Aircraft flight control systems

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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

Aircraft flight control was traditionally accomplished through mechanical & hydraulic systems. Subsequently, “Fly-By-Wire” through electronics has been widely used. Application of fibre optics in aircraft offers considerable advantages for flight control system. The military driver for fly by light is the increased use of composite materials in aircraft which provides less protection for control systems against EMI. As fibre optics are not affected by EMI, are lighter and has high bandwidth, it offers potential edge over FBW. This paper gives an overview of conventional (mechanical and hydro-mechanical), present (FBW) and futuristic (FBL) aircraft flight control systems.
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Aircraft turbine engine control systems development : historical perspective

Aircraft turbine engine control systems development : historical perspective

was first installed on an aircraft in 1941 [2]. Hans von Ohain had a patent for his engine in Germany in 1936 and the first flight with this engine had taken place in 1939 [2]. The first gas turbine engine developed by Whittle [2] had a simple throttle lever that controlled fuel flow into the engine. To accommodate the functional requirements when fitted on aircraft, design of fuel control system had to take into account effects of altitude, temperature and forward speed [3]. At the same time, continued requirements to improve gas turbine engine performance, production and life limiting processes had their impacts on gas turbine technologies [3]. In the 1950s, aircraft engine control systems were based on hydromechanical technologies and were complex artifacts. They encompassed a large number of components and subcomponents, and they were application-specific, such that a change in the design of the engine required a change in the design of the control system. Hydromechanical control systems reached a technological ceiling in a relatively short time. The maturity of the technology enabled engineers to understand, articulate, and modularize the interfaces between the engine and the hydromechanical control system. Furthermore, performance improvements derived primarily from operational experience rather than from scientific or technological breakthroughs had their impacts on turbine technology[4]. In the mature stage of development reached in the 1970s, hydromechanical control systems were characterized by a relatively low rate of technological change and increasingly predictable interdependencies with the other components. Although hydromechanical control systems had achieved relatively high reliability, they displayed limitations. Higher-thrustengines that were being
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Design And Simulation Of Attitude Control Systems For Charlie Aircraft

Design And Simulation Of Attitude Control Systems For Charlie Aircraft

Fig. 1Block Diagram of Pitch Attitude Control System The characteristic modes for nearly all aircrafts in most flight conditions are two oscillations: one of short period with relatively heavy damping, the other of long period with very flight damping. The period s and the damping of these oscillations vary from aircrafts to aircrafts and with the flight conditions. The short period oscillations are called the “short period mode” and primarily consist of variation of ‘ 𝛼 ’ and ‘ 𝜃 ’ with very little change in the forward velocity. The long period oscillations is called the “phugoid mode” and primarily consists of variations of ‘ 𝜃 ’ and ‘ 𝑢 ’ with ‘ 𝛼 ’ about constant. The phugoid mode can be thought of an exchange between potential and kinetic energy. The aircraft tends to fly a sinusoidal flight path in the vertical plan. As the aircraft proceeds from the height point of the flight path to the lowest point, it picks of speed, thus increasing the lift of the wing and curving the flight path until the aircraft starts climbing again and the velocity decreases, the lift decreases and the flight path curves downward. This condition continues until the motion is damped out, which generally requires a considerable number of cycles. However the period is very long and the pilot can damp the phugoid successfully even it is slightly divergent or unstable.
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AIRCRAFT ELECTRICAL POWER SYSTEMS AND NONLINEAR DYNAMIC LOADS SIMULATION USING MATLAB

AIRCRAFT ELECTRICAL POWER SYSTEMS AND NONLINEAR DYNAMIC LOADS SIMULATION USING MATLAB

Aircraft has become more dependent on uninterrupted electric power. The latest turbo fan engines, fly by wire flight control systems has increased the need of electrical power in civil aviation. In the similar lines the need to accommodate latest Electronic Warfare equipment has increased the requirement of electrical power in military aviation. Looking forward the future of military aviation with Unmanned Arial Vehicles (UAVs) where the aircraft is completely dependent on electrical power, the requirement of power generation, controlled power Distribution and utilization will certainly play a vital role. To meet the needs as discussed above there are two suggested options.
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Examination of Aircraft 's Cable Control Systems Tension

Examination of Aircraft 's Cable Control Systems Tension

may fluctuate at the level of technical acceptabil- ity, i.e. approx. 10%. An important aspect for air- craft designers and Aircraft Maintenance Instruc- tions preparation is the necessity of clearly defin- ing the conditions for examining extensiometers, i.e. determining the length of reference lines and their preparation, i.e. for example pre-tensioning (so-called cable training [6]) with specific values and defining the basis and value of forces used tensiometers. It seems appropriate to collect the stiffness structural and cable system data at the stage of structural tests, and further collect infor- mation on stiffness during flight tests at different ambient temperatures. This approach will reduce the measurement error to about 5–7%.
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Multi-objective design of robust flight control systems

Multi-objective design of robust flight control systems

controller gains that minimize a weighted combination of the infinite–norm of the sensitivity function (for disturbance attenuation requirements) and complementary sensitivity function (for robust stability requirements). After considering a single operating point for a level flight trim condition of a F-16 fighter aircraft model, two different approaches will then be considered to extend the domain of validity of the control law: 1) the controller is designed for different operating points and gain scheduling is adopted; 2) a single control law is designed for all the considered operating points by multiobjective minimisation. The two approaches will be an- alyzed and compared in terms of efficacy and required human and computational resources.
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AIRCRAFT DEPARTURE CONTROL SYSTEMS – HIDDEN SAFETY RISKS

AIRCRAFT DEPARTURE CONTROL SYSTEMS – HIDDEN SAFETY RISKS

Abstract: The aviation industry subjects to be comparable with other human-technology industries where risks are always present within their system. Modern appliances, including regulation, training and technology act as superior defense system. This being the case, the paper provides an integrated theoretical and practical reflections and knowledge of planning of safety risks within the framework of safety culture that are based on typical sets of hazardous situations that may affect the aircraft operations, with the main focus on Departure Control Systems (DCS) usage. Departure Control System (DCS) provides various functions and automated key processes in pre-flight preparation. These functions can be integrated or separated between various DCS usages. From system errors, data entry to different day-to- day operations, these risks were evaluated and analyzed in over five hundred flights. The research unveiled several risks with both visible and hidden consequences related to DCS usage, distinctively affecting aircraft mass and balance. Accident probability was measured and analyzed scrupulously for each airline separately, as a combination of implicit hidden and visible risk occurrence. The risk and accident occurrence ranking was done by Fussell-Vesely importance measures (FV) and Risk Reduction Worth (RRW). The issues were defined and prioritized, thus representing the first step to risk mitigation.
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Different Automation Concepts in Civil Aircraft Cockpits of Today and Their Influence on Airline Flight Operations

Different Automation Concepts in Civil Aircraft Cockpits of Today and Their Influence on Airline Flight Operations

The human machine interface are optimized considering the pilot's strengths and weaknesses. The answer to many problems with present automa- tion might be fuzzy logic that gives the highest pri- ority to latest pilot’s requests. Pilot should be able to exert control without being forced to disconnect the automation except in the most extraordinary circumstances. [3] Proper annunciation of mode changes (sound) must be ensured. It is important as well that automated systems have adequate displays to keep the operator informed about what is going on, and what is programmed to happen next.
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Evaluating GNSS integrity augmentation techniques for UAS sense and avoid

Evaluating GNSS integrity augmentation techniques for UAS sense and avoid

suitable data fusion algorithms to generate suitable warnings in case of GNSS data degradation or losses, thereby allowing a timely reaction/correction by the human pilot or by Unmanned Aircraft (UA) automatic flight control systems. A system such as this is called Avionics-Based or Aircraft-Based Augmentation System (ABAS). ABAS, GBAS and SBAS address (using different but synergic approaches) all four cornerstones of GNSS performance augmentation, namely: accuracy, integrity, availability and continuity [2-4]. The ABAS approach is particularly well suited to increase the levels of integrity and accuracy (as well as continuity in multi- sensor data fusion architectures) of GNSS in a variety of mission- and safety-critical applications. In UAS applications, airworthiness requirements for both cooperative and non- cooperative SAA impose stringent GNSS data integrity requirements, which cannot be fulfilled by current SBAS and GBAS technologies in some of the most demanding operational tasks. Therefore, a properly designed Avionics Based Integrity Augmentation (ABIA) system would allow an extended spectrum of autonomous and safety-critical operations including UAS SAA [4]. The ABIA system performs a continuous monitoring of GNSS integrity levels in flight by analysing the relationships between aircraft manoeuvres and GNSS accuracy degradations or signal losses (Doppler shift, multipath, antenna obscuration, signal-to-noise ratio, jamming, etc.). In case of any detected or predicted integrity threshold violation, the ABIA system provides suitable warning or caution signals to the UA Automatic Flight Control System (AFCS) and to the remote Ground Control Station (GCS), thereby allowing timely correction manoeuvres to be performed. This increased level of integrity could provide a pathway to support unrestricted access of UAS to all classes of airspace. Furthermore, using suitable data link and data processing technologies on the ground, a certified ABAS capability could be a core element of a future GNSS Space- Ground-Avionics Augmentation Network (SGAAN) for UAS SAA and other safety-critical aircraft/UA applications.
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Aircraft Flight Control Simulation Using Parallel Cascade Control

Aircraft Flight Control Simulation Using Parallel Cascade Control

enough iteration rate that the effects seem real. In most cases a tradeoff between accuracy and complexity must be negotiated. The primary simulator components make up the pilot-in-the-loop of the FFS where the pilot provides the inputs to the simulator and receives a feedback response through his or her hands, eyes, ears and "seat-of-the-pants". These systems are: the flight controls. e.g. the pilot control stick wheel and rudder pedals and flight control models, through which the pilot provides inputs to the simulator: the aerodynamic model which computes the effects of those inputs on the aircraft; the visual system which provides the visual feedback on the airplane behaviour and the environmental conditions: the motion system which gives feedback on the behaviour of the airplane with motion and acceleration cues; and the flight control (again) which provides feedback to the pilot's hand. There are also many secondary and auxiliary systems that require simulation including secondary flight control systems such as the flap system and the throttle controls.
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Assessing GNSS integrity augmentation techniques in UAV sense and avoid architectures

Assessing GNSS integrity augmentation techniques in UAV sense and avoid architectures

The ABIA IFG module is designed to provide CIF and WIF alerts in real-time (i.e., in accordance with the specified TTC and TTW requirements in all relevant flight phases) [1-5]. The GNSS and Sensors Layer (GSL) passes the aircraft Position, Velocity, Time (PVT) and attitude (Euler angles) data (from the on board Inertial Navigation Systems, Air Data Computer, etc.), GNSS data (raw measurements and PVT) and the Flight Control System (FCS) actuators data to the Data Extraction Layer (DEL). At this stage, the required Navigation and Flight Dynamics (NFD) and GNSS Constellation Data (GCD) are extracted, together with the relevant information from an aircraft Three-Dimensional Model (3DM) and from a Terrain and Objects Database (TOD). The 3DM database is a detailed geometric model of the aircraft built in a Computer Aided Three-dimensional Interactive Application (CATIA). The TOD uses a Digital Terrain Elevation Database (DTED) and additional man-made objects data to obtain a detailed map of the surfaces neighbouring the aircraft. In the Integrity Processing Layer (IPL), the Doppler Analysis Module (DAM) calculates the Doppler shift by processing the NFD and GCD inputs. The Multipath Analysis Module (MAM) processes the 3DM, TOD, GNSS Constellation Module (GCM) and A/C Navigation/Dynamics Module (ADM) inputs to determine multipath contributions from the aircraft (wings/fuselage) and from the terrain/objects close to the aircraft. The Obscuration Analysis Module (OAM) receives inputs from the 3DM, GSCS and ADS, and computes the GNSS antenna obscuration matrixes corresponding to the various aircraft manoeuvres [6]. The Signal Analysis Module (SAM) calculates the C/N 0 of the direct GNSS signals received by the aircraft in the presence
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Design Of Vertical Take-Off And Landing (VTOL) Aircraft System

Design Of Vertical Take-Off And Landing (VTOL) Aircraft System

reverse transition from FFF back to hover mode is called an ―inbound transition‖. Various points on the transition curve between P1 and P2 are sometimes called ―P1.n‖ where the ―n‖ is sometimes replaced by a number. There is an intermediate flight mode sometimes called ―Slow Forward Flight‖ (SFF). Hover mode (P1) and FFF mode (P2) often involve very different flight characteristics and control systems. Hover mode usually requires active stabilization in two or all 3 axis. FFF mode, or airplane mode, generally does not require stabilization, but even fixed wing aircraft can sometimes benefit from some stabilization. The stability feedback parameters for P1 and P2 are often very different, and the transition between P1 and P2 benefits from a smooth transition of stability feedback parameters. OAV uses the same PID feedback parameters as are commonly used for tricopters. In addition to the Gyro based stability feedback, the KK2 board also has accelerometers that can measure acceleration in all 3 axis, X, Y, and Z. This includes the constant acceleration due to gravity. The accelerometers and gyros are combined to create an IMU (Inertial Measurement Unit) which provides what is sometimes called an ―Auto-Level‖ function. The Z axis accelerometer can also be used for ―altitude damping‖ which helps the pilot to hold a more constant altitude in a hover. Lateral acceleration can also be used to detect and compensate for an uncoordinated turn, also known as a ―slip‖. As VTOL aircraft transition between P1 (hover mode) and P2 (Fast Forward Flight or FFF) the control systems often vary dramatically. Hover control is often by direct control of various lift motors. It can also be accomplished by servos that drive collective or cyclic pitch on rotors and it also
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Sliding Mode Control of MIMO Non-Square Systems via Squaring Matrix Transforms

Sliding Mode Control of MIMO Non-Square Systems via Squaring Matrix Transforms

establish the system response and controller performance for both discussed techniques. In these simulations, a target state was selected to optimize tracking of. While all states were simulated, a representative example, State Three (Cart Three’s position), is examined here. Shown in Figure 6 (and Figure 8), the tracking of State Three was nearly perfect and State Four had minimal error when controlled with the transformed matrix technique. While this transformed matrix control methodology did not exhibit particularly good performance in tracking States One and Two, they were not targeted during the system design so this is to be expected. The tracking of these non- primary states can likely be tuned via optimization of Q matrix value selection.
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Flight simulator transfer of training study : a thesis presented in partial fulfilment of the requirements for the degree of Master of Aviation at Massey University

Flight simulator transfer of training study : a thesis presented in partial fulfilment of the requirements for the degree of Master of Aviation at Massey University

Flight data was recorded to determine the participants' performance when flying the NOB holding pattern in the aircraft and the resulting flight times were used to determine the Percent [r]

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197103 pdf

197103 pdf

Incorporation into process control systems to automate automobile seat cushion production facilities Use in flight simulator being built for BOACjwill be used to train pilots and flight [r]

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Flight simulator transfer of training study : a thesis presented in partial fulfilment of the requirements for the degree of Master of Aviation at Massey University

Flight simulator transfer of training study : a thesis presented in partial fulfilment of the requirements for the degree of Master of Aviation at Massey University

Flight data was recorded to determine the participants' performance when flying the NOB holding pattern in the aircraft and the resulting flight times were used to determine the Percent [r]

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Assessing avionics based GNSS integrity augmentation performance in UAS mission  and safety critical tasks

Assessing avionics based GNSS integrity augmentation performance in UAS mission and safety critical tasks

Both cooperative and non-cooperative SAA systems are being developed to address UAS safe integration into the non-segregated airspace [1]. The SAA capability can be defined as the automatic detection of possible conflicts (i.e., collision threats) by the UA platform and the implementation of avoidance manoeuvres to prevent the identified collision threats. An analysis of the available SAA candidate technologies and the associated sensors was presented in [15]. An approach to the definition of encounter models and their applications on the SAA strategies is presented in [15, 16] considering both cooperative and non-cooperative scenarios. As part of our research, the possible synergies attainable with the adoption of different detection, tracking and trajectory generation algorithms were studied. Additionally, the error propagation from different sources and the impacts of host and intruders dynamics on the ultimate SAA solution were investigated [15]. SAA system requirements can be derived from the current regulations applicable for the human pilot see-and-avoid capability [17-22]. The proposed ABIA/SAA integrated architecture is illustrated in Fig. 5. The Position, Velocity and Attitude (PVA) measurements are typically obtained by adopting multi-sensor data fusion techniques [23 -25]. An initial flight path is generated using the aircraft dynamics model. The IFG module run is performed on that trajectory. Based on a Boolean decision logic that sorts sensors’ data based on estimated performance parameters, the C-SAA or non- cooperative SAA sensors are used for safe separation. If both the safe separation thresholds are violated and a mid-air collision threat is detected the WIF is generated. To prevent any WIF, the flight path optimization process starts when the first CIF is generated. Pseudo-Spectral Optimisation (PSO) and Differential Geometry Optimization (DGO) techniques are used to generate a new optimised trajectory free of any integrity degradations. Depending on the relationship between the available time-to-collision and the computation time PSO and DGO trajectory solutions, the optimised trajectory data are sent to the AFCS (and/or to the ground pilot) for execution of the avoidance manoeuvres. In the trajectory optimisation process time is used as the cost functional and the aircraft dynamics model/satellite elevations are used as path constraints. The selection of the optimal trajectory from the generated set of safe trajectories is performed, which is then fed to the aircraft guidance
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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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Vol 10, No 1 (2014)

Vol 10, No 1 (2014)

Many linear numerical integration techniques with single and multi step are available which can also be classified into implicit and explicit numerical integration techniques [8]. With respect to the stability and accuracy, each of these numerical integration techniques has advantages and disadvantages [8]. Depending on the performance, these methods can be suitably used for stiff and non-stiff systems. Methods not designed for stiff problems must use time steps small enough to resolve the fastest possible changes, which makes them rather ineffective on intervals where the solution changes slowly. The most popular numerical integration methods are listed below.
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Models and the scaling of energy costs for locomotion

Models and the scaling of energy costs for locomotion

Previous sections have shown that, at least for running and flight, muscles do the work of locomotion more efficiently in larger animals. Kram and Taylor (1990) tried to explain this observation for running. They ignored the metabolic cost of doing work, and considered only the cost of exerting the force required to counteract gravity. They assumed that muscles work over the same ranges of the force–velocity relationship, irrespective of speed and body size; faster muscle fibres would be recruited at higher running speeds, and smaller animals (whose feet remain on the ground for shorter times) would need faster muscles than large ones. These assumptions led them to the hypothesis:
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