The String Stability of a Trajectory-Based Interval Management Algorithm in the Midterm Airspace

Swieringa, Kurt A.

doi:10.2514/6.2015-2278

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…23,28 This simulation included strings of up to four IM aircraft. The schedule conformance was plotted as a function of aircraft sequence for each scenario to determine if it degraded as a function of string figure 14 in the Appendix).…”

Section: A Schedule Conformance At the Final Approach Fixmentioning

confidence: 99%

“…Both human-in-theloop simulations and fast-time simulations that investigated the performance of the ASTAR12 algorithm and its acceptability to pilots and controllers indicated that the ground speed term that was added to ASTAR12 improved compatibility with TMA-TM and CMS; however, there were cases where the ground speed term contributed to undesirable speed reversals. 14,22,23 The ASTAR12 algorithm was updated to support the Capture, Cross, and Maintain operations that were investigated in this simulation; the updated version of ASTAR is called ASTAR13. The primary modification was a new state-based CTD speed control law that was added to support the Capture and Maintain operations, and the "maintain" phase of the Cross operation; that is, the phase that occurs after the IM aircraft crosses the achieve-by point.…”

Section: Interval Management (Im)mentioning

confidence: 99%

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System Performance of an Integrated Airborne Spacing Algorithm with Ground Automation

Swieringa

Wilson

Baxley

2016

16th AIAA Aviation Technology, Integration, and Operations Conference

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The National Aeronautics and Space Administration's (NASA's) first Air Traffic Management (ATM) Technology Demonstration (ATD-1) was created to facilitate the transition of mature ATM technologies from the laboratory to operational use. The technologies selected for demonstration are the Traffic Management Advisor with Terminal Metering (TMA-TM), which provides precise time-based scheduling in the Terminal airspace; Controller Managed Spacing (CMS), which provides controllers with decision support tools to enable precise schedule conformance; and Interval Management (IM), which consists of flight deck automation that enables aircraft to achieve or maintain precise spacing behind another aircraft. Recent simulations and IM algorithm development at NASA have focused on trajectory-based IM operations where aircraft equipped with IM avionics are expected to achieve a spacing goal, assigned by air traffic controllers, at the final approach fix. The recently published IM Minimum Operational Performance Standards describe five types of IM operations. This paper discusses the results and conclusions of a human-in-the-loop simulation that investigated three of those IM operations. The results presented in this paper focus on system performance and integration metrics. Overall, the IM operations conducted in this simulation integrated well with ground-based decisions support tools and certain types of IM operational were able to provide improved spacing precision at the final approach fix; however, some issues were identified that should be addressed prior to implementing IM procedures into real-world operations.

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Section: A Schedule Conformance At the Final Approach Fixmentioning

confidence: 99%

Section: Interval Management (Im)mentioning

confidence: 99%

System Performance of an Integrated Airborne Spacing Algorithm with Ground Automation

Swieringa

Wilson

Baxley

2016

16th AIAA Aviation Technology, Integration, and Operations Conference

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, energy consumption was not a concern in their context. In [10,11], string stability and steady-state error were considered in the context of interval management, where air traffic controllers provide speed guidance to regulate the spacing between aircraft on landing approach. This demonstrates that even in a centralized control context that does not incorporate aircraft dynamic models or wake effects, string stability can be a relevant factor.…”

Section: Introductionmentioning

confidence: 99%

String stability of energy-saving aircraft formations

Riehl¹,

Hufstedler²,

Chatelain³

et al. 2021

Preprint

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This work was supported by the "RevealFlight" Concerted Research Action (ARC) of the Federation Wallonie-Bruxelles Groups of aircraft have the potential to save significant amounts of energy by flying in formations; all but the leading aircraft can benefit from the upwash of the wakes of preceding aircraft. A potential obstacle as the number of aircraft in such a formation increases is that disturbances at one aircraft, for example caused by turbulence or wake meandering, can propagate and grow as each following aircraft tries to track the optimal energy-saving position relative to the one in front. This phenomenon, known as string instability, has not yet been adequately examined in the context of aircraft formations. We discuss some trade-offs involved in designing string stable controllers whose objective is to minimize energy, and present a control design method to achieve both string stability and energy efficiency of an aircraft formation. In simulations of a 10-aircraft linear formation in the presence of 2% turbulence intensity, our controller achieves string stability while reduced energy consumption by an average of 13% with respect to solo flight.

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Statistical engineering approach to improve the realism of computer-simulated experiments with aircraft trajectory clustering

Wilson

Swieringa

Leonard

et al. 2016

Quality Engineering

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The String Stability of a Trajectory-Based Interval Management Algorithm in the Midterm Airspace

Cited by 6 publications

References 12 publications

System Performance of an Integrated Airborne Spacing Algorithm with Ground Automation

System Performance of an Integrated Airborne Spacing Algorithm with Ground Automation

String stability of energy-saving aircraft formations

Statistical engineering approach to improve the realism of computer-simulated experiments with aircraft trajectory clustering

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