CN106340209A - Control method of air traffic control system for 4D trajectory-based operation - Google Patents

Abstract

The invention discloses a control method of an air traffic control system for 4D trajectory-based operation. The air traffic control system comprises a data communication module, a monitoring data fusion module, an airborne terminal module and a control terminal module, wherein the monitoring data fusion module is used for fusing the monitoring data of an air traffic control radar and automatic dependent monitoring data, and providing real-time trajectory information for the control terminal module; and the control terminal module comprises a preflight conflict-free 4D trajectory generation sub-module, an in-flight short-term 4D trajectory generation sub-module, a real-time flight conflict monitoring and alarming sub-module and a flight conflict resolution 4D trajectory optimization sub-module. According to the air traffic control method of the system of the invention, the control terminal module is utilized to process flight plan data, 4D trajectories are generated by using the hidden Markov model, and therefore, potential traffic conflicts of air traffic conditions can be analyzed; and a model prediction control theory method is adopted to provide an optimal resolution scheme. With the control method adopted, flight conflicts can be effectively prevented, and the safety of air traffic can be improved.

Description

The method of control of the air traffic control system run based on 4d flight path

The application is Application No.: 201510007981.7, invention and created name is the " control of air traffic control system Method ", the applying date is: the divisional application of the application for a patent for invention on January 7th, 2015.

Technical field

The present invention relates to a kind of air traffic control system and method, more particularly, to a kind of aerial based on the operation of 4d flight path Traffic control system and method.

Background technology

With fast-developing the becoming increasingly conspicuous with spatial domain resource-constrained contradiction of World Airways transport service, traffic flow is close in the air The complicated spatial domain of collection, still gradually shows its backwardness using the air traffic control mode that flight plan combines interval allotment Property, it is in particular in: (1) flight plan is not airborne vehicle configuration accurate blank pipe interval, easily cause traffic flow tactics pipe Crowded in reason, reduce spatial domain security；(2) reckoning to flight profile, mission profile for the air traffic control automation system centered on flight plan With Trajectory Prediction low precision, cause conflict dissolution ability；(3) job of air traffic control still lays particular emphasis on the single aviation of holding Personal distance between device, is difficult to rise to and carries out strategic Management to traffic flow.

4d flight path is with room and time form, in a certain airborne vehicle flight path each point locus (longitude, latitude and Highly) and the time accurate description, refer to use " control arrival time " on the way point of 4d flight path based on the operation of flight path, Airborne vehicle is controlled to pass through " time window " of specific way point.In high density spatial domain the operation based on 4d flight path (trajectory based operation) as one of basic operating mechanism, be following to big flow, high density, closely-spaced Under the conditions of spatial domain implement a kind of effective means of management, can significantly decrease the uncertainty of airborne vehicle flight path, improve spatial domain Security with Airport Resources and utilization rate.

Need on strategic level, single aircraft flight path to be carried out based on the air traffic method of operation that flight path runs Calculate and optimize, the traffic flow that many airborne vehicles are constituted is implemented collaborative and adjusts；Pre- tactical level passes through revise traffic flow In indivedual airborne vehicles flight path to solve congestion problems, and ensure the operational efficiency of all airborne vehicles in this traffic flow；And in war In art aspect, scheme is freed in prediction conflict and optimization, and airborne vehicle headway management is changed into consideration aviation from fixing manual type The factors such as device performance, regulation rule and environment in interior variable Separation control mode, therefore towards 4d flight path operation to sky Middle traffic control proposes new requirement.

Content of the invention

The technical problem to be solved in the present invention is to be to overcome the deficiencies in the prior art, provides a kind of 4d flight path that is based on to run The air traffic control system run based on 4d flight path method of control, can effectively prevent flight collision, improve air traffic Security.

The technical scheme realizing the object of the invention is to provide a kind of air traffic control system run based on 4d flight path Method of control, described air traffic control system include Airborne Terminal module, data communication module, monitor data fusion module with And control terminal module；Monitor that data fusion module is used for realizing air traffic control radar monitoring melting of data and automatic dependent surveillance data Close, provide real-time flight path information for control terminal module；

Described control terminal module includes following submodule:

Lothrus apterus 4d flight path generation module before flight, according to the forecast data of flight plan and world area forecast system, Set up airborne vehicle kinetic model, then set up flight path conflict pre- allotment theoretical model according to flight collision Coupling point, generate boat Pocket Lothrus apterus 4d flight path；

Flight middle or short term 4d flight path generation module, according to the real-time flight path information monitoring that data fusion module provides, utilizes HMM is thus it is speculated that airborne vehicle 4d track in following certain time window；

Real-time flight conflict monitoring and alarm module, for set up from airborne vehicle continuously dynamically to discrete conflict logic Observer, the conflict situation that the continuous dynamic mapping of Air Traffic System is expressed for discrete observation value；When system is possible to disobey During anti-air traffic control rules, Hybrid dynamics behavior implementing monitoring to air traffic hybrid system, provide for controller and When warning information；

Solving Flight Conflicts 4d track optimization module, is ensureing that system meets aircraft performance and regulation rule constraints Under, by select different free object function, using Model Predictive Control Theory method, calculate airborne vehicle conflict Resolution 4d boat Mark；And airborne vehicle conflict Resolution 4d flight path is sent to by the execution of Airborne Terminal module by data communication module；

The method of control of the described air traffic control system run based on 4d flight path includes several steps as follows:

Before step a, flight, Lothrus apterus 4d flight path generation module is according to the forecast of flight plan and world area forecast system Data, sets up airborne vehicle kinetic model, and sets up flight path conflict pre- allotment theoretical model according to flight collision Coupling point, generates Airborne vehicle Lothrus apterus 4d flight path；

Air traffic control radar is monitored that data and automatic dependent surveillance data are merged by step b, supervision data fusion module, raw Become airborne vehicle real-time flight path information and be supplied to control terminal module；Flight middle or short term 4d flight path in control terminal module generates Module speculates the airborne vehicle 4d track in following certain time window according to airborne vehicle real-time flight path information and history flight path information；Institute State the tool of the airborne vehicle 4d track speculating in following certain time window according to airborne vehicle real-time flight path information and history flight path information Body implementation process is as follows:

Step b6, to airborne vehicle track data pre-process, according to acquired airborne vehicle original discrete two-dimensional position sequence x =[x₁,x₂,...,x_n] and y=[y₁,y₂,...,y_n], it is carried out process the new airborne vehicle of acquisition using first-order difference method Discrete location sequence △ x=[△ x₁,△x₂,...,△x_n-1] and △ y=[△ y₁,△y₂,...,△y_n-1], wherein △ x_b= x_b+1-x_b,△y_b=y_b+1-y_b(b=1,2 ..., n-1)；

Step b7, airborne vehicle track data is clustered, to new airborne vehicle discrete two-dimensional position sequence △ x and △ after processing Y, by setting cluster number m', is clustered to it respectively using k-means clustering algorithm；

Step b8, to cluster after airborne vehicle track data carry out parameter training using HMM, by will Airborne vehicle running orbit data △ x and △ y after process is considered as the aobvious observation of hidden Markov models, by setting hidden state Number n' and parameter update period ζ ', rolled according to t' nearest position detection value and using b-w algorithm and obtain up-to-date hidden horse Er Kefu model parameter λ '；

Step b9, foundation HMM parameter, are obtained corresponding to current time observation using viterbi algorithm Hidden state q；

Step b10, by setting prediction time domain h', based on hidden state q of airborne vehicle current time, obtain future time period boat Position prediction value o of pocket；

Step c, real-time flight conflict monitoring and alarm module set up from airborne vehicle continuously dynamically to discrete conflict logic Observer, the conflict situation that the continuous dynamic mapping of Air Traffic System is expressed for discrete observation value；When system is possible to When violating air traffic control rules, the Hybrid dynamics behavior implementing monitoring to air traffic hybrid system, provide for controller Timely warning information；

Step d, Solving Flight Conflicts 4d track optimization module meet aircraft performance and regulation rule about ensureing system Under the conditions of bundle, by select different free object function, using Model Predictive Control Theory method, calculate airborne vehicle conflict solution De- 4d flight path；And airborne vehicle conflict Resolution 4d flight path is sent to by the execution of Airborne Terminal module by data communication module；

Step e, Airborne Terminal module receive and execute the 4d track data of control terminal module issue.

Further, in step b, the value of described cluster number m' is 4, and the value of hidden state number n' is 3, when parameter updates Section ζ ' is 30 seconds, and t' is 10, and prediction time domain h' is 300 seconds.

Further, the b8 of step b specifically refers to: the flight path sequence data length by being obtained is dynamic change, In order to real-time tracking airborne vehicle flight path state change it is necessary to initial flight path HMM parameter lambda '=(π, a, B) on the basis of, it is readjusted, more accurately to speculate the position in certain moment following for the airborne vehicle；Every period ζ ', according to The t' observation (o according to up-to-date acquisition₁,o₂,...,o_t') to flight path HMM parameter lambda '=(π, a, b) carry out weight New estimation.

The b10 of step b specifically refers to: every the periodHMM parameter lambda according to up-to-date acquisition '=(π, A, b) and nearest h history observation (o₁,o₂,...,o_h), based on hidden state q of airborne vehicle current time, predicted by setting Time domain h', obtains position prediction value o in future time period h' for the airborne vehicle in moment t

Further, the periodFor 4 seconds.

Further, the specific implementation process of described step d is as follows:

Step d1, to Solving Flight Conflicts process model building: conflict Resolution flight path is considered as continuous three sections of smooth curves, gives Surely free the beginning and end of flight path, according to flight path restrictive condition, set up and comprise acceleration, climb or rate of descent, turning rate Multivariable optimum conflict Resolution model；

Step d2, conflict Resolution variable bound under different flying conditions is modeled: wherein t need to implement conflict Resolution boat The variable bound of pocket k can be described as: a_k(t)≤a_m、ω_k(t)≤ω_m、γ_k(t)≤γ_m, a_m、ω_m、γ_mIt is respectively maximum Acceleration, turning rate and climb or rate of descent；

Step d3, termination reference point locations p setting airborne vehicle collision avoidance planning, collision avoidance planning control time domain θ, track are pre- Survey time domain υ；

Step d4, in each sampling instant, based on the current running status of airborne vehicle and historical position observation sequence, obtain The numerical value of spatial domain wind field variable；

Step d5, be set in given optimizing index function on the premise of, based on cooperative collision avoidance trajectory planning thought, pass through Give different weights to each airborne vehicle and incorporate real-time wind field variable filtering numerical value, obtain the collision avoidance rail of each airborne vehicle Mark and collision avoidance control strategy and each airborne vehicle only implements its first Optimal Control Strategy in Rolling Planning is spaced；

Step d6, in next sampling instant, repeat step d4 to d5 is until each airborne vehicle all reaches it and frees terminal.

Further, in step d3: terminate the next way point that reference point locations p are airborne vehicle, collision avoidance is planned Time domain θ is controlled to be 300 seconds, trajectory predictions time domain υ is 300 seconds；

The detailed process of step d4 is as follows:

D4.1 the stop position) setting airborne vehicle is as track reference coordinate initial point；

D4.2) when airborne vehicle is in straight running condition and at the uniform velocity turning running status, build spatial domain wind field and linearly filter Wave pattern x (t+ △ t)=f (t) x (t)+w (t) and z (t)=h (t) x (t)+v (t) obtains wind field variable value, wherein △ t table Show the sampling interval, x (t) represents the state vector of t, z (t) represents that the observation vector of t, f (t) and h (t) represent respectively State-transition matrix and output calculation matrix, w (t) and v (t) represents system noise vector sum measurement noise vector respectively；In boat When pocket is in speed change turning running status, build spatial domain wind field nonlinear filtering wave pattern

X (t+ △ t)=ψ (t, x (t), u (t))+w (t), z (t)=ω (t, x (t))+v (t) and u (t)=[ω_a(t), γ_a(t)]^t, wherein ψ () and ω () represent respectively state-transition matrix and output calculation matrix, ω_a(t) and γ_a(t) point Biao Shi not turning rate and rate of acceleration；

D4.3 the numerical value of wind field variable) is obtained according to constructed Filtering Model；

The detailed process of step d5 is as follows: order

$d_{it}^2=\left|\right|p_i\left(t\right)-p_i^f|{|}_2^2={(x_{it}-x_i^f)}^2+{(y_{it}-y_i^f)}^2,$

WhereinRepresent t airborne vehicle i present position p_i(t) and next way point p_i ^fBetween distance square, p_i(t)=(x_it,y_it),The priority index of so t airborne vehicle i may be set to:

$l_{it}=100\frac{d_{it}^{-2}}{\underset{i=1}{\overset{n_t}{\σ}}{d}_{it}^{-2}},$

Wherein n_tRepresent the airborne vehicle number that there is conflict in t spatial domain, from the implication of priority index, aviation Device is nearer apart from its terminal, and its priority is higher；

Set optimizing index

$\begin{array}{c}{j}^{*}(u_1^{\left(t\right)},u_1^{(t+\mathrm{\δ}t)},\mathrm{...},u_1^{(t+p\mathrm{\δ}t)},\mathrm{...},u_{n_t}^{\left(t\right)},u_{n_t}^{(t+\mathrm{\δ}t)},\mathrm{...},u_{n_t}^{(t+p\mathrm{\δ}t)})=\underset{s=1}{\overset{p}{\σ}}\underset{i=1}{\overset{n_t}{\σ}}{l}_{it}\left|\right|p_i(t+s\mathrm{\δ}t)-p_i^f|{|}_2^2\\ =\underset{s=1}{\overset{p}{\σ}}\underset{i=1}{\overset{n_t}{\σ}}{\left(p_i\right(t+s\mathrm{\δ}t)-p_i^f)}^t{q}_{it}\left(p_i\right(t+s\mathrm{\δ}t)-p_i^f)\end{array}$

, wherein i ∈ i (t) represents airborne vehicle code and i (t)={ 1,2 ..., nt }, p_i(t+s △ t) represents that airborne vehicle exists The position vector in moment (t+s △ t), p_i ^fRepresent next way point of airborne vehicle i, u_iRepresent the optimum of airborne vehicle i to be optimized Control sequence, q_itFor positive definite diagonal matrix, its diagonal element is priority index l in t for the airborne vehicle i_it, and

Further, the airborne vehicle Lothrus apterus 4d flight path of described step a generates in accordance with the following methods:

Step a1, carry out aircraft states transfer modeling, according to the flying height section of airborne vehicle in flight plan, set up (g, g, pre, post m) shift mould for the airborne vehicle stage to the petri pessimistic concurrency control that single airborne vehicle shifts in different legs: e= Type, wherein g represent flight leg, and g represents the transfer point of flight status parameter in vertical section, pre and post represents boat respectively Section and way point before and after to annexation,Represent the mission phase residing for airborne vehicle；

Step a2, to set up airborne vehicle full flight profile, mission profile hybrid model as follows,

v_h=κ (v_cas,mach,h_p,t_loc),

v_gs=μ (v_cas,mach,h_p,t_loc,v_ws, α),

Wherein v_casFor calibrated airspeed, mach is Mach number, h_pFor pressure altitude, α is the angle of wind direction forecast and air route, v_wsFor wind speed forecasting value, t_locFor temperature forecast value, v_hFor altitude rate, v_gsFor ground velocity；

Step a3, by the way of hybrid system emulation, speculate solution flight path: using by the method for time subdivision, using shape State continually varying characteristic Recursive Solution any time airborne vehicle voyage away from reference point in a certain mission phaseAnd heightWherein j₀For initial time airborne vehicle away from reference point Voyage, △ τ is the numerical value of time window, and j (τ) is the voyage away from reference point for the τ moment airborne vehicle, h₀For initial time airborne vehicle away from ginseng The height of examination point, h (τ) is the height away from reference point for the τ moment airborne vehicle, thereby it is assumed that the 4d flight path obtaining single airborne vehicle；

Step a4, to many airborne vehicles coupling model implement Lothrus apterus allotment: reach the time in crosspoint in advance according to two airborne vehicles, According to air traffic control principle, quadratic programming is carried out to the airborne vehicle 4d flight path being unsatisfactory for space requirement near crosspoint, obtains To Lothrus apterus 4d flight path.

Further, monitor in described step b that air traffic control radar is monitored data and automatic dependent surveillance by data fusion module Data is merged, and generates airborne vehicle real-time flight path information, specifically in accordance with the following methods:

Step b1, by coordinate unit and time unification；

Step b2, using closest data association algorithm, the point belonging to same target is associated, extracts targetpath； Step b3, the track data extracting from automatic dependent surveillance system and air traffic control radar respectively is joined from different space-time

Examine coordinate system conversion, be registered to the unified space-time reference coordinate system of control terminal；

Step b4, the coefficient correlation of two flight paths of calculating, if coefficient correlation is less than a certain predetermined threshold value then it is assumed that two are navigated Mark is uncorrelated；Otherwise this two flight path correlations, can be merged；

Step b5, related flight path is merged.

Further, in described step b5, related flight path is merged, put down using the weighting based on the sampling period All algorithms, its weight coefficient determined according to sampling period and precision of information, recycle Weighted Average Algorithm by associated from Dynamic dependent surveillance flight path and air traffic control radar Track Fusion are system flight path.

Further, the specific implementation process of described step c is as follows:

Step c1, the conflict hypersurface collection of functions based on regulation rule for the construction: set up hypersurface collection of functions in order to reflect system The contention situation of system, wherein, the continuous function related to single airborne vehicle in conflict hypersurfaceSuper bent for the i-th type Face, the continuous function related to two frame airborne vehiclesFor the i-th i type hypersurface；

Step c2, set up by airborne vehicle continuous state to discrete conflict situation observer: need built according to control specification Vertical observer, the collision event that observation system system is passed through hypersurface and produced, so that corresponding control decision made by controller Instruction；Observer ξ is used for the consecutive variations of aircraft position in observation system and produces collision event, claimsFor i-th Type observer,For the i-th i type observer；

, from the discrete watch-dog to conflict Resolution means that conflicts, this discrete watch-dog can be described as function for step c3, designWherein s is the space that observer observation vector transforms into, and d is the space that all decision vector d transform into；Work as observer Discrete observation vector when showing that a certain unexpected state occurs, send corresponding alarm at once.

The present invention has positive effect: the pipe of the air traffic control system run based on 4d flight path of (1) present invention Method processed, during the supposition of airborne vehicle real-time track, has incorporated the impact of enchancement factor, the rolling track supposition side being adopted Case can extract the changing condition of extraneous enchancement factor in time, improves the accuracy of airborne vehicle track supposition.

(2) method of control of the air traffic control system run based on 4d flight path of the present invention is in airborne vehicle conflict Resolution During, incorporate the impact of high-altitude wind field, trajectory planning scheme is freed in the rolling being adopted can be according to wind field in high-altitude Track is freed in adjustment in time for change, improves the robustness of airborne vehicle conflict Resolution.

(3) method of control of the air traffic control system run based on 4d flight path of the present invention is accurate for airborne vehicle configuration Blank pipe interval, strict control the time window by way point for the airborne vehicle, reduce traffic flow randomness, improve spatial domain safety Property.

(4) reckoning to flight profile, mission profile for the method for control of the air traffic control system run based on 4d flight path of the present invention With Trajectory Prediction high precision, and then conflict dissolution ability and automatization level are improved, reduce the live load of controller.

(5) method of control of the air traffic control system run based on 4d flight path of the present invention is no longer limited to keep single Personal distance between individual airborne vehicle, but from macroscopically the traffic flow in spatial domain is implemented with effective control, control work is permissible More transfer to departure time of aircraft, sequence of marching into the arena, bad weather change the aspects such as boat.

(6) method of control of the air traffic control system run based on 4d flight path of the present invention is based on different performance index Airborne vehicle optimum free the economy that flight path can significantly increase airborne vehicle operation, and the utilization rate in spatial domain.

Brief description

Fig. 1 is the composition schematic diagram of the air traffic control system of the present invention；

Fig. 2 is Airborne Terminal module composition schematic diagram；

Fig. 3 is data communication module composition schematic diagram；

Fig. 4 is to monitor data fusion module composition schematic diagram；

Fig. 5 is Lothrus apterus 4d flight path generation method schematic flow sheet before flight；

Fig. 6 is flight middle or short term 4d flying track conjecture method flow schematic diagram；

Fig. 7 is airborne vehicle flight path conflict monitoring and alarm method schematic flow sheet；

Fig. 8 frees 4d route optimization method schematic flow sheet for airborne vehicle.

Specific embodiment

(embodiment 1)

The air traffic control system run based on 4d flight path of the present embodiment, as shown in figure 1, include Airborne Terminal module 101st, data communication module 102, supervision data fusion module 103 and control terminal module 104.Concrete to each several part below Embodiment is described in detail respectively.

1. Airborne Terminal module

Airborne Terminal module 101 is that pilot obtains ground control order, reference 4d flight path, and input flight intent Interface, still gathers the interface of current aerospace device position data simultaneously.

As shown in Fig. 2 its specific embodiments is as follows:

Airborne Terminal module 101 receives following information input: (1) ads-b information acquisition unit 201 passes through airborne gps The aircraft position vector of collection, velocity vector, and the catchword of this airborne vehicle, pass through information and data transfer to machine after coding Carry data communication module 102；(2) airborne vehicle driver needs the flight intent inconsistent with ground control order, by people Machine inputting interface, and the ground controller of agreement can in the form of identifying by information and data transfer to airborne data communication Module 102.In addition Airborne Terminal module 101 realizes following information output: (1) passes through terminal display, receives and shows The air traffic control instruction that pilot can identify；(2) receive and show the front Lothrus apterus 4d boat generating of ground line terminal flight Mark, and the optimum of calculating frees 4d flight path after ground line end-probing is to conflict.

2. data communication module

Data communication module 102 can achieve vacant lot bidirectional data communication, realizes airborne real time position data and flight intent The downlink transfer of data cell 202 and ground control command unit 203, and the uplink with reference to 4d flight path unit 204.

As shown in figure 3, its specific embodiments is as follows:

Downlink data communication: Airborne Terminal 101 passes through airborne secondary radar answering machine by aircraft identification mark and 4d position Confidence ceases, and other additional datas, and the such as information transfer such as flight intent, flying speed, meteorology is to ground secondary radar (ssr), after secondary radar reception, data message is parsed, and be transferred to central data process assembly 301 and decode, by referring to Track data interface is made to be transferred to control terminal 104；Upstream data communication: ground control terminal 104 is passed through to instruct track data Interface, after central data process assembly 301 coding, the inquisitor just ground control order of ground secondary radar or with reference to 4d Flight path information transmission is simultaneously shown in Airborne Terminal 101.

3. monitor data fusion module

Monitor that data fusion module 103 realizes the fusion of air traffic control radar supervision and automatic dependent surveillance ads-b data, for pipe Flight middle or short term 4d flight path in terminal module 104 processed is generated submodule and real-time flight conflict monitoring and is provided with alarm submodule Flight path information in real time.

As shown in figure 4, its specific embodiments is as follows:

(1) in pretreatment stage by coordinate unit and time unification it is assumed that respectively from ads-b and air traffic control radar extract Data is the coordinate (as longitude, latitude, height above sea level) of series of discrete point, each point correspondence acquisition time；(2) using closest The point belonging to same target is associated by data association algorithm, extracts targetpath；(3) will be respectively from ads-b and blank pipe thunder The track data reaching middle extraction converts, is registered to the unified space-time of control terminal with reference to seat from different space-time reference coordinate system Mark system；(4) calculate two flight paths coefficient correlation, if coefficient correlation be less than a certain predetermined threshold value then it is assumed that two flight paths not Correlation, otherwise this two flight path correlations, can be merged；(5) related flight path is merged.Due to ads-b and blank pipe The precision of radar is different with the sampling period, the system using Weighted Average Algorithm based on the sampling period, its weight coefficient according to Sampling period and precision of information determine, recycle Weighted Average Algorithm by associated ads-b flight path and air traffic control radar flight path It is fused to system flight path.

4. control terminal module

Control terminal module 104 includes flying, and front Lothrus apterus 4d flight path generates, flight middle or short term 4d flight path generates, flies in real time Row conflict monitoring and alarm, this four submodules of Solving Flight Conflicts 4d track optimization.

(1) before flying, Lothrus apterus 4d flight path generates

The flight plan being obtained according to Flight Data Processing System (fdp) and world area forecast system (wafs) are issued Wind, the grib lattice point forecast data of temperature, set up the hybrid model of stratification to Air Traffic System, by system in peace The evolution of total state, the time locus of description state evolution, generate airborne vehicle flight path.

As shown in figure 5, its specific implementation process is as follows:

First, carry out aircraft states transfer modeling.Airborne vehicle shows as moving between leg along the process of track flight State handoff procedure, according to the flying height section of airborne vehicle in flight plan, sets up what single airborne vehicle shifted in different legs (g, g, pre, post are m) airborne vehicle stage metastasis model, wherein g represents flight leg, and g represents vertical to petri pessimistic concurrency control: e= Flight status parameter in straight section (include air speed, highly, configuration) transfer point, pre and post represent leg and air route respectively To annexation before and after point,Represent the mission phase residing for airborne vehicle.

Secondly, set up airborne vehicle full flight profile, mission profile hybrid model.Flight in single leg for the airborne vehicle is considered as even Continuous process, according to particle energy model, airborne vehicle dynamics under the different operation phase is with meteorological condition for the derivation airborne vehicle Equation, v_h=κ (v_cas,mach,h_p,t_loc), v_gs=μ (v_cas,mach,h_p,t_loc,v_ws, α), wherein v_casFor calibrated airspeed, Mach is Mach number, h_pFor pressure altitude, α is the angle of wind direction forecast and air route, v_wsFor wind speed forecasting value, t_locPre- for temperature Report value, v_hFor altitude rate, v_gsFor ground velocity.

Then, speculate solution flight path by the way of hybrid system emulation.Using by the method for time subdivision, utilization state Continually varying characteristic Recursive Solution any time airborne vehicle voyage away from reference point in a certain mission phaseAnd heightWherein j₀For initial time airborne vehicle away from reference point Voyage, △ τ is the numerical value of time window, and j (τ) is the voyage away from reference point for the τ moment airborne vehicle, h₀For initial time airborne vehicle away from ginseng The height of examination point, h (τ) is the height away from reference point for the τ moment airborne vehicle, thereby it is assumed that the 4d flight path obtaining single airborne vehicle.

Finally, many airborne vehicles coupling model is implemented with Lothrus apterus allotment.Reach the time in crosspoint according to two airborne vehicles in advance, press According to air traffic control principle, quadratic programming is carried out to the airborne vehicle 4d flight path being unsatisfactory for space requirement near crosspoint, obtains Lothrus apterus 4d flight path.

(2) flight middle or short term 4d flight path generates

The real-time track data of airborne vehicle is obtained after implementing to merge according to control radar and automatic dependent surveillance system ads-b, Using HMM thus it is speculated that airborne vehicle 4d track in following 5 minutes windows.

As shown in fig. 6, its specific implementation process is as follows:

First, airborne vehicle track data is pre-processed, according to acquired airborne vehicle original discrete two-dimensional position sequence x= [x₁,x₂,...,x_n] and y=[y₁,y₂,...,y_n], it is carried out process using first-order difference method obtain new airborne vehicle from Scattered position sequence △ x=[△ x₁,△x₂,...,△x_n-1] and △ y=[△ y₁,△y₂,...,△y_n-1], wherein △ x_b=x_b+1- x_b,△y_b=y_b+1-y_b(b=1,2 ..., n-1).

Secondly, airborne vehicle track data is clustered.To new airborne vehicle discrete two-dimensional position sequence △ x and △ y after processing, By setting cluster number m', respectively it is clustered using k-means clustering algorithm.

Then, using HMM, parameter training is carried out to the airborne vehicle track data after cluster.By locating Airborne vehicle running orbit data △ x and △ y after reason is considered as the aobvious observation of hidden Markov models, by setting hidden status number Mesh n' and parameter update period ζ ', rolled according to t' nearest position detection value and using b-w algorithm and obtain up-to-date hidden Ma Er Section's husband's model parameter λ ': the flight path sequence data length by being obtained is dynamic change, for real-time tracking airborne vehicle boat The state change of mark it is necessary to initial flight path HMM parameter lambda '=(π, a, b) on the basis of it is adjusted again Whole, more accurately to speculate the position in certain moment following for the airborne vehicle.Every period ζ ', according to t' observation of up-to-date acquisition Value (o₁,o₂,...,o_t') to flight path HMM parameter lambda '=(π, a, b) reevaluated.

Again and, according to HMM parameter, obtained corresponding to current time observation using viterbi algorithm Hidden state q.

Finally, every the periodHMM parameter lambda according to up-to-date acquisition '=(π, a, b) and nearest h go through History observation (o₁,o₂,...,o_h), based on hidden state q of airborne vehicle current time, by setting prediction time domain h', in moment t Obtain position prediction value o in future time period h' for the airborne vehicle.

The value of described cluster number m' is 4, and the value of hidden state number n' is 3, and it is 30 seconds that parameter updates period ζ ', and t' is 10, prediction time domain h' is 300 seconds, the periodFor 4 seconds.

(3) real-time flight conflict monitoring and alarm

When system is possible to the state occurring violating safe condition collection, condition monitoring is implemented by controller, to aviation Effective measure of control implemented by device, it is to avoid the generation of flight collision.

As shown in fig. 7, its specific implementation process is as follows:

First, the conflict hypersurface collection of functions based on regulation rule for the construction.The violation of air traffic control constraint can It is considered as controlled device (the multi rack airborne vehicle of the control zone flight) event that composition system is passed through hypersurface and produced, set up super bent Surface function collection is in order to reflect the contention situation of system.Wherein, related to single airborne vehicle continuous function in conflict hypersurfaceFor the i-th type hypersurface, and by the continuous function related to two frame airborne vehiclesSurpass for the i-th i type Curved surface.

Then, set up by the observer of airborne vehicle continuous state to discrete conflict situation.Need to be set up according to control specification Observer, the collision event that observation system system is passed through hypersurface and produced, so that controller is made corresponding control decision and is referred to Order.Observer ξ is used for the consecutive variations of aircraft position in observation system and produces collision event, claimsFor the i-th type Observer,For the i-th i type observer.

Finally, design is from the discrete watch-dog of conflict to conflict Resolution means.When the discrete observation vector of observer shows When a certain unexpected state occurs, send corresponding alarm at once.This discrete watch-dog can be described as functionIts Middle s is the space that observer observation vector transforms into, and d is the space that all decision vector d transform into.

(4) Solving Flight Conflicts 4d track optimization

Under conditions of ensureing to make system meet control specification, by selecting different object functions of freeing, using Excellent control theory method is so that the control input that controller is given can reach optimum.

As shown in figure 8, its specific implementation process is as follows:

Step d1, to Solving Flight Conflicts process model building: conflict Resolution flight path is considered as continuous three sections of smooth curves, gives Surely free the beginning and end of flight path, according to flight path restrictive condition, set up and comprise acceleration a_i(t), climb or rate of descent γ_i (t), turning rate ω_iThe multivariable optimum conflict Resolution model of (t).

Step d2, conflict Resolution variable bound under different flying conditions is modeled: wherein t need to implement conflict Resolution boat The variable bound of pocket k can be described as: a_k(t)≤a_m、ω_k(t)≤ω_m、γ_k(t)≤γ_m, a_m、ω_m、γ_mIt is respectively maximum Acceleration, turning rate and climb or rate of descent.

Step d3, termination reference point locations p setting airborne vehicle collision avoidance planning, collision avoidance planning control time domain θ, track are pre- Survey time domain υ.Terminate the next way point that reference point locations p are airborne vehicle, collision avoidance planning control time domain θ is 300 seconds, rail Mark prediction time domain υ is 300 seconds.

Step d4, in each sampling instant t, based on the current running status of airborne vehicle and historical position observation sequence, obtain Take the numerical value of spatial domain wind field variable, its detailed process is as follows:

D4.1 the stop position) setting airborne vehicle is as track reference coordinate initial point；

D4.2) when airborne vehicle is in straight running condition and at the uniform velocity turning running status, build spatial domain wind field and linearly filter Wave pattern x (t+ △ t)=f (t) x (t)+w (t) and z (t)=h (t) x (t)+v (t) obtains wind field variable value, wherein △ t table Show the sampling interval, x (t) represents the state vector of t, z (t) represents that the observation vector of t, f (t) and h (t) represent respectively State-transition matrix and output calculation matrix, w (t) and v (t) represents system noise vector sum measurement noise vector respectively；In boat When pocket is in speed change turning running status, build spatial domain wind field nonlinear filtering wave pattern

X (t+ △ t)=ψ (t, x (t), u (t))+w (t), z (t)=ω (t, x (t))+v (t) and u (t)=[ω_a(t), γ_a(t)]^t,

Wherein ψ () and ω () represents state-transition matrix and output calculation matrix, ω respectively_a(t) and γ_a(t) point Biao Shi not turning rate and rate of acceleration；

D4.3 the numerical value of wind field variable) is obtained according to constructed Filtering Model.

Step d5, be set in given optimizing index function on the premise of, based on cooperative collision avoidance trajectory planning thought, pass through Give different weights to each airborne vehicle and incorporate real-time wind field variable filtering numerical value, obtain the collision avoidance rail of each airborne vehicle Mark and collision avoidance control strategy and each airborne vehicle only implements its first Optimal Control Strategy, detailed process in Rolling Planning is spaced As follows: order

WhereinRepresent t airborne vehicle i present position p_i(t) and next way point p_i ^fBetween distance square, p_i(t)=(x_it,y_it),The priority index of so t airborne vehicle i may be set to:

$l_{it}=100\frac{d_{it}^{-2}}{\underset{i=1}{\overset{n_t}{\σ}}{d}_{it}^{-2}},$

Wherein n_tRepresent the airborne vehicle number that there is conflict in t spatial domain, from the implication of priority index, aviation Device is nearer apart from its next way point, and its priority is higher.

Set optimizing index

$\begin{array}{c}{j}^{*}(u_1^{\left(t\right)},u_1^{(t+\mathrm{\δ}t)},\mathrm{...},u_1^{(t+p\mathrm{\δ}t)},\mathrm{...},u_{n_t}^{\left(t\right)},u_{n_t}^{(t+\mathrm{\δ}t)},\mathrm{...},u_{n_t}^{(t+p\mathrm{\δ}t)})=\underset{s=1}{\overset{p}{\σ}}\underset{i=1}{\overset{n_t}{\σ}}{l}_{it}\left|\right|p_i(t+s\mathrm{\δ}t)-p_i^f|{|}_2^2\\ =\underset{s=1}{\overset{p}{\σ}}\underset{i=1}{\overset{n_t}{\σ}}{\left(p_i\right(t+s\mathrm{\δ}t)-p_i^f)}^t{q}_{it}\left(p_i\right(t+s\mathrm{\δ}t)-p_i^f)\end{array}$

, wherein i ∈ i (t) represents airborne vehicle code and i (t)={ 1,2 ..., n_t, p_i(t+s △ t) represents that airborne vehicle exists The position vector in moment (t+s △ t), p_i ^fRepresent next way point of airborne vehicle i, u_iRepresent the optimum of airborne vehicle i to be optimized Control sequence, q_itFor positive definite diagonal matrix, its diagonal element is priority index l in t for the airborne vehicle i_it, and

Step d6, in next sampling instant, repeat step d4 to d5 is until each airborne vehicle all reaches it and frees terminal.

Airborne Terminal module receives and executes the 4d track data of control terminal module issue.

Obviously, above-described embodiment is only intended to clearly illustrate example of the present invention, and is not to the present invention The restriction of embodiment.For those of ordinary skill in the field, can also be made it on the basis of the above description The change of its multi-form or variation.There is no need to be exhaustive to all of embodiment.And these belong to this Obvious change that bright spirit is extended out or change among still in protection scope of the present invention.

Claims (2)

1. a kind of method of control of the air traffic control system run based on 4d flight path, described air traffic control system includes Airborne Terminal module, data communication module, supervision data fusion module and control terminal module；Monitor that data fusion module is used In the fusion realizing air traffic control radar supervision data and automatic dependent surveillance data, provide real-time flight path letter for control terminal module Breath；It is characterized in that:

Described control terminal module includes following submodule:

Lothrus apterus 4d flight path generation module before flight, according to the forecast data of flight plan and world area forecast system, sets up Airborne vehicle kinetic model, then sets up flight path conflict pre- allotment theoretical model according to flight collision Coupling point, generates airborne vehicle Lothrus apterus 4d flight path；

Flight middle or short term 4d flight path generation module, according to the real-time flight path information monitoring that data fusion module provides, using hidden horse Er Kefu model is thus it is speculated that airborne vehicle 4d track in following certain time window；

Real-time flight conflict monitoring and alarm module, for set up from airborne vehicle continuously dynamically to the observation of discrete conflict logic Device, the conflict situation that the continuous dynamic mapping of Air Traffic System is expressed for discrete observation value；When system is possible to violate sky During middle traffic control rule, the Hybrid dynamics behavior implementing monitoring to air traffic hybrid system, provide timely for controller Warning information；

Solving Flight Conflicts 4d track optimization module, ensureing that system meets under aircraft performance and regulation rule constraints, By select different free object function, using Model Predictive Control Theory method, calculate airborne vehicle conflict Resolution 4d flight path； And airborne vehicle conflict Resolution 4d flight path is sent to by the execution of Airborne Terminal module by data communication module；

The method of control of the described air traffic control system run based on 4d flight path includes several steps as follows:

Before step a, flight, Lothrus apterus 4d flight path generation module is according to the forecast data of flight plan and world area forecast system, Set up airborne vehicle kinetic model, and set up flight path conflict pre- allotment theoretical model according to flight collision Coupling point, generate aviation Device Lothrus apterus 4d flight path；

Air traffic control radar is monitored that data and automatic dependent surveillance data are merged by step b, supervision data fusion module, generates boat Pocket real-time flight path information is simultaneously supplied to control terminal module；Flight middle or short term 4d flight path generation module in control terminal module Speculate the airborne vehicle 4d track in following certain time window according to airborne vehicle real-time flight path information and history flight path information；Described according to Speculate the concrete reality of the airborne vehicle 4d track in following certain time window according to airborne vehicle real-time flight path information and history flight path information Apply process as follows:

Step b6, to airborne vehicle track data pre-process, according to acquired airborne vehicle original discrete two-dimensional position sequence x= [x₁,x₂,...,x_n] and y=[y₁,y₂,...,y_n], it is carried out process using first-order difference method obtain new airborne vehicle from Scattered position sequence △ x=[△ x₁,△x₂,...,△x_n-1] and △ y=[△ y₁,△y₂,...,△y_n-1], wherein △ x_b=x_b+1- x_b,△y_b=y_b+1-y_b, b=1,2 ..., n-1；

Step b7, airborne vehicle track data is clustered, to new airborne vehicle discrete two-dimensional position sequence △ x and △ y after processing, lead to Cross setting cluster number m', respectively it is clustered using k-means clustering algorithm；

Step b8, to cluster after airborne vehicle track data carry out parameter training using HMM, by processing Airborne vehicle running orbit data △ x and △ y afterwards is considered as the aobvious observation of hidden Markov models, by setting hidden state number N' and parameter update period ζ ', rolled according to t' nearest position detection value and using b-w algorithm and obtain up-to-date hidden Ma Erke Husband's model parameter λ '；

Step b9, foundation HMM parameter, are obtained hidden corresponding to current time observation using viterbi algorithm State q；

Step b10, by setting prediction time domain h', based on hidden state q of airborne vehicle current time, obtain future time period airborne vehicle Position prediction value o；

Step c, real-time flight conflict monitoring and alarm module set up from airborne vehicle continuously dynamically to the sight of discrete conflict logic Survey device, the conflict situation that the continuous dynamic mapping of Air Traffic System is expressed for discrete observation value；When system is possible to violate During air traffic control rules, Hybrid dynamics behavior implementing monitoring to air traffic hybrid system, provide in time for controller Warning information；

Step d, Solving Flight Conflicts 4d track optimization module are ensureing that system meets aircraft performance and regulation rule constrains bar Under part, by select different free object function, using Model Predictive Control Theory method, calculate airborne vehicle conflict Resolution 4d Flight path；And airborne vehicle conflict Resolution 4d flight path is sent to by the execution of Airborne Terminal module by data communication module；

Step e, Airborne Terminal module receive and execute the 4d track data of control terminal module issue；

In described step b, the value of described cluster number m' is 4, and the value of hidden state number n' is 3, and it is 30 that parameter updates period ζ ' Second, t' is 10, and prediction time domain h' is 300 seconds.

2. the method for control of the air traffic control system run based on 4d flight path according to claim 1, its feature exists In: the b8 of step b specifically refers to: the flight path sequence data length by being obtained is dynamic change, for real-time tracking boat The state change of pocket flight path it is necessary to initial flight path HMM parameter lambda '=(π, a, b) on the basis of to it Readjust, more accurately to speculate the position in certain moment following for the airborne vehicle；Every period ζ ', according to the t' of up-to-date acquisition Individual observation (o₁,o₂,...,o_t') to flight path HMM parameter lambda '=(π, a, b) reevaluated；

The b10 of step b specifically refers to: every the periodHMM parameter lambda according to up-to-date acquisition '=(π, a, b) With nearest h history observation (o₁,o₂,...,o_h), based on hidden state q of airborne vehicle current time, by setting prediction time domain H', obtains position prediction value o in future time period h' for the airborne vehicle in moment t.