<i>Flight Mechanics, Vol. 1, Theory of Flight Paths</i>

Tracking agile aircraft under high accelerations generally demands sophisticated models for determining trajectories with desirable accuracy. Often this raises complexity of the estimation algorithm as it gives rise to more elaborated methods for both taking model nonlinearities into account and handling a greater number of state variables that describe the model. The approach of this work recalls a 3D model based on flight dynamics of a point of mass for which augmentation to the Extended Kalman-Bucy filter (EKBF) is proposed. Two methods of augmentation to the EKBF filter are studied: (i) use of second-order terms to approximate the model according to the Daum's theory; (ii) deployment of a neural network coupled to the filter for compensation of modeling and calculation errors. The evaluation of the filters performance is accomplished by measuring nonlinearities, biasness, accuracy and robustness. The designed filters are suitably accurate and robust for tracking targets in air combat scenario.

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“…The limitations of well established models of the literature for target tracking were overcome by the use of the Miele model [8].…”

Section: Discussionmentioning

confidence: 99%

“…The update and prediction equations for the NEKBF are of the same form as (8), (9), (10), (11) and (12), with the exception that the vectors and matrices have to be substituted by the augmented ones:…”

Section: Ekbf -Neural Networkmentioning

confidence: 99%

Augmentation to the Extended Kalman-Bucy filter for single target tracking

Melo

Brancalion

Kienitz

2010

2010 13th International Conference on Information Fusion

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“…; mÞ: O i O iÀ1 is the separation vector. In this study, the coordinate systems for the ith aircraft of interest are the following: the ground axes system ðS gi Þ (not shown in the figure), the local horizontal system ðS hi Þ with its origin fixed on the aircraft (axes X hi ; Y hi ; Z hi ), the wind axes system S wi (axes # i i wi ; # j j wi ; # k k wi ) obtained from S hi by three successive rotations of heading angle (velocity yaw) ðw i Þ; flight path angle (velocity pitch) ðg i Þ; and bank angle (velocity roll) ðm i Þ; and the body axes system ðS bi Þ (not shown in the figure) obtained from S wi by two rotations, the sideslip angle Àb i and the angle of attack a i : Readers may refer to Etkin [23] and Miele [24] for the notation and the matrix equations relating various system of axes. Let # i i li ; # j j li ; # k k li be the unit vectors along the orthogonal axes of the system S li : Define a matrix M hwi which relates the coordinates of S hi to S wi (abbreviated as M hwi : S hi !…”

Section: Mathematical Modelmentioning

confidence: 99%

Decentralized nonlinear robust control of UAVs in close formation

Singh

Zhang

Chandler

et al. 2003

Intl J Robust & Nonlinear

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SUMMARYThis paper treats the design of a decentralized nonlinear robust control system for formation flying of multiple unmanned aerial vehicles (UAVs). In close formation, it is assumed that vortex of any UAV affects the motion of all the UAVs behind it. The forces produced by these vortices are complex functions of relative position co-ordinates of the UAVs. In this paper, these forces are treated as unknown functions, which cannot be parameterized. Since the system is not invertible in the wind axes system, a simplified coordinate system obtained from the wind axes system for which the velocity roll (bank angle) is zero, is considered for the design of the control system. A nonlinear robust control system for the separation trajectory control of the wing aircraft in the simplified wind coordinate system is derived. Uncertain functions and unmeasured variables are estimated using a high-gain observer for the synthesis of the control system. Each wing UAV synthesizes its control law using its own state variables and the relative position of the preceding UAV with respect to the wing UAV. Thus the control system is decentralized since each UAV has to communicate (depending on sensors for position measurement) with at most one (preceding) UAV, and no data transmission from the remaining vehicles is required. Simulation results for two UAVs are presented which show precise separation trajectory control of each wing UAV in spite of the presence of unknown and unstructured vortex forces, while the lead aircraft maneuvers. Furthermore, these results confirm that when the wing aircraft is positioned properly in the vortex of the lead aircraft, it experiences reduction in its required flight power.

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“…To determine the V velocity of the lost mass particles, Meshchersky equation can be used (Miele, 1962):…”

Section: Gravity As a Sum Of Reaction Forcesmentioning

confidence: 99%

The Hubble Law and Gravity as Manifestations of Linear Measurement Standards Variability

Groppen¹

2015

JMSIC

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An approach to study universe simulators based on the idea of linear measurement standards variability is discussed. It permits us to develop free of contradictions simulators of the universe, describing the Hubble law and gravity as manifestations of linear measurement standards variability. The latter allows us to interpret gravity also as manifestation of reaction forces in the anisotropic medium thus giving us a possibility to control gravity as well as conditions of experiments for its verification: the paper describes statement and results of our experiments which are based on the use of charged deployed capacitors and a high voltage for gravity control and for optimization of the experimental conditions.

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Flight Mechanics, Vol. 1, Theory of Flight Paths

Cited by 29 publications

References 0 publications

Augmentation to the Extended Kalman-Bucy filter for single target tracking

Augmentation to the Extended Kalman-Bucy filter for single target tracking

Decentralized nonlinear robust control of UAVs in close formation

The Hubble Law and Gravity as Manifestations of Linear Measurement Standards Variability

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