Control Augmentation Strategies for Helicopters used as Personal Aerial Vehicles

Gerboni, Carlo A.; Venrooij, Joost; Nieuwenhuizen, Frank M.; Joos, Alexander; Fichter, Walter; Bülthoff, Heinrich H.

doi:10.2514/6.2016-2137

Cited by 3 publications

(5 citation statements)

References 6 publications

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“…The discrete time step was chosen based on rough approximations and estimations of the dynamics of personal aerial vehicles [50,51]. Since the aim of the middle layer is to determine the prescribed behaviour of the bottom level and provide a reference to the guidance algorithm, therefore the time step size was selected considering the settling time of the step response dynamics, which is t = 5 s for both longitudinal velocity transfer functions in the cited references.…”

Section: Simulation Scenariomentioning

confidence: 99%

“…Speed limits were chosen taking into consideration the operation in an urban environment. Therefore the minimum speed is set as a = 15 Kn (below this speed, manoeuvres are considered low-speed [50,52]), the maximum speed is set as b = 180 Kn. Thus, the average roughly matches the speed forecast by Perfect et al [52] for the decision variable limits, and the range is in harmony with the characteristic speeds [2,41].…”

Section: Speed Distributionsmentioning

confidence: 99%

“…The dynamics model has a relationship with the task allocation adjacency matrices since it governs the available subsequent states (speeds), which is based on the level flight acceleration limits of the aircraft. Considering passenger comfort, the acceleration limit is specified as 1Kn/s ≈ 0.5m/s 2 [53], which seems to match the average acceleration capabilities of personal aerial vehicles [50]. Variation of acceleration limits with speed is not considered here.…”

Section: Speed Distributionsmentioning

confidence: 99%

“…For the simulations in this paper, threshold times of 30, 40, 50 and 60s are investigated. Similarly to TCAS [58], a 5s transition time is assumed based on settling time of the yaw transfer function [50], with a worst-case relative speed of b = 360Kn. For traffic management GPS and ADS-B technologies, separation limits have long been expected to become time-based, defined according to wake turbulence intensity [59].…”

Section: Description Of the Environmentmentioning

confidence: 99%

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Traffic conflict reduction based on distributed stochastic task allocation

2022

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The aim of this paper is to provide preliminary results on a traffic coordination framework based on stochastic task allocation. General trends and the predicted advent of personal aerial vehicles increase traffic rapidly, but current air traffic management methods admittedly cannot scale appropriately. A hierarchical system is proposed to overcome the problem, the middle layer of which is elaborated in this paper. This layer aims to enable stochastic control of traffic behaviour using a single parameter, which is achieved by applying distributed stochastic task allocation. The task allocation algorithm is used to allocate speeds to vehicles in a scalable way. By regulating the speed distribution of vehicles the conflict rates remain manageable. Multi-agent simulation results show that it is possible to control ensemble dynamics and together with that traffic safety and throughput via a single parameter. Using transient simulations the dynamic performance of the system is analysed. It is shown that the traffic conflict reduction problem can be transformed into a control design problem. The performance of a simple controller is also evaluated. It was shown that by applying the controller, quicker transients can be achieved for the mean speed of the system.

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Section: Simulation Scenariomentioning

confidence: 99%

Section: Speed Distributionsmentioning

confidence: 99%

Section: Speed Distributionsmentioning

confidence: 99%

Section: Description Of the Environmentmentioning

confidence: 99%

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Traffic conflict reduction based on distributed stochastic task allocation

2022

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“…3,37 For the minimum speed, handling characteristics were taken into account. 66,67 Therefore, for exponential distribution parameters were chosen as a,b=[0.0025,0.2] and for normal distribution the parameter range was selected as μ,ν=[5,90].…”

Section: Introductionmentioning

confidence: 99%

On the underlying dynamics of traffic conflicts related to stochastic behaviour

Oreg

Shin

Tsourdos

2022

Proceedings of the Institution of Mechanical Engineers, Part G:

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The aim of this paper is to analyse why and how air traffic conflicts occur as a result of the stochastic behaviour of both the ownship and the intruder and to show how system-level characteristics can be derived from such an analysis. Ensemble dynamics in a given traffic scenario have already been analysed using multi-agent simulations by many; however, such an analysis is hardly ever backed up and interpreted in terms of an analytical study. By making use of directional conflict probability maps, characteristics of integral, system-level quantities can be explained, providing further insight into the relationship of speed distribution parameters and system performance quantities, namely, safety and throughput.

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