Flight control systems

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Neural Network based Sensor Fault Detection for Flight Control Systems

Neural Network based Sensor Fault Detection for Flight Control Systems

A wide variety of conventional approaches exist for the detection and identification of failures in dynamic systems. Either hardware or analytical redundancy technique are adopted in designing the failure detection system [1], [2]. However, hardware redundancy results in higher cost, increased power consumption and increase in volume and weight. Analytical redundancy implies the use of a validated mathematical model to analytically generate signals that would otherwise be produced by redundant hardware. These redundant techniques employ state estimation, adaptive filtering, statistical decision theory etc. Kalman filters and Luenberger Observers have been very popular for generating the signals for analytical redundancy purposes [3],[4]. Kalman filter and its various advanced versions were proposed by many researchers. Montgomery and Cagayan [5] used Kalman filters and Multiple Model Kalman Filtering (MMKF) for state estimation to detect failures in digital flight control systems. Another Kalman filter based approach, the Generalized Likelihood Ratio (GLR) was proposed by Paul M. Frank [1] and Thomas Kerr [2]. Sequential Probability Ratio Test (SPRT) which is similar to the GLR algorithm was advancement in this field and was given by Thomas Kerr [2]. T. V. Rama Murthy [3] and Shapiro E Y [4] used dedicatedLuenberger observers for sensor failure detection in aircraft control systems. Dedicated Luenberger observers were used by Clark and Setzer for sensor failure detection in a system with random disturbances [6]. An adaptive control approach to Sensor Failure Detection and Isolation was used by M.N. Wagdi [7]. Observer based sensor and actuator fault detection in small autonomous helicopters was done by G. Heredia et.al.[8].
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Multi-objective design of robust flight control systems

Multi-objective design of robust flight control systems

veloped, based on the application of a modern multi–objective evolutionary optimization algorithm to the associated minimization problem. In the last two decades, multiple redun- dant, full authority, fail/safe operational, fly–by–wire control systems have been brought to a very mature state. As a result, many aircraft, from earlier designs such as the F-16, F-18, and Tornado through the more recent Mirage 2000, European Fighter Aircraft (EFA), Rafale, and advanced demonstrators such as X-29 and X-31, are highly augmented, actively controlled vehicles that possess either a marginal or negative static stability margin without augmentation, for reasons related to improved performances, weight/cost reduction, and/or low observability.
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New experimental approaches to the biology of flight control systems

New experimental approaches to the biology of flight control systems

However sophisticated tethered-flight paradigms may become, it goes without saying that the natural state of flight is free flight. It does not follow, however, that free flight is necessarily natural flight – in most experimental situations, the subject will be trailing leadwires, carrying a load, flying in a wind tunnel, or simply flying in a confined space. Nevertheless, it is only possible to have the chance of identifying true closed-loop dynamics in free flight, and for this reason free-flight paradigms are likely to play an increasingly important part in our developing understanding of animal flight control. The key difficulty from a flight dynamics perspective is that the forces and moments cannot be directly measured – only the animal’s consequent motion. This is problematic because although Newton’s Second Law tells us that knowledge of mass and acceleration is equivalent to knowledge of force for a moving particle, things are more complicated for a solid body. For example, a measured roll acceleration might reflect the direct application of a roll torque, but it might also reflect a non- zero product of the angular velocity components about the pitch and yaw axes if their moments of inertia are unequal. The issues of coupling alluded to in section 2.1.2 therefore mean that it will not in general be possible to treat different degrees of freedom separately.
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The path performance analysis of missile

The path performance analysis of missile

midcourse and terminal phases the system may be designed to track acceleration commands to effect an intercept of the target. This article explores several aspects of the missile flight control system, including its role in the overall missile system, its subsystems, types of flight control systems, design objectives, and design challenges. Also discussed are some of APL’s contributions to the field, which have come primarily through our role as Technical Direction Agent on a variety of Navy missile programs.[3]

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Conflicts in Routing and UAV Autonomy Algorithms for Ad-hoc & Infrastructure based UAV networks

Conflicts in Routing and UAV Autonomy Algorithms for Ad-hoc & Infrastructure based UAV networks

Zheng et al. (2018) proposed an adaptive hybrid reinforcement learning, self- learning routing protocol (RLSRP) to address the network-layer routing requirements and position-prediction-based directional (PPMAC) protocol for the FANET MAC layer. The protocol implements two cooperative transceivers operating concurrently, with one processing position and control packets while the other handles data traffic. This scheme depends on position prediction and estimation, which may be problematic if predictions become significantly inaccurate. The protocol relies heavily on the assumption that GPS coordinate vectors will be shared amongst all participating UAV nodes, which is also subject to link availability. From the perspective of an autonomous platform algorithm, it is important to clarify how such a routing scheme will be affected by flight control systems, which are not integrated with routing algorithms.
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Integrating Routing Schemes and Platform Autonomy Algorithms for UAV Ad-hoc & Infrastructure based networks

Integrating Routing Schemes and Platform Autonomy Algorithms for UAV Ad-hoc & Infrastructure based networks

Zheng et al. [5] proposed an adaptive hybrid reinforcement learning, self-learning routing protocol (RLSRP) to address the network layer routing requirements and position-prediction based directional (PPMAC) protocol for the FANET MAC layer.The protocol implements two cooperative transceivers operating concurrently with one processing position and con- trol packets while the other handles data traffic. This scheme depends on position prediction and estimation which may be problematic if predictions become significantly inaccurate due to any number of reasons. The model relies heavily on the assumption that GPS coordinate vectors will be shared amongst all participating UAV nodes, which is also subject to link availability. From an autonomous platform algorithm perspective it is important to clarify how such a routing scheme will be affected by flight control systems which are not integrated with routing algorithms.
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Aircraft Flight Control Simulation Using Parallel Cascade Control

Aircraft Flight Control Simulation Using Parallel Cascade Control

Abstract Aircraft flight simulation is a billion dollar industry worldwide that requires vast engineering resources. A method for modeling flight control systems using parallel cascade system identification is proposed as an addition to the flight simulator engineer’s toolbox. This method is highly efficient in terms of the data collection required for the modeling process since it is a black box method. This means that only the input and the output to the flight control system are required and details of the inner workings of the system can be largely ignored resulting in significantly fewer real data signals that need to be recorded. The paper views on two objectives. One the specific parallel cascade models can be identified that reproduce the behavior of a particular part of an aircraft flight control system. i.e. the pilot input control meet the objective test requirements of a commercial aircraft flight simulator. The second is to produce such a model which also meets the basic requirements for implementation in a working flight simulator.
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For the UAV to carry out these missions, its flight control system has to be inherently reliable. Little is known on the application of Markov Analysis techniques for the failure analysis of UAV’s flight control systems. Hence, in order to ascertain the reliability of a proposed UAV flight control system design to be adopted for the Air Force Institute of Technology (AFIT), Nigerian Air Force (NAF) base, Kaduna ABT-18 UAV project, this paper, implements Markov analysis as a tool for the failure analysis of the ABT-18 UAV flight control system.
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Evolution of Aircraft Flight Control System and Fly-By-Light Flight Control System

Evolution of Aircraft Flight Control System and Fly-By-Light Flight Control System

on conventional methods of mechanical and hydro-mechanical system. The present generation aircraft are using fly-by-wire (FBW) and in future likely to migrate to fly-by-light (FBL) method for aircraft control system. Mechanical & Hydro- mechanical flight control systems have been replaced by Fly- By-Wire due to increasing speed of modern aircraft. Due to inherent characteristics of FBL like light weight, compact size, large bandwidth, immunity to EMI & HIRF FBL, it is expected to be ideal futuristic flight control system. Fly-by- Light control systems offer inherent resistance to the new generation more hostile military environments. The inherent features are the motivator to achieve the technological advances to make Fly-by-Light systems a successful replacement aircraft control system technology for future. The application of optical fiber in aviation promises to be very exciting study, covering highly complex aircraft stability and controls.
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Navigation systems in 
		NOE flight sensors and their integration

Navigation systems in NOE flight sensors and their integration

A radio altimeter broadcasts an FM modulated continuous wave 4GHz signal. Their frequency is modulated on a "ramp" so that it changes in a linear fashion. Frequency between the transmitted signal and the reflected received signal is continuously monitored and processed to display altitude above the terrain. It is mainly designed to display the distances from 0 to 2500 feet (give or take depending on the model). It is not active when the aircraft is above 2500 feet. (again, give or take depending on the model). The figure 5 shows the equipment of radar altimeter with some deflection. It deflects with corresponding to the change in the fight altitude. This prevent the flight from disaster by giving the exact altitude with the ground level.
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Flight mechanics and control of escape manoeuvres in hummingbirds  II  Aerodynamic force production, flight control and performance limitations

Flight mechanics and control of escape manoeuvres in hummingbirds II Aerodynamic force production, flight control and performance limitations

In this paper, we extend the kinematic analysis of our companion paper (Cheng et al., 2016) to assess how flight performance of hummingbird escape manoeuvres is related to mechanical power output of their muscles and to delays in their neural sensing and motor-control systems. Escape manoeuvres from vigilant hummingbirds under potential threat may represent a near-maximal flight performance, thereby revealing effects of underlying limiting factors. We first calculate aerodynamic forces, moments and mechanical power of escape manoeuvres using measured wing kinematics and quasi-steady aerodynamic modelling. At high angular rates of change in body orientation, animals with flapping wings may experience substantial damping as a result of asymmetries of wing motion caused by body rotation; this is known as flapping counter torque (Hedrick et al., 2009). Thus, we estimate damping and the corresponding time constant of averaged open-loop dynamics to quantify the relative importance of damping and inertia. We then assess potential performance limitations imposed by delays in neural sensing and control systems and by muscle power. Finally, we explore interspecific differences in muscle power and their implications for flight performance. For clarity and concision, among the four species studied in the companion paper, we focus only on magnificent hummingbirds and black-chinned hummingbirds, representing large and small species, respectively.
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Adaptive Quaternion Attitude Control of Aerodynamic Flight Control Vehicles

Adaptive Quaternion Attitude Control of Aerodynamic Flight Control Vehicles

depicted in Fig. 6, the matched uncertainties are cancelled out adaptively and as depicted in Fig.s 4 and 5, an acceptable tracking performance is achieved.Moreover, according to stability theorem, all error signals including neural network weights error should remain bounded. As it is shown in Fig. 7, the NN’s weightsare bounded and since the ideal weights in (21) are bounded so, the weights errorsremain bounded. In continuance, a sinusoidal external disturbance as defined in eq. (3) is applied to the closed loop system, as Fig. 8, shows in the absence of adaptive parts of control law, the disturbance effects on tracking performance and increases the tracking errors.The tracking errors are improved as soon as these adaptive parts are added to the control law.
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Dynamic Model of Flying Machines with  the Autopilot

Dynamic Model of Flying Machines with the Autopilot

+ (8) In Figure 1 is shown the scheme of automatic control of the FM with the autopilot. Using transfer functions of elements it is possible to write down the general transfer function concerning the simplified block dia- gramme.

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Online Full Text

Online Full Text

In practice, an UAV autopilot uses a combination of PID feedback and feedforward controllers, such as in case of Kestrel Autopilot [13], to generate the control efforts of conventional control surfaces and engine throttle. There are about fifteen flight modes of an UAV such as manual, homing, altitude, and targeting modes. The control strategy is based upon the use of cascading controllers for which multiple PID controllers are incorporated into one input/output loop. Cascading controllers that link the output from one PID unit as the input to another PID unit are useful for more complex missions and maneuvers. As for an autonomous flight the autopilot of an UAV should be correctly configured and tuned for each flight mode, this control strategy yields a great number of parameters and requires more efficient tuning procedure.
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Flight stabilization control of a hovering model insect

Flight stabilization control of a hovering model insect

Dynamic stability is an inherent property of the system. It deals with the motion of a flying body about its equilibrium state following a disturbance, without active control being applied (it involves the solution of Eqn·1 without the term Bc); if the amplitude of the oscillation decreases with time and goes to zero, then flight is dynamically stable, otherwise it is unstable or neutrally stable. The results of stability analysis could show whether or not the system needs to be controlled. Stability properties (stable or unstable; how and how fast the disturbance decrease or increase, etc.) can be determined using the techniques of eigenvalue and eigenvector analysis [this has been done for hovering bumblebee (Sun and Xiong, 2005) and for locusts in forward flight (Taylor and Thomas, 2003)]. In eigenvalue and eigenvector analysis [for a concise description of the theory, see Taylor and Thomas (Taylor and Thomas, 2003)], the disturbance motion is expressed as a linear contribution of natural modes of motion of the system, thus the stability properties of the flight can be represented by the natural modes of motion. In the present study, the technique of eigenvalue and eigenvector analysis is applied to Eqn·1 (with Bc set to zero). The results would tell us which mode is unstable or weakly stable (although stable, the disturbance goes to zero slowly) and needs to be controlled. In addition, the eigenvector of a natural mode of motion would tell us what are the main variables in the mode; this information is very useful in studying the control of this mode.
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Stability Control For Quadcopter With An Autonomous Flight System

Stability Control For Quadcopter With An Autonomous Flight System

Unmanned Aerial Vehicles (UAV) are becoming more and more popular in a multitude of fields, including but not restricted to military applications, corporate and academic research as well as personal hobbies. Many of these projects are constrained by high costs (military), or limited practical use (hobbies). The main goal of this project is to create lightweight flyer that can maintain a steady altitude in flight, fly in accordance to a pre-set flight path, has a simple interface for controlling, and has the capability to carry a payload. The quadcopter is to be used by any user capable of responsibly using the flyer. Therefore, a design and controller based on a beginner-level pilot’s skills are essential to the project. To accomplish this, the quadcopter will use various technologies, including an Arduino microcontroller interfaced with an array of sensors, infrared sensors; communicate with radio frequency.
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Numerical Simulation on Denotation Driven Asymmetric Structure Fragments

Numerical Simulation on Denotation Driven Asymmetric Structure Fragments

Fragments are normally constructed from steel or tungsten alloys of high densities. When designed for people, their mass is usually larger than 1g. When designed for vehicles or aircrafts, their mass is usually larger than 6g. Existing fragments are mainly availa- ble in spherical, cubic or rhombic and cylindrical forms. Spherical fragments are the most frequently used form featuring simple structure and compara- tively the smallest resistance during flight [1, 2] . Cubic or rhombic fragments achieve better penetration into the target by using the sharp angle at their edge [3, 4] . These fragments are also easily machined and have a larger contact area with explosive than their spherical counterparts, therefore obtaining higher velocities when driven by explosive. Cylindrical fragments [5, 6] are usually used in missile warheads requiring a large-mass damage unit such as anti-missile warheads.
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The neuronal control of dragonfly flight  II  Physiology

The neuronal control of dragonfly flight II Physiology

Mechanisms which regulate the timing of contraction and the power output of different muscles make use of variation in the number of motor neurones that spike and the number of spikes in[r]

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Effects of landuse changes to water quality and green mussels (Perna viridis) due to development along Danga-Pendas coastal area

Effects of landuse changes to water quality and green mussels (Perna viridis) due to development along Danga-Pendas coastal area

This report focused on inventory management and control at an in-flight catering company dry goods raw material store. The objective is to develop an inventory management and control procedure. ABC analysis was carried out to determine the frequency of stock check. Barcode system can improve the inventory control of the dry goods raw material stock. Forecasting technique based on the historical data can help reduce over stock of raw material. The implementation of the inventory control techniques can improve the inventory management and at the company. The techniques proposed have been validated by the expert.
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Neural control of hindleg steering in flight in the locust

Neural control of hindleg steering in flight in the locust

A Fig. 1. Activity of the metathoracic second coxal abductor muscle (M126) during hindleg steering in intact locusts. The animals were suspended from the prothorax in a laminar airstream. Flight steering was elicited via open-loop yaw deviations of the animal away from the straight flight course by rotating it around its vertical axis. Upward deflection of the yaw trace indicates deviation of the animal to the left side, downward deflection indicates deviation to the right side. (A) Recording of the right and left M126 during tethered flight. Yaw deviations to either side elicited spikes in the contralateral M126. This was correlated with extension of the corresponding hindleg, whereas the hindleg ipsilateral to the yaw deviation remained drawn up in the normal flight position. (B) Recording of the left posterior coxal rotator muscles (PCRM) and the ipsilateral M126 during tethered flight. The two black bars indicate leg extensions in response to yaw deviations to the right side. The onset of leg extension correlated with activity in the larger of the two motor units recorded in M126. This unit ceased firing while the hindleg was still extended. Movement of the hindleg back to the normal flight position is indicated by activity in the posterior coxal rotators. The smaller motor unit in M126 was
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