CN108073185A - Multiple no-manned plane reaches cooperative control method simultaneously - Google Patents
Multiple no-manned plane reaches cooperative control method simultaneously Download PDFInfo
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/10—Simultaneous control of position or course in three dimensions
- G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft
- G05D1/104—Simultaneous control of position or course in three dimensions specially adapted for aircraft involving a plurality of aircrafts, e.g. formation flying
Abstract
The present invention relates to a kind of multiple no-manned planes to reach cooperative control method simultaneously, belongs to flight test, unmanned plane and Collaborative Control technical field.Its content includes the decentralized control structure for only relying on local message interaction and the decentralized control strategy based on consistency algorithm.The two is combined according to the different characteristics of path planning and speed control, the influence of the unfavorable factors such as tracking error and pop-up threats is tackled using their complementary advantage.The present invention realizes that multiple no-manned plane reaches simultaneously, improves flexibility, robustness and the reliability of control strategy.Improve whole multiple no-manned plane cooperation efficiency so that the big system of entire multiple no-manned plane operation plays the effect of bigger in war.
Description
Technical field
The present invention relates to a kind of multiple no-manned planes to reach cooperative control method simultaneously, belongs to flight test, unmanned plane and association
Same control technology field.
Background technology
Two main features of current UCAV cooperative mission controls research have:
(1) distributed AC servo system becomes important a developing direction and research hotspot
The control mode of more UCAV systems is broadly divided into centerized fusion and distributed AC servo system.Centerized fusion is by long-term
Research and fully development, achieve substantial amounts of achievement in research, but fast development recently as distributed control technology and its
Under the advantage solved dynamically, on uncertain problem and the trend being continuously improved in UCAV capacity of will, distributed AC servo system
It is increasingly becoming the important development direction and research hotspot of more UCAV cooperative mission controls.
(2) it is effective means using hierarchical structure successively solve
Due to the high complexity of more UCAV cooperative mission controls problems, either centerized fusion or distributed control
System, be required for establishing corresponding hierarchical control system, by PROBLEM DECOMPOSITION be different levels subproblem, it is at all levels successively
It solves to reduce problem difficulty.
Multiple no-manned plane Collaborative Control can be decomposed into collaboration Target Assignment, collaboration path planning, feasible trajectory and generate, be asymptotic
Multiple subproblems such as track following.Multiple no-manned plane cooperate with path planning, refer to consider collaboration constraint in the case of for multi rack without
Man-machine path planning enables them to complete task with minimum cost cooperation.It is identical with unmanned plane unit path planning, mostly nobody
Machine collaboration path planning also will be that each frame unmanned plane cooks up the feasible flight path for meeting constraints, and difference is then
The path of mainly each frame unmanned plane is not necessarily optimal for its own.
Before task starts, first can according to certain operational principle by combat duty can be decomposed into it is a series of can be by
The task that UCAV is performed.Being obtained after Task-decomposing for task includes different types, such as goal verification, attacks, injures assessment,
Even same type of task is also distributed about different geographical locations and with different time requirement.Participate in the UCAV to fight
Often there is different purposes, the UCAV of same purposes may also include different models.More UCAV tasks distribution are exactly to study
How correct task distributed into correct UCAV in the correct time, make the whole battle effect after UCAV collaboration completion tasks
It can be maximum.The research in this field includes two aspect contents:First, Task Allocation Problem models, second is that Task Allocation Problem solves.
(1) more UCAV Task Allocation Problems modelings
For the complex problem solving under specific background, effective resolving ideas be by problem convert or resolve into one or
Several basic problems are recycled and solved with the relevant ripe theoretical and method of basic problem.To more UCAV Task Allocation Problems
Research in, a variety of Task Assignment Models based on classical problem have been proposed in researcher, are mainly asked including more travelling salesmans
Inscribe model, Vehicle Routing Problem model, mixed integer linear programming model, dynamic network flow Optimized model, multiprocessor resource
Distribution model etc..After the problem of research is abstracted as the theoretical model of classical problem, basic model is provided for problem solving
Frame, but the distribution of UCAV tasks has higher complexity, has more criterions to need to consider, these particular/special requirements can be with
The form of constraints is added in model, such as time windows constraints, priority restrictions, UCAV capacity consistencies etc..Research is asked
After topic is converted to classical problem, it is possibly realized but also using business software solve, it can be by UCAV Task Allocation Problems
After being modeled as MILP problems, directly solved using business software CPLEX.
(2) more UCAV Task Allocation Problems solve
After the mathematical model for establishing more UCAV Task Allocation Problems, various optimization methods can be used to carry out problem
It solves.Due to importance of more UCAV Task Allocation Problems in theory and practice, people study and explore a variety of Optimization Solutions
Algorithm, development experience is from simple to complicated, process from single to polynary.
1. the method for solving in centerized fusion
For centerized fusion, common method for solving can be divided into optimal method.Optimal method is often by task
Assignment problem is reduced to mathematical programming model, is then determined using the method for exhaustion (breadth-first or depth optimization), Dynamic Programming branch
The methods of boundary, is solved.Under conditions of problem has solution, the hypothesis based on some simplification can ensure to provide optimal method
The optimal solution of problem, but with the increase of problem scale, solve difficulty and also sharply increase, time consumption is exponentially increased.Such
Another deficiency of method is limited to the descriptive power of challenge, and the model of foundation cannot fully reflect answering for battlefield surroundings
Polygamy, it is also extremely difficult to the expression of battlefield surroundings randomness and dynamic.
Heuristic is directed to the NP characteristics of more UCAV Task Allocation Problems, is not intended to acquire in polynomial time and ask
The optimal solution of topic, but traded off calculating between time and distribution effects, it is near excellent to obtain with acceptable calculation amount
Or satisfactory solution.Heuristic can be divided into traditional heuritic approach and modern intelligence optimization algorithm again, and the latter is because with easy
In realize, computation complexity is low, superior performance the features such as, be widely used in recent years.
2. the method for solving in distributed AC servo system
For the distributed AC servo system of more UCAV, various methods are thought with the market auction mechanism based on contract net is more afterwards
A kind of effective method.The basic thought of contract net is to regard task distribution as a process of exchange, passes through " bid-throwing
This market auction mechanism of mark-acceptance of the bid " realizes appointing and migrating for task.When UCAV has found oneself do not have in Campaign Process
When enough some tasks of ability processing or the cost of execution task are excessive, just these tasks are externally auctioned, by other
UCAV submits a tender according to its ability and state, then presides over the UCAV of auction by task immigration to can be performed with more low-cost
The UCAV of task.Method for allocating tasks principle based on contract net is simple, be easily achieved and efficient effect is good, it has also become
The method being most widely used in more UCAV distributed task schedulings distribution.
In distributed control system, it is optimal to pursue more UCAV overall combat effectiveness, it will usually with UCAV individual interests
It clashes, therefore there has been proposed more UCAV method for allocating tasks based on multiple agent satisficing decision theory.This method foundation
The measures of effectiveness of UCAV individual execution tasks determines individual acceptable set of tasks, then leads on the basis of individual is satisfied
It crosses joint satisfaction and task distribution is carried out to more UCAV, realize the individual compromises between more UCAV overall efficiencies of UCAV.Research
The inspiration that personnel are communicated and cooperated using pheromones between by insect, it is proposed that the unmanned plane based on artificial information's element is coordinated
Technology emerges in large numbers whole self-organizing behavior, but this method by the local sensing and reaction equation reciprocation of a large amount of simple individuals
It is more suitable for unmanned plane that is simple, small-sized, cheap and can consuming.
The content of the invention
It is an object of the invention to provide a kind of multiple no-manned planes to reach cooperative control method simultaneously;
1) probability for redistributing increase Mission Success of task is passed through.More UCAV are performed in unison with task, as a frame UCAV
When being smashed or broken down, the task of the UCAV can be dynamically assigned to other UCAV, and other UCAV still can be completed
Assigned tasks.
2) quality completed by the mutual cooperation raising task of member.More UCAV cooperate in Campaign Process, can
It completes quality to improve task and reduces risk, the radar of different UCAV repeatedly starts shooting in different time, shut down respectively to attacking
Targeted scans can improve target locating effect and reduce the probability found by enemy.
3) the mutually shared ability for extending execution task of resource is passed through.The ability of single rack UCAV is limited, but multi rack is different
The UCAV of ability can possess the ability for completing Complex Combat Mission by cooperation.
4) the parallel time for performing shortening task and completing of task is passed through.Multi rack UCAV is in task implementation procedure by dividing
Work cooperation executing tasks parallelly can shorten the time of task completion, improve the efficiency that task is completed.
Multiple no-manned plane (UAV) at the same arrival be typical Collaborative Control problem, have in formation flight, concerted attack should
With.Using multiple no-manned plane cooperative multiple target attack as application background, the problem that reached simultaneously to multiple no-manned plane is studied.It considers
The characteristics of dynamic of battlefield surroundings and uncertain and unmanned plane itself, proposes that one kind reaches simultaneously suitable for multiple no-manned plane
Decentralized control method, content include only rely on local message interaction decentralized control structure and based on consistency algorithm
Decentralized control strategy.The two is combined according to the different characteristics of path planning and speed control, utilizes the mutual of them
Benefit advantage tackles the influence of the unfavorable factors such as tracking error and pop-up threats.
The present invention to achieve these goals, adopts the following technical scheme that:
A kind of multiple no-manned plane reaches cooperative control method simultaneously, and this method is:
(A), the decentralized coordination control description that more UAV are reached simultaneously;
In task, there is n framves unmanned plane to reach previously given k target location in synchronization, wherein n >=k >=
1;
(B) unmanned plane motion model is established
The movement of unmanned plane is connected in the rigid motion on axis for a directional velocity, and velocity magnitude is in a certain range
It is interior can consecutive variations.In actual path planning and Collaborative Control algorithm, generally change without considering the flying height of unmanned plane,
It is the particle moved in two dimensional surface by unmanned aerial vehicle vision.The simplification motion model of unmanned plane is taken as:
In formula:I ∈ { 1,2 ..., n } are the number of unmanned plane;[xi yi]T∈R2For plan-position coordinate;vi、And ωi
Respectively flying speed, course angle and course angular speed;
UAV course anglesDefinition be, in reference frame XOY, the angle between UCAV directional velocities and OX direction of principal axis;
Depending on the selection of reference frame, if compared with Land Navigation Installation, this angle is exactly the actual course line of UCAV, advance
The physical location of direction and aircraft just refers to fly without reference to object to the angle of line this two rays between guidance station
Angle between the direction of advance and direct north of machine;
The flight control system of unmanned plane has the function of that the speed of automatic pilot is kept and course is kept, and tracks given
Speed command and course angle instruction;The speed of automatic pilot is kept and course is kept with first-order dynamic model approximate description:
In formula:vi cWithThe respectively speed command of automatic pilot and course angle instruction;av,iWithFor with unmanned plane
And its normal number that state of flight is related;System passes through speed command vi cIt is instructed with course angleEach unmanned plane to be controlled to transport
It is dynamic, so as to track the path cooked up, the Collaborative Control effect that reaches by adjusting speed while reach;
Collaborative Control algorithm mainly by providing speed command signal in real time, and each UCAV is by adjusting the speed of itself
Size reaches consistent to ensure to reach this variable of the remaining time of respective target, and so as to ensure to reach simultaneously, formation is beaten simultaneously
The effect hit increases success attack rate and increase damage effectiveness;
The kinematic constraint of unmanned plane is accounted for by the following conditions:0 < vmin,i≤vi≤vmax,i,
The speed v of unmanned planeiIn its allowed band [vmin,i,vmax,i], (0 < vmin,i< vmax,i) in variation, meanwhile, boat
To angleChange rate there is also upper limit value ωmax,i;
Excursion [a of accelerationmin,i,amax,i],(amin,i0 < a of <max,i):amin,i≤v.i=ai≤amax,i。
(C) while algorithm is reached;
In multiple no-manned plane Collaborative Control, before take-off or task start before target area is detected in advance, visit
Bright threatening area carries out task distribution afterwards;Path planning is carried out for the original state of each unmanned plane, and optimizes place
Reason;Its Global Optimization Model can usually be established:
In formula, P is the set of the feasible flight path of all unmanned planes;piFor the flight path of the i-th frame unmanned plane;Each group
(p1,p2,…,pN) form a kind of scheme of path planning;J is a kind of target function of definition;
Total cost is the algebraical sum of each unmanned plane during flying path cost:
Here unmanned plane during flying path cost is defined by path length cost and cost two parts is threatened to form:
Ji=kJlength,i+(1-k)Jthreat,i(0≤k≤1) (5)
In formula, the tactics tendency that the value of coefficient k embodies policymaker is different with the emphasis of task, k=0 expression decision-makings
Person no matter path length only demand it is most safe, k=1 represent policymaker only demand shortest path without considering safety factor;
Global Optimization Model is static models, and offline solve obtains global optimum path;But when unmanned plane, target or prestige
The model also changes correspondingly when the side of body changes, and at this moment must carry out weight-normality online and draw.When battlefield is uncertain and dynamic compared with
When notable, unmanned plane, target or threaten will change dramatically so that weight-normality draw frequency increase, this may make path planning
Real-time be deteriorated;For such case, the rolling optimization model that multiple no-manned plane cooperates with path planning is established:
Rolling optimization method substitutes the solution of global issue with the solution for being scrolled into a series of capable local problems online,
Rolling each step only to local problem's Optimization Solution in current scrolling window, and implement current optimal policy.
Further, in the decentralized coordination control description reached simultaneously in (A), more UAV:
The initial position of unmanned plane is the physical location at its current time, the Arbitrary distribution in space, usual actual conditions
Lower distance objective point has certain scope, it is impossible to too close to target;
Each unmanned plane one and only one definite target location in initial plan, the target position of different unmanned planes
Put may it is identical may also be different, the distribution of target point task generally in advance segregation reasons it is good or according to real-time condition by total
Body commanding agency makes one's choice;
In the case where there is accident generation, according to mission requirements, if the task object of unmanned plane changes online, this is needed
Whole system is wanted to possess the task under emergency case and reassigns ability;
It is another to assume that unmanned plane obtains in advance or in real time and threaten and the relevant information of obstacle, it can independently offline or online planning
Path and the estimate for providing path length in real time, can independently fly along path planning;
Accident carries out online real-time path planning under a situation arises, for the target conditions of reassignment, and can be fast
The information of the acquisition introductory path of speed;
Multiple no-manned plane reaches the goal in research of Collaborative Control technology simultaneously, by coordinating the movement shape between each unmanned plane
State reaches above-mentioned task objective, in addition can be carried out under bursty state according to the variation of job change and unmanned plane state real
When adjust, as far as possible ensure task completion;Task has been carried out verifying before starting to the threat around target, is opened in task
Task distribution and path planning are carried out before beginning to each unmanned plane, unmanned plane must also avoid threat and taboo during task
Fly area;
Since the flight path and speed of unmanned plane are not fixed, by adjusting path length and flying speed
To ensure to reach simultaneously;
When in task process, an emergency occurred, sudden threat, system needs task and reassigns and path weight-normality
The ability drawn, while in the case where not changing Collaborative Control algorithm, ensure to reach the completion of task simultaneously.
Further, path planning specifically uses environment of the Voronoi diagram as unmanned plane path planning in two dimensional surface
Description instrument;
In Voronoi diagram, the barriers such as threat are located in the polygon that multiple summits are surrounded, and unmanned plane is moved along side;
In order to meet cotasking demand, different UCAV are often required along Different Flight while reach specified region, pass through association
The time relationship of multi rack UCAV is adjusted, realizes each UCAV in space, temporal harmonious, method has:Adjust the speed of UCAV
Degree, flight track length increase maneuver;
By adjusting the speed of each UCAV so that multiple UCAV reach target and perform task simultaneously.
Beneficial effects of the present invention:
The present invention realizes that multiple no-manned plane reaches simultaneously, improves flexibility, robustness and the reliability of control strategy.It improves whole
Multiple no-manned plane cooperation efficiency so that entire multiple no-manned plane big system of fighting plays the effect of bigger in war.
Specific embodiment
Multiple no-manned plane (UAV) at the same arrival be typical Collaborative Control problem, have in formation flight, concerted attack should
With.Using multiple no-manned plane cooperative multiple target attack as application background, the problem that reached simultaneously to multiple no-manned plane is studied.It considers
The characteristics of dynamic of battlefield surroundings and uncertain and unmanned plane itself, proposes that one kind reaches simultaneously suitable for multiple no-manned plane
Decentralized control method, content include only rely on local message interaction decentralized control structure and based on consistency algorithm
Decentralized control strategy.The two is combined according to the different characteristics of path planning and speed control, utilizes the mutual of them
Benefit advantage tackles the influence of the unfavorable factors such as tracking error and pop-up threats.
1) the decentralized Harmonic Control description that more UAV are reached simultaneously
It is assumed that in certain subtask, there is n framves unmanned plane to reach k previously given target location in synchronization,
Middle n >=k >=1.The initial position of unmanned plane is the physical location at its current time, may in space Arbitrary distribution, it is usually real
Distance objective point has certain scope in the case of border, it is impossible to too close to target.Each unmanned plane have in initial plan and
Only there are one definite target location, the target locations of different unmanned planes may it is identical may also be different, point of target point task
With generally segregation reasons are good in advance or made one's choice according to real-time condition by overall commanding agency.There is accident
In the case of, according to mission requirements, for the task object of unmanned plane it is possible that can change online, this needs whole system to possess burst
In the case of task reassign ability.It is related to obstacle (no-fly zone) that another hypothesis unmanned plane can obtain threat in advance or in real time
Information independently offline or online path planning and can provide the estimate of path length in real time, can be independently along path planning
Flight.Accident carries out online real-time path planning under a situation arises, for the target conditions of reassignment, and can be quickly
Obtain the information of introductory path.
Multiple no-manned plane reaches the goal in research of Collaborative Control technology simultaneously, is to find a kind of control method or strategy, passes through
Coordinate the motion state between each unmanned plane, reach above-mentioned task objective, and avoid the influence of unfavorable factor as far as possible, such as path
In addition error etc. can in real time be adjusted, as far as possible under bursty state according to the variation of job change and unmanned plane state
Guarantee task completion.Task has been carried out verifying before starting to the threat around target, to each nothing before task starts
Man-machine to have carried out task distribution and path planning, unmanned plane must also avoid threat and no-fly zone during task.Due to nothing
Man-machine flight path and speed are not fixed, therefore can be by adjusting path length and flying speed to ensure simultaneously
It reaches.When in task process, an emergency occurred, such as the exemplary sudden threat of the darker regions in figure, system needs
Task reassigns and the ability of path replanning, while in the case where not changing Collaborative Control algorithm, ensures to reach simultaneously and appoint
The completion of business.
2) unmanned plane motion model is established
The motion model of unmanned plane is studied first.The movement of unmanned plane can regard a directional velocity as and be connected in axis
On rigid motion, velocity magnitude within the specific limits can consecutive variations.In actual path planning and Collaborative Control algorithm,
Generally change without considering the flying height of unmanned plane, be the particle moved in two dimensional surface by unmanned aerial vehicle vision.The letter of unmanned plane
Change motion model to be taken as:
In formula:I ∈ { 1,2 ..., n } are the number of unmanned plane;[xi yi]T∈R2For plan-position coordinate;vi、And ωi
Respectively flying speed, course angle and course angular speed.UAV course anglesDefinition be, in reference frame XOY, UCAV speed
Spend the angle between direction and OX direction of principal axis.This proximate nutrition is wide, depends primarily upon the selection of reference frame, if phase
For Land Navigation Installation, this angle be exactly the actual course line of UCAV, the direction of advance and the physical location of aircraft to guidance station it
Between line this two rays angle, the folder between the direction of advance of aircraft and direct north is just referred to without reference to object
Angle can also be conveniently defined according to mission requirements with what is resolved.
It is assumed that the speed that the flight control system of unmanned plane has the function of automatic pilot is kept and course is kept, it can be with
Speed command and the course angle instruction that track gives.The speed of automatic pilot is kept and course keeps approximate with first-order dynamic model
Description:
In formula:vi cWithThe respectively speed command of automatic pilot and course angle instruction;av,iWithFor with unmanned plane
And its normal number that state of flight is related.System passes through speed command vi cIt is instructed with course angleEach unmanned plane to be controlled to transport
It is dynamic, so as to track the path cooked up, the Collaborative Control effect that reaches by adjusting speed while reach.
Collaborative Control algorithm mainly by providing speed command signal in real time, and each UCAV is by adjusting the speed of itself
Size reaches consistent to ensure to reach this variable of the remaining time of respective target, and so as to ensure to reach simultaneously, formation is beaten simultaneously
The effect hit increases success attack rate and increase damage effectiveness.On the basis of this target, the adjusting range of speed is bigger,
Speed can change faster, more can guarantee the validity of algorithm in all cases.However, real unmanned plane has flight
Rate limitation and course angle rate limitation, from the research level of current unmanned plane and from reduce unmanned plane cost in use in terms of
Consider, constraint, the constraint of acceleration performance of speed bound can not ignore.In the research of this paper, unmanned plane
Kinematic constraint is accounted for by the following conditions:0 < vmin,i≤vi≤vmax,i,The speed v of unmanned planeiIt can
In its allowed band [vmin,i,vmax,i], (0 < vmin,i< vmax,i) in variation, meanwhile, course angleChange rate there is also upper
Limit value ωmax,i.In view of the restrictive condition in terms of unmanned mechanomotive force, real unmanned plane also has acceleration limitation, there is acceleration
Excursion [a of degreemin,i,amax,i],(amin,i0 < a of <max,i):
3) while algorithm research is reached
In multiple no-manned plane Collaborative Control, usually before take-off or task start before target area is visited in advance
It surveys, verifies threatening area, carry out task distribution afterwards.Path planning is carried out for the original state of each unmanned plane, and is carried out
Optimization processing.Its Global Optimization Model can usually be established:
In formula, P is the set of the feasible flight path of all unmanned planes;piFor the flight path of the i-th frame unmanned plane;Each group
(p1,p2,…,pN) form a kind of scheme of path planning;J is a kind of target function of definition, according to the difference of task, J's
Definition is also different, such as the task object having is that flight path is most short, has plenty of whole threat minimum etc..In order to study conveniently,
It is the algebraical sum of each unmanned plane during flying path cost to generally assume that total cost:
Here unmanned plane during flying path cost is defined by path length cost and cost two parts is threatened to form:
Ji=kJlength,i+(1-k)Jthreat,i (0≤k≤1) (5)
In formula, the tactics tendency that the value of coefficient k embodies policymaker is different with the emphasis of task, k=0 expression decision-makings
Person no matter path length only demand it is most safe, k=1 represent policymaker only demand shortest path without considering safety factor.
Global Optimization Model is static models, can solve to obtain global optimum path offline.But when unmanned plane, target or
The model also changes correspondingly when threat changes, and at this moment must carry out weight-normality online and draw.When battlefield is uncertain and dynamic
When more significant, unmanned plane, target or threaten will change dramatically so that weight-normality draw frequency increase, this may advise path
The real-time drawn is deteriorated.For such case, the rolling optimization model that multiple no-manned plane cooperates with path planning can be also established:
Rolling optimization method substitutes the solution of global issue with the solution for being scrolled into a series of capable local problems online,
Rolling each step only to local problem's Optimization Solution in current scrolling window, and implement current optimal policy.
This method is designed and is realized by following basic ideas in terms of path planning:
1. no longer using fuel consumption is minimum or the minimum major control target as single rack unmanned plane of path cost, but it is logical
Cross adjustment flying speed and path length makes unmanned plane at the appropriate speed while reaches as far as possible, and retains larger speed adjustment
Allowance.
2. in view of path planning specific rate controls more cost source, speed is mainly adjusted by control arrival time, is only existed
Residual paths length is adjusted as necessary by path planning.
3. by introducing external reference signal or when control instruction allows the operator control or intervene the arrival of unmanned plane
Between.
This method path planning is specifically using environment description of the Voronoi diagram as unmanned plane path planning in two dimensional surface
Instrument.In Voronoi diagram, the barriers such as threat are located in the polygon that multiple summits are surrounded, and unmanned plane can be moved along side.For
Meet cotasking demand, often require different UCAV along Different Flight while reach and specify region.Generally by coordinating multi rack
The time relationship of UCAV realizes each UCAV in space, temporal harmonious, and common method has:It adjusts the speed of UCAV, fly
Row flight path length increases maneuver etc..By adjusting the speed of each UCAV so that multiple UCAV reach target and perform simultaneously to be appointed
Business, since the adjusting range of UCAV speed is limited, it is desirable that the flight path difference in length of each UCAV cannot be too big.It uses at present herein
Voronoi diagram plans each aircraft path, and too in-depth study is not done in terms of path optimization, according to goal in research, optimization
Selection ensures that the flight path difference in length of each UCAV is little as far as possible, can so ensure in the case where some pop-up threats occur,
There is larger control adjustment surplus.Designed UCAV datum speeds are also taken in maximin as far as possible close to intermediate portion
Point.
Claims (3)
1. a kind of multiple no-manned plane reaches cooperative control method simultaneously, the method is characterized in that:
(A), the decentralized coordination control description that more UAV are reached simultaneously;
In task, there is n framves unmanned plane to reach k previously given target location, wherein n >=k >=1 in synchronization;
(B) unmanned plane motion model is established
The movement of unmanned plane is connected in the rigid motion on axis for a directional velocity, and velocity magnitude within the specific limits may be used
Consecutive variations.In actual path planning and Collaborative Control algorithm, generally change without considering the flying height of unmanned plane, by nothing
It is man-machine to be considered as the particle moved in two dimensional surface.The simplification motion model of unmanned plane is taken as:
In formula:I ∈ { 1,2 ..., n } are the number of unmanned plane;[xi yi]T∈R2For plan-position coordinate;vi、And ωiRespectively
Flying speed, course angle and course angular speed;
UAV course anglesDefinition be, in reference frame XOY, the angle between UCAV directional velocities and OX direction of principal axis;Depend on
In the selection of reference frame, if compared with Land Navigation Installation, this angle is exactly the actual course line of UCAV, the direction of advance
And the physical location of aircraft just refers to aircraft without reference to object to the angle of this two rays of the line between guidance station
Angle between direction of advance and direct north;
The flight control system of unmanned plane has the function of that the speed of automatic pilot is kept and course is kept, and tracks given speed
Instruction and course angle instruction;The speed of automatic pilot is kept and course is kept with first-order dynamic model approximate description:
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In formula:vi cWithThe respectively speed command of automatic pilot and course angle instruction;av,iWithFor with unmanned plane and its
The related normal number of state of flight;System passes through speed command vi cIt is instructed with course angleThe each unmanned plane is controlled to move, from
And the path cooked up is tracked, the Collaborative Control effect for reaching by adjusting speed while reaching;
Collaborative Control algorithm mainly by providing speed command signal in real time, and each UCAV is by adjusting the velocity magnitude of itself
Reach consistent to ensure to reach this variable of the remaining time of respective target, so as to ensure to reach simultaneously, form strike simultaneously
Effect increases success attack rate and increase damage effectiveness;
The kinematic constraint of unmanned plane is accounted for by the following conditions:0 < vmin,i≤vi≤vmax,i,
The speed v of unmanned planeiIn its allowed band [vmin,i,vmax,i], (0 < vmin,i< vmax,i) in variation, meanwhile, course angle
Change rate there is also upper limit value ωmax,i;
Excursion [a of accelerationmin,i,amax,i],(amin,i0 < a of <max,i):
(C) while algorithm is reached;
In multiple no-manned plane Collaborative Control, before take-off or task start before target area is detected in advance, verify prestige
Region is coerced, carries out task distribution afterwards;Path planning is carried out for the original state of each unmanned plane, and carries out optimization processing;
Its Global Optimization Model can usually be established:
In formula, P is the set of the feasible flight path of all unmanned planes;piFor the flight path of the i-th frame unmanned plane;Each group of (p1,
p2,…,pN) form a kind of scheme of path planning;J is a kind of target function of definition;
Total cost is the algebraical sum of each unmanned plane during flying path cost:
<mrow>
<mi>J</mi>
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<mn>1</mn>
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Here unmanned plane during flying path cost is defined by path length cost and cost two parts is threatened to form:
Ji=kJlength,i+(1-k)Jthreat,i(0≤k≤1) (5)
In formula, the tactics tendency that the value of coefficient k embodies policymaker is different with the emphasis of task, and k=0 expressions policymaker is not
Pipeline electrical path length only demand it is most safe, k=1 represent policymaker only demand shortest path without considering safety factor;
Global Optimization Model is static models, and offline solve obtains global optimum path;But when unmanned plane, target or threaten hair
The model also changes correspondingly during changing, and at this moment must carry out weight-normality online and draw.When battlefield is uncertain and dynamic is more significant
When, unmanned plane, target or threaten will change dramatically so that weight-normality draw frequency increase, this may make the reality of path planning
When property is deteriorated;For such case, the rolling optimization model that multiple no-manned plane cooperates with path planning is established:
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Rolling optimization method substitutes the solution of global issue with the solution for being scrolled into a series of capable local problems online, is rolling
Dynamic each step is only to local problem's Optimization Solution in current scrolling window, and implement current optimal policy.
2. multiple no-manned plane according to claim 1 reaches cooperative control method simultaneously, the method is characterized in that:
In the decentralized coordination control description reached simultaneously in (A), more UAV:
The initial position of unmanned plane is the physical location at its current time, the Arbitrary distribution in space, under usual actual conditions away from
There is certain scope from target point, it is impossible to too close to target;
Each unmanned plane one and only one definite target location in initial plan, the target location of different unmanned planes can
Can it is identical may also be different, the distribution of target point task generally in advance segregation reasons it is good or according to real-time condition by referring generally to
Mechanism is waved to make one's choice;
In the case where there is accident generation, according to mission requirements, if the task object of unmanned plane changes online, this needs is whole
The task that a system possesses under emergency case reassigns ability;
It is another to assume that unmanned plane obtains in advance or in real time and threaten and the relevant information of obstacle, it can independently offline or online path planning
And the estimate of path length is provided in real time, it can independently fly along path planning;
Accident carries out online real-time path planning under a situation arises, for the target conditions of reassignment, and can be quickly
Obtain the information of introductory path;
Multiple no-manned plane reaches the goal in research of Collaborative Control technology simultaneously, by coordinating the motion state between each unmanned plane, reaches
To above-mentioned task objective, in addition can in real time be adjusted according to the variation of job change and unmanned plane state under bursty state
It is whole, ensure the completion of task as far as possible;Task has been carried out verifying before starting to the threat around target, before task starts
Each unmanned plane has been carried out task distribution and path planning, during task unmanned plane must also avoid threat and it is no-fly
Area;
Since the flight path and speed of unmanned plane are not fixed, by adjusting path length and flying speed to protect
Card reaches simultaneously;
When in task process, an emergency occurred, sudden threat, system needs task and reassigns and path replanning
Ability, while in the case where not changing Collaborative Control algorithm, ensure to reach the completion of task simultaneously.
3. multiple no-manned plane according to claim 1 reaches cooperative control method simultaneously, the method is characterized in that:
Path planning specifically uses environment description tool of the Voronoi diagram as unmanned plane path planning in two dimensional surface;
In Voronoi diagram, the barriers such as threat are located in the polygon that multiple summits are surrounded, and unmanned plane is moved along side;
In order to meet cotasking demand, different UCAV are often required along Different Flight while reach specified region, it is more by coordinating
The time relationship of frame UCAV realizes each UCAV in space, temporal harmonious, and method has:It adjusts the speed of UCAV, fly
Row flight path length increases maneuver;
By adjusting the speed of each UCAV so that multiple UCAV reach target and perform task simultaneously.
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