A New Approach for Modeling, Analysis and Control of Air Traffic Flow

Menon, P. K.; Sweriduk, Gregory D.; Bilimoria, Karl D.

doi:10.2514/6.2002-5012

Cited by 74 publications

(62 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Real-time flight control and swarm operation must also take into account high-fidelity six-degree-of-freedom (6-DOF) flight dynamic models, traffic variations, weather, and other time-varying operational conditions found in crowded urban environments. These aspects stand in stark contrast to those focused on 3-D air traffic flow control with much longer time horizon [7]- [9] as well as 2-D road traffic flow theory, bipartite matching, and transport operation theory that assume fixed flight pathways and road/route topologies [10]. Furthermore, existing air traffic control systems require human operators to perform real-time control of airport congestion and prevention of mid-air collision [7].…”

Section: Introductionmentioning

confidence: 71%

A Survey on Aerial Swarm Robotics

et al. 2018

View full text Add to dashboard Cite

Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

show abstract

Section: Introductionmentioning

confidence: 71%

A Survey on Aerial Swarm Robotics

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Thus, we can conclude that Positivity and Conservation hold for system (9) if C(η r (t)) hold for all r ∈ [R].…”

Section: Basic Control Design Objectivesmentioning

confidence: 99%

“…In contrast with the models analyzed in [1,9], we allow the outflow rate of each section to be a nonlinear function of its state, which represents the amount of material present in the section. Nonlinear outflow rates are of particular interest in air traffic management problems.…”

Section: Modelmentioning

confidence: 99%

“…Note that linear system (9) can be generated from the more general system (3) with η replacing β and outflow rate…”

Section: Basic Control Design Objectivesmentioning

confidence: 99%

“…Previous work in this area [1,9,10,12,13] has focused primarily on the use of linear models to describe the outflow of each compartment, or section, of the network. Such models describe the outflow rate of a section as depending linearly on the amount of material in that section.…”

mentioning

confidence: 99%

See 2 more Smart Citations

A linear programming approach to routing control in networks of constrained nonlinear positive systems with concave flow rates

2016

View full text Add to dashboard Cite

We consider control design for positive compartmental systems in which each compartment's outflow rate is described by a concave function of the amount of material in the compartment. We address the problem of determining the routing of material between compartments to satisfy time-varying state constraints while ensuring that material reaches its intended destination over a finite time horizon. We give sufficient conditions for the existence of a time-varying state-dependent routing strategy which ensures that the closed-loop system satisfies basic network properties of positivity, conservation and interconnection while ensuring that capacity constraints are satisfied, when possible, or adjusted if a solution cannot be found. These conditions are formulated as a linear programming problem. Instances of this linear programming problem can be solved iteratively to generate a solution to the finite horizon routing problem. Results are given for the application of this control design method to an example problem.

show abstract

A network‐based dynamic air traffic flow model for short‐term en route traffic prediction

Chen

et al. 2016

J of Advced Transportation

View full text Add to dashboard Cite

This paper presents a dynamic network-based approach for short-term air traffic flow prediction in en route airspace. A dynamic network characterizing both the topological structure of airspace and the dynamics of air traffic flow is developed, based on which the continuity equation in fluid mechanics is adopted to describe the continuous behaviour of the en route traffic. Building on the network-based continuity equation, the space division concept in cell transmission model is introduced to discretize the proposed model both in space and time. The model parameters are sequentially updated based on the statistical properties of the recent radar data and the new predicting results. The proposed method is applied to a real data set from Shanghai Area Control Center for the short-term air traffic flow prediction both at flight path and en route sector level. The analysis of the case study shows that the developed method can characterize well the dynamics of the en route traffic flow, thereby providing satisfactory prediction results with appropriate uncertainty limits. The mean relative prediction errors are less than 0.10 and 0.14, and the absolute errors fall in the range of 0 to 1 and 0 to 3 in more than 95% time intervals respectively, for the flight path and en route sector level. airport conditions, airline operation and human factors. [4]. Furthermore, the dimension of the model depends on the number of aircraft under consideration, which demands a huge computational cost in real context to resolve. Thus, in practice, it is difficult to make sound online air traffic control strategies based on the trajectory-based model due to the short forecast horizon and the expensive computational cost.Efficient ATFM requires reasonable prediction of the whole traffic flow situations in the specified airspace, rather than the temporal-spatial information of individual aircraft. Therefore, the aggregate air traffic flow models are introduced recently, which focus on the overall distribution of the air traffic flow in the airspace volumes of interest [5][6][7][8][9][10][11][12][13][18][19][20][21][22][23][24]. Because the aircrafts in the airspace volumes are spatially aggregated, the dimension of the aggregated model depends solely on the number of airspace volumes rather than the total number of aircrafts in the airspace, which will reduce the computational cost significantly. In addition, because the behaviour of the individual aircraft is not taken into account in the aggregated model, it is less sensitive to the uncertainty factors related to individual aircraft, such as the departure delay and the weather, and thus, a longer forecast time horizon with less prediction errors can be achieved.Recently, the aggregated approach is widely discussed in the literatures [5][6][7][8][9][10][11][12][13][18][19][20][21][22][23][24]. A stochastic framework with linear dynamic system model was developed by Sridhar et al., where the dimension of the model depends on the number of control volumes by introducing split parameters to d...

show abstract

A New Approach for Modeling, Analysis and Control of Air Traffic Flow

Cited by 74 publications

References 5 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

A linear programming approach to routing control in networks of constrained nonlinear positive systems with concave flow rates

A network‐based dynamic air traffic flow model for short‐term en route traffic prediction

Contact Info

Product

Resources

About