State estimation for hybrid systems: applications to aircraft tracking

Hwang, Inseok; Balakrishnan, Hamsa; Tomlin, Claire J.

doi:10.1049/ip-cta:20050053

Cited by 82 publications

(48 citation statements)

References 24 publications

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“…In addition Lagrangian models have been used to predict sector capacities allowing them to be put into the framework of Eulerian models. Key progress in this area has been in improved differential models of air traffic flow, improved hybrid state estimation and the creation of application specific polynomial time algorithms that can be used in lieu of integer programming [6,22,36,45]. Instead of predicting the path of individual aircraft, Eulerian and aggregate flow models predict flow patterns in the airspace [7,13,32].…”

Section: Traditional Air Traffic Management Approachesmentioning

confidence: 99%

A multiagent approach to managing air traffic flow

Agogino

Tumer

2010

Auton Agent Multi-Agent Syst

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Abstract. Intelligent air traffic flow management is one of the fundamental challenges facing the Federal Aviation Administration (FAA) today. FAA estimates put weather, routing decisions and airport condition induced delays at 1,682,700 hours in 2007 [18], resulting in a staggering economic loss of over $41 Billion [42]. New solutions to the flow management are needed to accommodate the threefold increase in air traffic anticipated over the next two decades. Indeed, this is a complex problem where the interactions of changing conditions (e.g., weather), conflicting priorities (e.g., different airlines), limited resources (e.g., air traffic controllers) and heavy volume (e.g., over 40,000 flights over the US airspace) demand an adaptive and robust solution.In this paper we explore a multiagent algorithm where agents use reinforcement learning to reduce congestion through local actions. Each agent is associated with a fix (a specific location in 2D space) and has one of three actions: setting separation between airplanes, ordering ground delays or performing reroutes. We simulate air traffic using FACET which is an air traffic flow simulator developed at NASA and used extensively by the FAA and industry. Our FACET simulations on both artificial and real historical data from the Chicago and New York airspaces show that agents receiving personalized rewards reduce congestion by up to 80% over agents receiving a global reward and by up to 90% over a current industry approach (Monte Carlo estimation).

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Section: Traditional Air Traffic Management Approachesmentioning

confidence: 99%

A multiagent approach to managing air traffic flow

Agogino

Tumer

2010

Auton Agent Multi-Agent Syst

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“…The α -β and α -β -r algorithms for aircraft tracking have been addressed in [3]. The multiple model estimation based algorithms considering the maneuvering of an aircraft have been proposed in [4,5]. Trajectory prediction can be classified into nominal, worst-case, probabilistic approaches [6].…”

Section: Introductionmentioning

confidence: 99%

ADS-B based Trajectory Prediction and Conflict Detection for Air Traffic Management

Baek¹,

Bang²

2012

International Journal of Aeronautical and Space Sciences

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The Automatic Dependent Surveillance Broadcast (ADS-B) system is a key component of CNS/ATM recommended by the International Civil Aviation Organization (ICAO) as the next generation air traffic control system. ADS-B broadcasts identification, positional data, and operation information of an aircraft to other aircraft, ground vehicles and ground stations in the nearby region. This paper explores the ADS-B based trajectory prediction and the conflict detection algorithm. The multiple-model based trajectory prediction algorithm leads accurate predicted conflict probability at a future forecast time. We propose an efficient and accurate algorithm to calculate conflict probability based on approximation of the conflict zone by a set of blocks. The performance of proposed algorithms is demonstrated by a numerical simulation of two aircraft encounter scenarios.

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“…Control performance is generally improved with better state estimates; however, this must be verified through testing. SHS state estimation problems are often solved with 'multiple model' estimation schemes that involve a filter for each mode [9]. However, with large numbers of modes, as in our problem, these approaches are infeasible [10].…”

Section: Introductionmentioning

confidence: 99%

Demand response with moving horizon estimation of individual thermostatic load states from aggregate power measurements

Vrettos

Mathieu

Andersson

2014

2014 American Control Conference

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We present an optimization-based state estimation method that allows us to estimate the states of individual thermostatically controlled loads (TCLs), such as air conditioners and space heaters, from measurements of the power consumption of small aggregations of TCLs. The state estimator can be used together with a controller to provide ancillary services to power systems such as frequency control. The main advantage of this method is that it is designed to work with existing communication infrastructure. We assume that aggregate power measurements are available from distribution substations every few seconds, while TCL state measurements are available from smart meters only every 20 minutes. We model TCLs as hybrid systems and propose a moving horizon state estimator (MHSE), which is formulated as a mixed-integer linear program. We demonstrate the performance of the MHSE in two case studies: (a) estimation of TCL states in the absence of external control actions, and (b) a power tracking problem with closed-loop control using broadcast control inputs. To demonstrate the robustness of the method, we conduct a parametric analysis with respect to aggregation size and diversity, process noise characteristics, and control trajectory characteristics. The results show that the method generally provides accurate estimates of TCL states, resulting in improved controller performance in most cases, and is implementable in real-time with reasonable computational power.

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State estimation for hybrid systems: applications to aircraft tracking

Cited by 82 publications

References 24 publications

A multiagent approach to managing air traffic flow

A multiagent approach to managing air traffic flow

ADS-B based Trajectory Prediction and Conflict Detection for Air Traffic Management

Demand response with moving horizon estimation of individual thermostatic load states from aggregate power measurements

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