Simulation Analysis of the Effect of Relaxation Time on Flight Delays Propagation

Relaxation time of flights is one of the main factors that affect the diffusion of flight delays, but the specific relationship between them is ambiguous.？Gaining a clear idea of their relationship conduces to the control of flight delays.？Through the establishment of the aviation network model and simulation analysis of the effect of relaxation time on delay spread, it can be found that the relaxation time is inversely proportional to the total delay time and the number of airports that have been delayed due to the delay spread, and there is no evident linear relationship between the relaxation time and the average delay time.？This demonstrates that increasing the relaxation time properly can reduce the propagation of flight delays and improve the punctuality rate of flights.

FLIGHT DELAY STATE-SPACE MODEL BASED ON GENETIC EM ALGORITHM

Flight delay prediction remains an important research topic due to dynamic nature in flight operation and numerous delay factors.Dynamic data-driven application system in the control area can provide a solution to this problem.However,in order to apply the approach,a state-space flight delay model needs to be established to represent the relationship among system states,as well as the relationship between system states and input/output variables.Based on the analysis of delay event sequence in a single flight,a state-space mixture model is established and input variables in the model are studied.Case study is also carried out on historical flight delay data.In addition,the genetic expectation-maximization（EM）algorithm is used to obtain the global optimal estimates of parameters in the mixture model,and results fit the historical data.At last,the model is validated in Kolmogorov-Smirnov tests.Results show that the model has reasonable goodness of fitting the data,and the search performance of traditional EM algorithm can be improved by using the genetic algorithm.

Characterizing Flight Delay Profiles with a Tensor Factorization Framework

In air traffic and airport management,experience gained from past operations is crucial in designing appropriate strategies when facing a new scenario.Therefore,this paper uses massive spatiotemporal flight data to identify similar traffic and delay patterns,which become critical for gaining a better understanding of the aviation system and relevant decision-making.However,as the datasets imply complex dependence and higher-order interactions between space and time,retrieving significant features and patterns can be very challenging.In this paper,we propose a probabilistic framework for highdimensional historical flight data.We apply a latent class model and demonstrate the effectiveness of this framework using air traffic data from 224 airports in China during 2014–2017.We find that profiles of each dimension can be clearly divided into various patterns representing different regular operations.To prove the effectiveness of these patterns,we then create an estimation model that provides preliminary judgment on the airport delay level.The outcomes of this study can help airport operators and air traffic managers better understand air traffic and delay patterns according to the experience gained from historical scenarios.

FLIGHT CLASSIFICATION MODEL BASED ON TRANSITIVE CLOSURE ALGORITHM AND APPLICATION TO FLIGHT SEQUENCING PROBLEM

A new arrival and departure flight classification method based on the transitive closure algorithm （TCA） is proposed. Firstly, the fuzzy set theory and the transitive closure algorithm are introduced. Then four different factors are selected to establish the flight classification model and a method is given to calculate the delay cost for each class. Finally, the proposed method is implemented in the sequencing problems of flights in a terminal area, and results are compared with that of the traditional classification method（TCM）. Results show that the new classification model is effective in reducing the expenses of flight delays, thus optimizing the sequences of arrival and departure flights, and improving the efficiency of air traffic control.

Stochastic Air Traffic Flow Management for Demand and Capacity Balancing Under Capacity Uncertainty

This paper introduces an innovative approach to the synchronized demand-capacity balance with special focus on sector capacity uncertainty within a centrally controlled collaborative air traffic flow management(ATFM)framework.Further with previous study,the uncertainty in capacity is considered as a non-negligible issue regarding multiple reasons,like the impact of weather,the strike of air traffic controllers(ATCOs),the military use of airspace and the spatiotemporal distribution of nonscheduled flights,etc.These recessive factors affect the outcome of traffic flow optimization.In this research,the focus is placed on the impact of sector capacity uncertainty on demand and capacity balancing(DCB)optimization and ATFM,and multiple options,such as delay assignment and rerouting,are intended for regulating the traffic flow.A scenario optimization method for sector capacity in the presence of uncertainties is used to find the approximately optimal solution.The results show that the proposed approach can achieve better demand and capacity balancing and determine perfect integer solutions to ATFM problems,solving large-scale instances(24 h on seven capacity scenarios,with 6255 flights and 8949 trajectories)in 5-15 min.To the best of our knowledge,our experiment is the first to tackle large-scale instances of stochastic ATFM problems within the collaborative ATFM framework.

Bilevel Programming Model for Joint Scheduling of Arrival and Departure Flights Based on Traffic Scenario

In order to meet the needs of collaborative decision making,considering the different demands of air traffic control units,airlines,airports and passengers in various traffic scenarios,the joint scheduling problem of arrival and departure flights is studied systematically.According to the matching degree of capacity and flow,it is determined that the traffic state of arrival/departure operation in a certain period is peak or off-peak.The demands of all parties in each traffic state are analyzed,and the mathematical models of arrival/departure flight scheduling in each traffic state are established.Aiming at the four kinds of joint operation traffic scenarios of arrival and departure,the corresponding bi-level programming models for joint scheduling of arrival and departure flights are established,respectively,and the elitism genetic algorithm is designed to solve the models.The results show that:Compared with the first-come-firstserved method,in the scenarios of arrival peak&departure off-peak and arrival peak&departure peak,the departure flight equilibrium satisfaction is improved,and the runway occupation time of departure flight flow is reduced by 38.8%.In the scenarios of arrival off-peak&departure off-peak and departure peak&arrival off-peak,the arrival flight equilibrium delay time is significantly reduced,the departure flight equilibrium satisfaction is improved by 77.6%,and the runway occupation time of departure flight flow is reduced by 46.6%.Compared with other four kinds of strategies,the optimal scheduling method can better balance fairness and efficiency,so the scheduling results are more reasonable.

Reference tracking control for flexible air-breathing hypersonic vehicle with actuator delay and uncertainty

This paper considers the problem of reference tracking control for the flexible air-breathing hypersonic flight vehicle with actuator delay and uncertainty.By constructing the Lyapunov functional including the lower and upper bounds of the time-varying delay,the non-fragile controller is designed such that the resulting closed-loop system is asymptotically stable and satisfies a prescribed performance cost index.The simulation results are given to show the effectiveness of the proposed control method,which is validated by excellent output reference altitude and velocity tracking performance.

FPGA-based High-precision Measurement Algorithm for the Ultrasonic Echo Time of Flight

Based on the evaluation of advantages and disadvantages of high-precision digital time interval measuring algorithms, and combined with the principle of the typical time-difference ultrasonic flow measurement, the requirements for the measurement of echo time of flight put forward by the ultrasonic flow measurement are analyzed. A new high-precision time interval measurement algorithm is presented, which combines the pulse counting method with the phase delay interpolation. The pulse counting method is used to ensure a large dynamic measuring range, and a double-edge triggering counter is designed to improve the accuracy and reduce the counting quantization error. The phase delay interpolation is used to reduce the quantization error of pulse counting for further improving the time measurement resolution. Test data show that the systexn for the measurement of the ultrasonic echo time of flight based on this algorithm and implemented on an Field Programmable Gate Army（FleA） needs a relatively short time for measurement, and has a measurement error of less than 105 ps.

ROBUST STABILITY CRITERIA FOR SYSTEM WITH DELAY & ITS APPLICATION TO FCS

A study is devoted to the delay robust stability for linear system with time delay, using matrix analysis and comparison theory on differential integral difference equations. Since the matrix norm used to derive the results may be arbitrary consistent matrix norm, the obtained results possess some agility, and it makes quite convenient to check stability for linear systems with time delay. In particular, a sufficient and necessary condition for delay robust stability is obtained, and a formula to calculate the maximum delay for systems with stability is given. At the end, the results are used to analyze a flight control system (FCS). Comparison with some literature shows that the criteria in this paper are not only simple, but also of small conservativeness.

Networked guaranteed cost PID attitude tracking control of a laboratory flight system

This paper investigates the networked flight control system for a laboratory 3 degrees of freedom (3-DOF) helicopter,and presents a novel networked guaranteed cost proportion-integration-differentiation (PID) attitude tracking control method with consideration of time-varying delay and packet dropouts.As the 3-DOF helicopter characteristics of multi-input multi-output (MIMO),channel coupling,and nonlinearity,a genera linear time delay system is modeled by analyzing the motions on elevation,pitch,and travel axis.By using the reciprocal convex approach,the free weight matrix,and the cone complementarity linearization (CCL) method,the PID tracking controller parameters can be designed if the related linear matrix inequalities (LMIs) are feasible.Finally,a practical experiment of laboratory 3-DOF helicopter is given,and the experimental results show that the proposed method is effective.

A geographical and operational deep graph convolutional approach for flight delay prediction

Flight delay prediction has attracted great interest in civil aviation community due to its significant role in airline planning,flight scheduling,airport operation,and passenger service.Flight delay is affected by numerous factors and irregularly propagates in air transportation networks owing to flight connectivity,which brings critical challenges to accurate flight delay prediction.In recent years,Graph Convolutional Networks(GCNs)have become popular in flight delay prediction due to the advantage in extracting complicated relationships.However,most of the existing GCN-based methods have failed to effectively capture the spatial-temporal information in flight delay prediction.In this paper,a Geographical and Operational Graph Convolutional Network(GOGCN)is proposed for multi-airport flight delay prediction.The GOGCN is a GCN-based spatial-temporal model that improves node feature representation ability with geographical and operational spatial-temporal interactions in a graph.Specifically,an operational aggregator is designed to extract global operational information based on the graph structure,while a geographical aggregator is developed to capture the similar nature among spatially close airports.Extensive experiments on a real-world dataset demonstrate that the proposed approach outperforms the state-of-the-art methods with a satisfying accuracy improvement.

Closed-loop fault detection for full-envelope flight vehicle with measurement delays

Abstract A closed-loop fault detection problem is investigated for the full-envelope flight vehicle with measurement delays, where the flight dynamics are modeled as a switched system with delayed feedback signals. The mode-dependent observer-based fault detection filters and state estimation feedback controllers are derived by considering the delays＇ impact on the control system and fault detection system simultaneously. Then, considering updating lags of the controllers/filters＇ switching signals which are introduced by the delayed measurement of altitude and Mach number, an asynchronous H analysis method is proposed and the system model is further augmented to be an asynchronously switched time-delay system. Also, the global stability and desired performance of the augmented system are guaranteed by combining the switched delay-dependent Lyapunov Krasovskii functional method with the average dwell time method （ADT）, and the delaydependent existing conditions for the controllers and fault detection filters are obtained in the form of the linear matrix inequalities （LMIs）, Finally, numerical example based on the hypersonic vehicles and highly maneuverable technology （HiMAT） vehicle is given to demonstrate the merits of the proposed method.

H_∞ output tracking control for flight control systems with time-varying delay

For flight control systems with time-varying delay, an H∞ output tracking controller is proposed. The controller is designed for the discrete-time state-space model of general aircraft to reduce the effects of uncertainties of the mathematical model, external disturbances, and bounded time-varying delay. It is assumed that the feedback-control loop is closed by the communication network, and the network-based control architecture induces time-delays in the feedback information. Suppose that the time delay has both an upper bound and a lower bound. By using the Lyapu- nov-Krasovskii function and the linear matrix inequality （LMI）, the delay-dependent stability criterion is derived for the time-delay system. Based on the criterion, a state-feedback H∞ output tracking controller for systems with norm-bounded uncertainties and time-varying delay is presented. The control scheme is applied to the high incidence research model （HIRM）, which shows the effectiveness of the proposed approach.

Collaborative slot secondary allocation based on flight wave operation

Purpose-Flights are often delayed owing to emergencies.This paper proposes a cooperative slot secondary assignment(CSSA)model based on a collaborative decision-making(CDM)mechanism,and the operation mode of flight waves designs an improved intelligent algorithm to solve the optimal flight plan and minimize the total delay of passenger time.Design/methodology/approach-Taking passenger delays,transfer delays and flight cancellation delays into account comprehensively,the total delay time is minimized as the objective function.The model is verified by a linear solver and compared with the first come first service(FCFS)method to prove the effectiveness of the method.An improved adaptive partheno-genetic algorithm(IAPGA)using hierarchical serial number coding was designed,combining elite and roulette strategies to find pareto solutions.Findings-Comparing and analyzing the experimental results of various scale examples,the optimization model in this paper is greatly optimized compared to the FCFS method in terms of total delay time,and the IAPGA algorithm is better than the algorithm before in terms of solution performance and solution set quality.Originality/value-Based on the actual situation,this paper considers the operation mode of flight waves.In addition,the flight plan solved by the model can be guaranteed in terms of feasibility and effectiveness,which can provide airlines with reasonable decision-making opinions when reassigning slot resources.

Models of Choosing the Financial Compensation Strategy for Flight Delay

At present, most airlines adopted generally the same amount of compensa- tion strategy when needing to provide financial compensation to all flight delay passengers. However, due to the existence of differences in travel time value, ticket fare, as well as the expectation of compensation for flight delays, the gap between the effect of same amount of compensation and many passengers＇ （especially the high-value ones） expectations is large, it results in that airlines need to pay higher cost of compensation, but the total effect of compensation for passengers are not better. This paper advanced four financial com- pensation strategies for flight delays, summarized their own characteristics, and took into account the interests of both airlines and passengers, built the optimization models of the four financial compensation strategies under the restriction of the airline＇s compensation cost and on the goal of the maximum total effectiveness of the financial compensation to all passengers. Finally, based on the specific circumstances of the flight delays, the paper discussed the method for airline to choose the optimal financial compensation strategy through solving four models and comparing the compensation effectiveness.

Network science approach for identifying disruptive elements of an airline

Currently,flight delays are common and they propagate from an originating flight to connecting flights,leading to large disruptions in the overall schedule.These disruptions cause massive economic losses,affect airlines’reputations,waste passengers’time and money,and directly impact the environment.This study adopts a network science approach for solving the delay propagation problem by modeling and analyzing the flight schedules and historical operational data of an airline.We aim to determine the most disruptive airports,flights,flightconnections,and connection types in an airline network.Disruptive elements are influential or critical entities in an airline network.They are the elements that can either cause(airline schedules)or have caused(historical data)the largest disturbances in the network.An airline can improve its operations by avoiding delays caused by the most disruptive elements.The proposed network science approach for disruptive element analysis was validated using a case study of an operating airline.The analysis indicates that potential disruptive elements in a schedule of an airline are also actual disruptive elements in the historical data and they should be considered to improve operations.The airline network exhibits small-world effects and delays can propagate to any part of the network with a minimum of four delayed flights.Finally,we observed that passenger connections between flights are the most disruptive connection type.Therefore,the proposed methodology provides a tool for airlines to build robust flight schedules that reduce delays and propagation.

Locally generalised multi-agent reinforcement learning for demand and capacity balancing with customised neural networks

Reinforcement Learning(RL)techniques are being studied to solve the Demand and Capacity Balancing(DCB)problems to fully exploit their computational performance.A locally gen-eralised Multi-Agent Reinforcement Learning(MARL)for real-world DCB problems is proposed.The proposed method can deploy trained agents directly to unseen scenarios in a specific Air Traffic Flow Management(ATFM)region to quickly obtain a satisfactory solution.In this method,agents of all flights in a scenario form a multi-agent decision-making system based on partial observation.The trained agent with the customised neural network can be deployed directly on the corresponding flight,allowing it to solve the DCB problem jointly.A cooperation coefficient is introduced in the reward function,which is used to adjust the agent’s cooperation preference in a multi-agent system,thereby controlling the distribution of flight delay time allocation.A multi-iteration mechanism is designed for the DCB decision-making framework to deal with problems arising from non-stationarity in MARL and to ensure that all hotspots are eliminated.Experiments based on large-scale high-complexity real-world scenarios are conducted to verify the effectiveness and efficiency of the method.From a statis-tical point of view,it is proven that the proposed method is generalised within the scope of the flights and sectors of interest,and its optimisation performance outperforms the standard computer-assisted slot allocation and state-of-the-art RL-based DCB methods.The sensitivity analysis preliminarily reveals the effect of the cooperation coefficient on delay time allocation.

Trajectory tracking control of a VTOL unmanned aerial vehicle using offset-free tracking MPC

Designing a stable and robust flight control system for an Unmanned Aerial Vehicle(UAV)is an arduous task.This paper addresses the trajectory tracking control problem of a Ducted Fan UAV(DFUAV)using offset-free Model Predictive Control(MPC)technique in the presence of various uncertainties and external disturbances.The designed strategy aims to ensure adequate flight robustness and stability while overcoming the effects of time delays,parametric uncertainties,and disturbances.The six degrees of freedom DFUAV model is divided into three flight modes based on its airspeed,namely the hover,transition,and cruise mode.The Dryden wind turbulence is applied to the DFUAV in the linear and angular velocity component.Moreover,different uncertainties such as parametric,time delays in state and input,are introduced in translational and rotational components.From the previous work,the Linear Quadratic Tracker with Integrator(LQTI)is used for comparison to corroborate the performance of the designed controller.Simulations are computed to investigate the control performance for the aforementioned modes and different flight phases including the autonomous flight to validate the performance of the designed strategy.Finally,discussions are provided to demonstrate the effectiveness of the given methodology.

Method for measuring promptγ-rays generated by D-T neutrons bombarding a depleted uranium spherical shell

The prompt T-ray spectrum from depleted uranium （DU） spherical shells induced by 14 MeV D-T neutrons is measured. Monte Carlo （MC） simulation gives the largest prompt 2/ flux with the optimal thickness of the DU spherical shells 3-5 cm and the optimal frequency of neutron pulse 1 MHz. The method of time of flight and pulse shape coincidence with energy （DC-TOF） is proposed, and the subtraction of the background y-rays discussed in detail. The electron recoil spectrum and time spectrum of the prompt γ-rays are obtained based on a 2＇＇ × 2＇＇ BC501A liquid scintillator detector. The energy spectrum and time spectrum of prompt γ-rays are obtained based on an iterative unfolding method that can remove the influence of γ-rays response matrix and pulsed neutron shape. The measured time spectrum and the calculated results are roughly consistent with each other. Experimental prompt γ-ray spectrum in the 0.4-3 MeV energy region agrees well with MC simulation based on the ENDF/BVI.5 library, and the discrepancies for the integral quantities ofγ-rays of energy 0.4-1 MeV and 1 3 MeV are 9.2% and 1.1%, respectively.