Abstract Aircraftflight simulation is a billion dollar industry worldwide that requires vast engineering resources. A method for modeling flightcontrol 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 flightcontrol 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 aircraftflightcontrol system. i.e. the pilot input control meet the objective test requirements of a commercial aircraftflight simulator. The second is to produce such a model which also meets the basic requirements for implementation in a working flight simulator.
Aircraftflightcontrol 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 flightcontrol 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) aircraftflightcontrol systems.
that makes the augmented plant minimum-phase, and thus ASPR, by defining an LMI set that represents a con- vex control effector failure polytope. The approach consists of minimizing the Frobenius norm of D subject to LMI constraints. The designs used a fixed eigenstructure assignment controller for an inner loop augmented with the simple adaptive controller. The adaptive algorithm and the proposed method to compute the feedfor- ward gain have been applied to both an F/A-18 aircraft and a tailless aircraft with lateral wind gust disturbances. A feedforward gain was designed for an F/A-18 aircraft for a loss of control effectiveness in any one control ef- fector of 92% trailing edge flap, 99% aileron, or 80% rudder. Furthermore, a feedforward gain was designed for a tailless aircraft for a loss of control effectiveness in any one control effector of 50% elevon, 50% all moving tips, or 50% yaw thrust vectoring. Computer simulations for both aircraft with a failure in any one control ef- fector under lateral gust conditions exhibited almost perfect tracking with the adaptive algorithm whereas the nonadaptive F/A-18 controller could not achieve a coordinated turn when an aileron failure occurred and the nonadaptive tailless aircraft controller diverged when an all moving tip failure occurred.
The pilot is final authority for the operation of the airplane and is ultimately responsible for the safe operation of the aircraft. Pilot's tasks in order of priority are safety, passenger comfort, and effi- ciency. Automation inputs should be consistent in a given piece of equipment and automation should not produce effects which are unwanted, illogical and inconsistent with safe practice. It should always defer to humans and should never be programmed to actively counteract them. The aircraft essen- tial systems have to provide full authority to the pi- lot in order to achieve the maximum possible per- formance with a simple intuitive procedure. New products must be designed in order to make greater, not less use of the humans in the cockpit.
Further examination showed that there are underlying problems in the process design. From a Human Factors point of view certain communicative patterns were ignored. An example of this situation: in interviews with numerous Flight Managers, they raised concern about the following issue. According to the before-mentioned reference model, the gate staff is supposed to send the passengers through the jet way bridges at a specific time without prior notice to the crew. The only exception is if the crew has declared the aircraft “not ready for boarding” beforehand. However, the gate staff experience was that crews often forgot this announcement and passengers arrived at an aircraft with closed doors or not yet finished preparations onboard. In consequence, this procedure is mostly ignored today and the gate staff is calling for permission to begin the boarding process. This quite often delays the boarding process for a couple of minutes with negative effects for the whole process chain. The original design of the seamless process is clearly violated. The staff claim that they feel uncomfortable sending somebody into a situation that might be unpleasant for them. This is an example how uncertainty in data (Carayon, 2006) contributes to work system complexity. At this point obviously the human factor was ignored. This is just one of many examples where the communication processes hold shortcomings resulting in negative effects for the whole process chain.
As the statistics analysis demonstrates (Akhrameev, Goman, Merkulov, & Klumov, 1989; Akhrameev, & Goman, 1989; Ahrameev, & Goman, 1991; Akhrameev, 1998; Report of the Interstate Aviation Committee (IAC), 2008), the majority of air accidents occur either due to various equipment failures (15 ... 20%) or due to piloting errors made in the process of piloting (about 80%). The article (Babichenko, & Zemlyaniy, 2014) examined some of the factors that lead to piloting errors associated with both psycho-physiological qualities of pilots and the nature of the problems to be solved. It is shown that with the objectively growing complexity of aircraftcontrol tasks, crews intellectual support is necessary for flight safety, i.e. giving the functions of an intelligent system to the onboard equipment; it supposes creation of onboard knowledge base, inference engine and the corresponding interface. Implementation of this provision in the full form means building an onboard expert flight safety system (Babichenko, & Zemlianiy, 2014, Shishkin, 2005). Unfortunately, nowadays there are no similar solutions for GA aircraft, due to reasons of high cost and development complexity. At the same time, solutions simplifying the implementation of intellectual support with traditional algorithmic and schematic methods are partially embedded in military FV production. Therefore, taking into account the relevance of diagnostics of critical flight modes for GA aircraft, including those associated with the danger of aircraft collision in the air and collisions with ground obstacles; at this stage it is appropriate to use existing solutions for intellectual support for their adaptation and further development. Diagnostic algorithms integration of with algorithms for control automation in the manual (giving recommendations to pilots) or in the automatic mode will significantly improve flight safety for Small Aviation and GA aircraft.
OTA_AirFareDisplayRQ/RS – allows a client to request information on fares, which exist between a city pair for a particular date or date range. No inventory check for available seats on flights is performed by the server before the RS is send back. The request can optionally contain information indicating that a more specific response (e.g. passenger information, specific flight information and information on the types of fares that the client is interested in) is required. The RS message repeats FareDisplayInfo elements, each of which contains information on a specific fare contract including airline, travel dates, restrictions and pricing. It can also return information on other types of fares that exist, but have not been included in the response. OTA_AirFlifoRQ/RS – requests updated information on the operation of a specific flight (it requires the airline, flight number and departure date; the departure and arrival airport locations can be also be included). The RS includes real-time flight departure and arrival information. It also includes: departure airport, arrival airport, marketing and operating airline names; when applicable, flight number, type of equipment, status of current operation, reason for delay or cancellation, airport location for diversion of flight, current departure and arrival date and time, scheduled departure and arrival date and time, duration of flight, flight mileage, baggage claim location.
There are three categories of methods of parameter estimation , namely Equation Error Methods (EEM), Output Error Methods (OEM), and Filter Error Methods (FEM). The equation error method focus on making the parameter estimation problem as a linear one. The output error method and filter error method determine the parameters through a non-linear approach. The equation error method and output error method are categorized as the deterministic method, whereas, the filter error method as the stochastic one. The equation error method finds its basis on linear regression by employing the ordinary least squares method. The prime merit of the equation error method is computational easiness and absence of an iterative technique for parameter estimation. The significant demerits are asymptotic biasing behavior, erratic nature, and inappropriateness in the estimates when employed for parameter estimation with noisy measurement data . The output error method gains the application to most of the parameter estimation problems, especially about flight vehicles. The output error method functions on the iterative adjustment of the parameter vector for which the response error between measured and estimated response is minimized. A kind of output error method, i.e., Maximum-Likelihood method focuses on estimating aerodynamic parameters through maximizing the probability density function of the observations [1, 13].
The solar model has been used to calculate the available solar energy for different endurance periods at various altitudes and latitudes. In each case of a flight duration, the minimum available solar energy per day is considered as a reference parameter for the pre-conceptual design process. The study was aimed at designing a high altitude long endurance solar powered aircraft for a surveillance mission over a specific area in Iraq which lies between latitudes 29 0 and 38 0 N. it had to carry a 100
Two optimum speeds can be defined: the minimum power speed at which the total power P is least, and the maximum range speed at which the energy required to travel unit distance (P/v) is least. These speeds can be obtained from Equation·1 by calculus (see, for example, Alexander, 1996). Textbooks of aerodynamics usually treat the zero-lift drag as constant, as it would be if the flight of different-sized animals were dynamically similar. Dynamic similarity would, however, require animals to fly at equal Reynolds numbers, that is at speeds inversely proportional to their linear dimensions; a moth with a 10·mm wing chord would have to fly ten times as fast as a bird with a 100·mm chord. That would generally not be the case, so we must take account of differences in C 0 . All
frameworks are highly complex and aimed at supporting certification activities. These often couple computational fluid dynamics (CFD) with computational structural mechanics (CSM) and result in processes that provide the desired insight, but are computationally very expensive (Cooper et al. 2016, Lindhorst et al. 2014, Wang et al. 2015). Reduced order models such as VARLOADS (Kier et al. 2005) have also been developed, but these have only seen limited research usage. In academia, Palacios et al. (Palacios et al. 2010, Palacios and Cesnik 2008, Simpson et al. 2015) have shown the capability to link aeroelasticity with flightcontrol and develop novel approaches to aeroservoelastic analysis of highly flexible configurations. Structural flexibility effects were modelled through the implementation of a nonlinear structural dynamics formulation. Aerodynamic contributions were captured through the implementation of an unsteady vortex lattice method code. Although the approach adopted by Palacios et al is computationally cheaper than those used in industry, real time simulation is still not possible.
Careful attention needs to be given to the technology and measures used to form a picture of pilot state and augment pilot performance. This refers, e.g., to the reliability and robustness of recording and assessment technology. Eye tracking is actually a very interesting technology to observe pilot monitoring performance. However, we found that the technology needs improvements to be reliable and robust inside an aircraft cockpit. An important constraint is the fact that an aircraft cockpit is often a dark environment, which can affect the eye tracking measure. Incorrect measurement can of course lead to incorrect assessments and this may cause unappreciated notifications on the MCMD. Further, the function used to assess pilot monitoring performance described in this paper depends on what we know about the pilot actual monitoring performance, i.e., if they are or are not looking at the right information sources. Hereby, the presented approach does not consider individual differences, e.g., pilot experience. It may not be a problem if an experienced pilot is not monitoring the navigation display in the defined frequency, e.g., because the pilot is very familiar with the route. Further, we have to think about how the technology presented in this paper can be applied on a two pilot crew. Given the case that two pilots can be observed and assessed, the question arises how individual feedback on monitoring performance can be provided to each crew member. At the moment, notifications are issued on the MCMD, which is a display shared by the pilot crew.
Abstract. As a subclass of Automatic Target Recognition problem, Automatic Aircraft Recognition plays an important role in air traffic management and modern battlefield for automatic monitoring and detection. The research on Automatic Aircraft Recognition is still in the exploratory stage. Since aircrafts move at high speeds in complex background, it is still a challenging task of fast data processing and accurate aircraft type recognition. Besides, active learning has recently attracted many researcher’s interesting. Based on this, we employ a learning-based approach which combines active learning with reinforcement learning to learn how and when to request labels for the aircraft type recognition problem. The experimental results show that the model can achieve a good prediction accuracy with few label requests.
at different flight conditions to get the best lift-induced drag reduction for the given flight phase; or it can be kept in the vertical position while in ground so that it reduces wingspan while meeting gate and runaway clearance; or it can act as a load alleviation mechanism where in the case of gusts or strong sideslip velocities, the winglet can adjust itself, so it reduces the bending moment on the wing and the device itself. Similar solutions have been already proposed, but most of them focused on the use of shape memory alloy materials (20,21,22,23,24,25) , foldable wings during
5 the aircraft movement. Basically, the behavior of the aircraft can be identified and controlled if all flight variables related to the flight behavior are evaluated in respect to time. Strictly speaking, 21 flight variables are available to describe the flight behavior of an aircraft during flight . These 21 flight variables are the three components of velocity at trim (U, V, W), the three components at any instance flight (u, v, w), the three aptitude angles (, Ψ, ϕ), the three angle of velocity vectors with respect to the body axis coordinate system (α, β, γ), the aircraft position with respect to the inertia reference (x, y, z), while the other six parameters are related to the derivatives quantities in translational motion and rotational motion . These flight variables are all related to each other through a number of equations known as the governing equations of flight motion and they belong to the class of non-linear time- varying ordinary differential equation. Hence, the ability to solve the governing equations of flight motion is important since it offers the capability to analyze the flight dynamics behavior for any given type of aircraft. Therefore, the combination with the control theory offers the possibility to define the behavior of an aircraft during flight.
The commander of the aviation unit tasked to support the helicopterborne operation designates the AMC. The AMC is the Marine aviator desig- nated by the commander of the aviation unit tasked to support a helicopterborne operation. Depending on the size and scope of the MAGTF, he may also be the ACE commander. Unless the mission commander is the MAGTF commander, there will not be a command relationship between the mission commander and the AMC. In some cases, the mission commander exercises tactical control of assigned aviation assets; that is, he may direct and control the movements or maneu- vers necessary to accomplish missions or tasks assigned. During the planning phase, the AMC is co-equal to the HUC. During execution, specific authority is delegated from the mission com- mander to the AMC. The AMC typically works in direct support of the mission commander and answers directly to the mission commander’s requests for assistance and support. The sup- ported-supporting relationships and the means by which they are executed are critical to mission success; therefore, the AMC must have a detailed understanding of the command and support rela- tionships with key subordinates (e.g., AFL, EFL). The AMC is responsible for the planning and execution of all aviation functions relative to the assigned helicopterborne mission; therefore he must be an experienced aviator. It is the AMC’s responsibility to establish liaison with the mission commander and HUC (the commander responsi- ble for the ground tactical plan) in order to con- duct concurrent and parallel planning. The AMC shall assume the duties of the assault support coordinator (airborne) (ASC[A]) of a mission if no ASC(A) is assigned.
flaps. They examined the response of an aircraft with an automated cruise flap which was controlled using the instantaneous coefficient of lift as the sole input. Such a flap, by itself, was found to be unstable. From a trimmed state, if the wing were perturbed such that the coefficient of lift were increased, the flap angle would increase to move the stagnation point back to the ideal location. The larger flap angle would then increase the coefficient of lift even more, and the flap would quickly diverge to its limit of travel. Although aircraft such as uninhabited air vehicles (UAVs) are probably equipped with separate autopilot control systems to maintain altitude and airspeed, such systems could not counter the instability of the flap controller since the flap system has such a short time constant (determined primarily by the reaction time of the flap servo). A filter is needed to slow the operation of the flap.
Air speed and flight attitude angles are fundamental parameters for manual of automatic control of flying bodies. Conventional measurement methods rely on probes (e.g. Pitot tubes or vanes) having a one-to-one correspondence with the physical quantities of interest and requiring specific placements. Here, a novel measurement approach is proposed, relying on indirect measurement and on a plurality of pressure readings made by thin capacitive sensors directly placed on the aircraft skin. A redundant number of probes relaxes the accuracy requirements posed on the individual units and helps achieving fault detection or fault tolerance. A strategy for efficiently processing/combining sensor data is herein presented together with an error propagation analysis, and experimental data.
According to Article 8 Regulation 216/2008, operating aircraft should comply with the basic requirements defined in Annex IV. According to point 7.f of the Annex, no crew member should allow their task achievement or decision making to deteriorate to the extent that flight safety is endangered because of the effects of fatigue, fatigue accumulation, sleep deprivation, number of sectors flown, night hours, etc. Rest periods must provide sufficient time to enable crew members to overcome the effects of the previous duties and to be well rested by the start of the following flight duty period. In point 8 f of Annex IV, it has also been noted that, for the purpose of flight safety, a rostering system must be applied for a flight or a series of flights. It needs to address flight time, flight duty periods, duty and adapter rest periods. Limitations established for the rostering system must take into account all relevant factors contributing to fatigue, in particular such as the number of sectors flown, time zone crossing, sleep deprivation, disruption of circadian cycles, night hours, positioning, cumulative duty time for given periods of time, sharing of allocated tasks between crew members, and also the provision of augmented crews.
When the multipath ranging error exceeds 2 metres and the aircraft flies in proximity of the ground (below 500 ft AGL), the warning integrity flag shall be generated. In order to define the integrity thresholds associated with Doppler and fading effects, a dedicated analysis of the GNSS receiver tracking performance was performed. When the GNSS measurement errors exceed certain thresholds, the receiver loses lock to the satellites. Since both the code and carrier tracking loops are nonlinear, especially near the threshold regions, only Monte Carlo simulations of the GNSS receiver in different dynamics and SNR conditions can determine the receiver tracking performance [8, 9]. Numerous sources of measurement errors affect the Phase Lock Loop (PLL), Frequency Lock Loop (FLL) and Delay Lock Loop (DLL). PLL, FLL and DLL are adopted in Scalar Tracking Loops (STL) as well as Vector Tracking Loops (VTL) are considered as part of this research. Error models described in  allow determining the effective Carrier-to-Noise ( C/N ) ratio corresponding to the receiver tracking thresholds. The integrity flag criterion applicable to the ABIA system is: