Automatic formation flight control

Pachter, Meir; D'Azzo, John; Dargan, J.

doi:10.2514/3.21364

Cited by 110 publications

(46 citation statements)

References 2 publications

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“…Each UAV is assumed to be equipped with airspeed, heading, and flight path angle autopilots. Following Pachter et al in [1] and [2], the controlled UAV dynamics are modeled as first order systems as follows:…”

Section: Uav Dynamicsmentioning

confidence: 99%

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A collision-free formation reconfiguration control approach for Unmanned Aerial Vehicles

Lie

2010

Int. J. Control Autom. Syst.

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Formation flying of Unmanned Aerial Vehicles (UAVs) has gained a lot of interest due to its many potential advantages. Flying in formation allows wider sensing coverage area and in effect, this leads to improved surveillance and enhanced situational awareness. Also flying in formation eases coordination and data fusion. This paper presents the control architecture for fixed-wing UAV reconfiguration control using a novel combination of known techniques. The current premise is for the UAVs to assume their final target states within a specified time interval while avoiding collisions with one another or with an obstacle during the process. Some simulations are performed to assess the performance of the reconfiguration control scheme. The effects of the control parameters on the reconfiguration trajectories are also examined.

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Section: Uav Dynamicsmentioning

confidence: 99%

“…Also, flying in formation eases coordination and data fusion. Further, it has been shown that flying in formation has an advantage aerodynamically in terms of induced drag reduction [1,2].…”

Section: Introductionmentioning

confidence: 99%

A collision-free formation reconfiguration control approach for Unmanned Aerial Vehicles

Lie

2010

Int. J. Control Autom. Syst.

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“…For example, in [172] it is assumed a flight path angle control autopilot exists and a guidance law is constructed that takes heading rate and velocity constraints of the vehicle into account. The same holds for the formation control schemes of [155,156]. Usually the assumption is made that the autopilot response to heading and airspeed commands is first order in nature to simplify the design.…”

Section: Motivationmentioning

confidence: 99%

“…Inertial trajectory control is essential for these UAVs, since they are usually required to follow predetermined paths through certain target points in the threedimensional air space [29,96,97,151,171,172,184]. Other situations where trajectory control is desired include formation control, aerial refueling and autonomous landing maneuvers [68,155,156,168,182,207]. This has lead to a lot of literature dedicated to formation and flight path control for UAVs, but also for other types of (un)manned vehicles [80,146].…”

Section: Motivationmentioning

confidence: 99%

“…Two different approaches can be distinguished in the design of these trajectory control systems. The most popular approach is to separate the guidance and control laws: A given reference trajectory is converted by the guidance laws to velocity and attitude commands for the actual flight controller which in turn generates the actuator signals [155,156,172]. For example, in [172] it is assumed a flight path angle control autopilot exists and a guidance law is constructed that takes heading rate and velocity constraints of the vehicle into account.…”

Section: Motivationmentioning

confidence: 99%

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Adaptive Backstepping Flight Control for Modern Fighter Aircraft

Sonneveldt¹

2011

Advances in Flight Control Systems

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Consensus of fractional‐order multi‐agent systems using iterative learning control in the sense of Lebesgue‐p norm

Lan

Dai

2021

Asian Journal of Control

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In this paper, open loop and closed loop PDα‐type fractional‐order iterative learning control algorithms with fixed topology for the fractional‐order distributed multi‐agent systems are proposed. In the sense of Lebesgue‐p norm, the convergences of the proposed methods are discussed by using the generalized Young inequality of convolution, and their convergence conditions are presented. The proposed algorithm extends the application scope of traditional iterative learning control theory. Theoretical analysis shows that by selecting the appropriate learning gain matrix, the output of each agent can completely track the desired trajectory in a limited time interval as the number of iterations increases. The feasibility and theoretical correctness of the presented algorithm are verified by numerical simulations.

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Automatic formation flight control

Cited by 110 publications

References 2 publications

A collision-free formation reconfiguration control approach for Unmanned Aerial Vehicles

A collision-free formation reconfiguration control approach for Unmanned Aerial Vehicles

Adaptive Backstepping Flight Control for Modern Fighter Aircraft

Consensus of fractional‐order multi‐agent systems using iterative learning control in the sense of Lebesgue‐p norm

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