). In addition, the advantages of the proposed method are verified by comparing with the existing strategy results on the rudder test platform, indicating that the control accuracy is improved by 70% and the steady-state error is reduced by at least 50%.

scholarly journals Fault tolerant control of uav with wing layout based on control allocation

2021 ◽  
Vol 233 ◽  
pp. 04008
Author(s):  
Chen Jie ◽  
LIN Jianxin
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Flight Control ◽  
Fault Tolerant ◽  
Fault Tolerant Control ◽  
Control Allocation ◽  
Longitudinal Motion ◽  
Actuator Failure ◽  
Simulink Simulation ◽  
Control Command ◽  
Aerial Vehicle

As the flying wing layout unmanned aerial vehicle (uav) extensive research and task environment increasingly complex, Yu Feiyi layout unmanned aerial vehicle (uav) for fault tolerant control gradually become the main technical means of the flight control, using the established mathematical model of the flying wing uav longitudinal layout setting the actuator failure effect, is in the nature of adaptive control allocation fault-tolerant algorithm is given, and MATLAB/simulink simulation is carried out for uav longitudinal motion, realize the rapid and stable, the control command and response to complete the nonlinear fault-tolerant control of flying wing uavs.

Image Quality-Driven Octorotor Flight Control via Reinforcement Learning

2018 ◽  
Author(s):  
Qiang Li ◽  
Yunjun Xu
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Reinforcement Learning ◽  
Flight Control ◽  
Learning Method ◽  
Satisfactory Performance ◽  
Control Command ◽  
Command Generation ◽  
Learning Technique ◽  
The Relationship

This article presents the design of a reinforcement learning method based flight controller to enhance the qualities of image taken from an octorotor platform. Concerning the effect of a low resolution and a high blur rate of target images on feature extraction and target detection, we started by analyzing the relationship between these two kinds of image qualities and altitude and velocity of the octorotor. This leads to the generation of corresponding control commands. We then applied a reinforcement learning technique to automatically design the altitude and velocity controllers of the octorotor. The image analysis and the control command generation algorithms are successfully tested on the octorotor platform, and the controllers demonstrate a satisfactory performance in simulations.

Liquid crystalline ordering of paired helical filaments in neurofibrillary tangles of Alzheimer's disease

1986 ◽  
Vol 44 ◽  
pp. 348-349
Author(s):  
D.F. Clapin ◽  
V.J.A. Montpetit
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurofibrillary Tangles ◽  
Liquid Crystalline ◽  
Amyloid Fibrils ◽  
Geometric Analysis ◽  
Paired Helical Filaments ◽  
Microscopic Level ◽  
Light Microscopic Level ◽  
Regular Spacing

Alzheimer's disease is characterized by the accumulation of abnormal filamentous proteins. The most important of these are amyloid fibrils and paired helical filaments (PHF). PHF are located intraneuronally forming bundles called neurofibrillary tangles. The designation of these structures as "tangles" is appropriate at the light microscopic level. However, localized domains within individual tangles appear to demonstrate a regular spacing which may indicate a liquid crystalline phase. The purpose of this paper is to present a statistical geometric analysis of PHF packing.

Automation Surprise

2017 ◽  
Vol 7 (1) ◽  
pp. 28-41 ◽  
Author(s):  
Robert J. de Boer ◽  
Karel Hurts
Keyword(s):  
Flight Control ◽  
Representative Sample ◽  
Aviation Safety ◽  
Control Mode ◽  
Cognitive Errors ◽  
Laboratory Studies ◽  
Airline Pilots ◽  
Flight Operations ◽  
Widespread Phenomenon ◽  
Few Data

Abstract. Automation surprise (AS) has often been associated with aviation safety incidents. Although numerous laboratory studies have been conducted, few data are available from routine flight operations. A survey among a representative sample of 200 Dutch airline pilots was used to determine the prevalence of AS and the severity of its consequences, and to test some of the factors leading to AS. Results show that AS is a relatively widespread phenomenon that occurs three times per year per pilot on average but rarely has serious consequences. In less than 10% of the AS cases that were reviewed, an undesired aircraft state was induced. Reportable occurrences are estimated to occur only once every 1–3 years per pilot. Factors leading to a higher prevalence of AS include less flying experience, increasing complexity of the flight control mode, and flight duty periods of over 8 hr. It is concluded that AS is a manifestation of system and interface complexity rather than cognitive errors.

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