Flight trajectory of a passenger aircraft is critical for the research and development of future air traffic control system. Generally, though, flight data are closed to the public view. In this paper a simple method is introduced to estimate flight trajectories using a commercial GPS receiver at a cabin of an in-flight airplane and numerical weather data. Barometric pressure altitude and Mach number were evaluated at the study. Results prove that airplanes follow almost exactly the predetermined airway and cruising altitude. Maximum deviation was recorded only at a magnitude of several dozen meters.
This paper describes design of a stability and control augmentation system (SAS and CAS) for a small prototype quad-tilt-wing (QTW) unmanned aerial vehicle (UAV) developed by Japan Aerospace Exploration Agency (JAXA). Since the dynamics of QTW is originally unstable at most of the tilt angles in the both longitudinal and lateral-directional motions, stabilizing control is necessary for the practical operations. In this paper, a gain scheduled PD-SAS is designed by solving linear matrix inequalities (LMIs) formulated for a static output feedback problem. In addition to SAS, CAS is designed to improve the pilot handling. The CAS is derived based on an integral-type optimal servomechanism and controller order reduction technique. Both the controller gains are scheduled with the tilt angles, since the dynamic characteristics of QTW change widely according to the tilt angles. Nonlinear human-in-the-loop simulation results have shown that the obtained gain scheduled controllers stabilize the QTW in almost all the tilt angles and provide good tracking performance for the attitude commands.
NomenclatureA p_i = system matrix of QTW model A c_i = system matrix of linearized PFCS model B p_i = input matrix of QTW model B c_i = input matrix of linearized PFCS model C p_i = measurement matrix of QTW model C c_i = measurement matrix of linearized PFCS model u,v,w = velocity of QTW along body axis p,q,r = angular velocity T d = time constant of PD controller τ w = tilt angle, deg stick θ δ = pitch stick input from remote ground pilot, % clc δ = collective stick input from remote ground pilot, % stick Φ δ = roll stick input from remote ground pilot, % stick Ψ δ = yaw stick input from remote ground pilot, % θ = pitch angle φ = bank angle Q y , Q u = weighting matrix 0 Γ = optimal value of a performance index
The statistics complied by the Boeing Co. show that the accident rates of commercial jet airplanes have been kept constant at a low level for the past 30 years or so. The accident rate remains constant, as an increase in number of accidents is predicted due to expansion of air traffic. Therefore, a new technology that is able to further decrease the accident rate is required. Human error like pilot's mistake could be mentioned as the most important factor of aircraft accidents. Especially, human errors, which occur at abnormal situations such as OEI or control surfaces failure, seem to become the direct causes of an accident. In the present study, a control support system is constructed by using our proposed linear model matching control method, which is capable of recovering the controllability under an OEI situation of a multiengine plane, with the object of preventing an accident due to situational pilot error.
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