CN113741500A - Unmanned aerial vehicle air combat maneuver decision method for imitating Harris eagle intelligent predation optimization - Google Patents
Unmanned aerial vehicle air combat maneuver decision method for imitating Harris eagle intelligent predation optimization Download PDFInfo
- Publication number
- CN113741500A CN113741500A CN202110995706.6A CN202110995706A CN113741500A CN 113741500 A CN113741500 A CN 113741500A CN 202110995706 A CN202110995706 A CN 202110995706A CN 113741500 A CN113741500 A CN 113741500A
- Authority
- CN
- China
- Prior art keywords
- eagle
- blue
- red
- airplane
- intelligent
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 67
- 238000005457 optimization Methods 0.000 title claims abstract description 60
- 230000006870 function Effects 0.000 claims abstract description 57
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 34
- 239000011159 matrix material Substances 0.000 claims abstract description 33
- 230000008901 benefit Effects 0.000 claims abstract description 14
- 230000007246 mechanism Effects 0.000 claims abstract description 12
- 230000009471 action Effects 0.000 claims description 38
- 230000008569 process Effects 0.000 claims description 31
- 238000013461 design Methods 0.000 claims description 15
- 239000013598 vector Substances 0.000 claims description 15
- 238000011156 evaluation Methods 0.000 claims description 14
- 230000006399 behavior Effects 0.000 claims description 8
- 230000009286 beneficial effect Effects 0.000 claims description 3
- 239000004615 ingredient Substances 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 241000272184 Falconiformes Species 0.000 abstract description 91
- 239000002245 particle Substances 0.000 abstract description 4
- 238000012545 processing Methods 0.000 abstract description 2
- 238000011161 development Methods 0.000 description 9
- 241001465754 Metazoa Species 0.000 description 8
- 238000004364 calculation method Methods 0.000 description 5
- 230000008859 change Effects 0.000 description 4
- 238000006243 chemical reaction Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 238000006073 displacement reaction Methods 0.000 description 3
- 230000009189 diving Effects 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 230000001788 irregular Effects 0.000 description 2
- 230000009191 jumping Effects 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 241000282414 Homo sapiens Species 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 230000009194 climbing Effects 0.000 description 1
- 238000013211 curve analysis Methods 0.000 description 1
- 238000009795 derivation Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000002787 reinforcement Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
- G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses an unmanned aerial vehicle air combat maneuver decision method for imitating Harris eagle intelligent predation optimization, which comprises the following steps: the method comprises the following steps: building a six-degree-of-freedom airplane model and a controller; step two: designing a tactical planning maneuvering instruction generator; step three: designing a red and blue game scoring matrix; step four: designing a hybrid strategy mobile decision objective function; step five: designing an optimization algorithm for imitating the intelligent predation of Harris hawks; step six: and updating the state of the six-degree-of-freedom airplane. The invention has the advantages that: 1) the control object is a six-degree-of-freedom nonlinear airplane model for simulating a real airplane, and has higher practical application value compared with a three-degree-of-freedom airplane particle model; 2) constructing a maneuvering decision objective function by using a game mixing strategy, and processing the problem of converting constraint conditions into unconstrained optimization; 3) a Harris eagle intelligent predation optimization algorithm based on a multidimensional learning mechanism is designed, the population diversity is improved, and the situation that the intelligent predation optimization algorithm falls into a local optimal solution is avoided.
Description
Technical Field
The invention discloses an unmanned aerial vehicle air combat maneuver decision method for imitating Harris eagle intelligent predation optimization, and belongs to the field of air combat autonomous decision.
Background
The autonomous air Combat is one of the important modes of future war, Unmanned Combat Aircrafts (UCAV) can avoid casualties and deal with the severe conditions which are difficult to endure by human beings, and are the main forces in the future air Combat, and the strength of the air Combat capability determines the dominance of the war to a great extent. The core of the unmanned aircraft air combat process is maneuver decision, and the quality of the maneuver decision is directly related to the success or failure of the two parties.
Maneuver decision methods are broadly divided into three categories: one is based on mathematical solution, represented by differential game, the method has clear mathematical concept, but solves complex problems, and has great difficulty in solving complex problems; one type is based on machine search, and representative methods comprise matrix game, Monte Carlo tree search, Markov decision and the like, and the methods are most widely applied and have strong operability; one type is based on data training, typical methods include reinforcement learning, genetic fuzzy trees and the like, many unexpected results can be derived through the learning of a large number of samples, the method is a new type of air combat decision method, but the training process is very time-consuming, and still faces a lot of problems to be overcome.
The optimization algorithm imitating the harris eagle intelligent predation is a mathematic of the harris eagle intelligent predation behavior, is a novel population-based optimization algorithm inspired by nature, and has a inspiration from the harris eagle cooperative hunting behavior. Harris hawk needs to catch a prey and experience a long hunting process, and the hunting process is summarized into three stages: the method comprises a hunting search stage, a conversion stage and a hunting stage, wherein each stage has different attack strategies, and the hunting process of the three stages is mapped into an optimization algorithm in a mathematical form, namely: the algorithm has the advantages of few parameters needing to be set, easy application and good optimization capability in an exploration stage, a conversion stage and a development stage.
In conclusion, the invention provides an unmanned aerial vehicle air combat maneuver decision method simulating Harris eagle intelligent predation optimization, which combines a game mixing strategy to optimize to obtain optimal maneuvers and improve the unmanned aerial vehicle combat effectiveness.
Disclosure of Invention
The invention aims to provide an unmanned aerial vehicle air combat maneuver decision method for imitating Harris eagle intelligent predation optimization, which aims to solve the maneuver decision problem in the air combat process of unmanned aerial vehicles so as to improve the combat efficiency and the autonomous decision level; the invention carries out maneuvering decision based on a matrix game method, and has the improvement that an optimization objective function is designed by using a game mixing strategy, optimization is carried out by using an optimization algorithm imitating Harris eagle intelligent predation, and after an optimal mixing strategy is obtained, final maneuvering is determined by using a roulette mode. The hybrid strategy maneuver decision method improves the curability of the traditional pure strategy maximum and minimum algorithm, combines with an optimization algorithm, enables the optimal solution to be more flexible, and improves the effectiveness of the maneuver decision.
In order to achieve the purpose, the invention provides an unmanned aerial vehicle air combat maneuver decision method imitating Harris eagle intelligent predation optimization, which comprises the following specific implementation steps:
the method comprises the following steps: building six-freedom-degree airplane model and controller
S11, constructing a six-degree-of-freedom airplane model
The six-freedom-degree nonlinear model simulating the real airplane is adopted, instead of a particle model which is usually adopted, and the motion equation of the six-freedom-degree airplane comprises a kinetic equation and a kinematic equation, and can be specifically divided into displacement motion of the center of mass of the airplane and rotation motion around the center of mass.
S12 controller design based on attack angle and roll angle
The controller inputs the instructions of the attack angle and the roll angle and outputs four control quantities of an accelerator lever, an elevator deflection angle, an aileron deflection angle and a rudder deflection angle of the airplane, and the feedback information is the state quantity of the airplane.
Step two: design tactical planning maneuver instruction generator
S21 three-degree-of-freedom maneuvering instruction generator
The maneuvering instruction generator adopts a three-degree-of-freedom simplified airplane model, tangential overload, normal overload and speed roll angle are used as input of the maneuvering instruction generator, and flying speed, track inclination angle and course angle are used as output, so that the control of the airplane motion track can be realized. The tangential overload is mainly used for adjusting the speed of the airplane, and the normal overload and the roll angle are mainly used for adjusting the pitch angle and the yaw angle of the airplane.
S22 tactical planning maneuvering library
The maneuver library is designed to use the normal overload and the roll angle as control commands, and the expected maneuvers can be generated through the combination of different normal overload and roll angles. All the actions in the action libraries of the red and blue parties are combined to form a game action matrix.
The flexibility of the action library in design is expandability, and under the condition of meeting the performance limit of the airplane, a user can set the values of normal overload and speed and roll angle in the action library according to the requirement, and the values are spaced appropriately, so that strong maneuverability can be obtained.
Step three: designing a scoring matrix of a red and blue game
S31 direct threat based situation assessment function design
The most direct threat in air combat is represented by the angle relationship and the distance relationship between two parties, so that two components of an air combat situation assessment function can be defined: the angle threat index and the distance threat index are shown in the attached figure 1. The overall situation assessment function is the product of the angular threat index and the distance threat index.
S32, calculating game score matrix
And (4) a game scoring matrix, namely, aiming at the state quantities of the red and blue parties in each step, corresponding to the game maneuvering matrix in the step two, and respectively calculating the situation evaluation function under each action of the two parties, namely forming the game scoring matrix.
In the invention, the red party is recorded as the own party, the blue party is recorded as the enemy, and the larger the evaluation function value of the expected situation of the own party is, the more beneficial the evaluation function value is, and the opposite blue party is.
Step four: designing hybrid strategy maneuver decision objective function
The probability of selecting each maneuver is described by a hybrid strategy, which is a vector with dimensions of the number of maneuvers, and the sum of which is 1. The formula of the mixture of the ingredients marked with red is Pr ═ Pr1,Pr2,...,Prm]TThe mixed strategy of the blue square is Pb ═ Pb1,Pb2,...,Pbn]T。
The maneuvering decision process is divided into two steps: the first step is to predict the mixing strategy of the blue party, and the second step is to calculate the mixing strategy of the red party according to the mixing strategy of the blue party. The two parties of the red and blue are in a zero-sum game state, and each party tries to maximize the benefit of the party and minimize the benefit of the other party.
S41 target function of prediction blue square mixing strategy
Assuming that the blue party selects the mixing strategy Pb, the yield obtained by the red party selecting the ith motor action is shown as the formula (1). Then all the red parties are selected flexibly, and the red parties want to maximize the benefits of the red parties, as shown in the formula (2).
Wherein s isijIs the element of the ith row and the jth column in the game scoring matrix; benefitriRepresenting the income obtained by the selection of the ith action by the red party under the condition of the blue party mixing strategy Pb; benefitrmaxRepresenting the maximum gain obtained by the red under all choices, under the blue-side mixing strategy Pb.
From a bluesquare perspective, Benefit is then expectedrmaxThe smaller the better, the objective function of the blue square is shown in formula (3), and the constraint condition is shown in formula (4).
In which Pb is*The optimal mixing strategy is the blue square, namely the predicted blue square mixing strategy.
S42, calculating an objective function of the red square mixing strategy
On the basis of the predicted blue square mixed strategy, the red square aims to find the optimal strategy Pr*The gain is maximized, the objective function is shown in formula (5), and the constraint condition is shown in formula (6).
Wherein, Pr*And S is a game score matrix.
After the hybrid strategy of the maneuver decision is determined, how to select the final maneuver is also a problem, and the final maneuver is determined by adopting a roulette mode in the invention, namely, the maneuver corresponding to the probability maximum in the hybrid strategy is selected. Because the roulette is selected according to probability in nature, the uncertainty in the game process is better met.
Step five: optimization algorithm for designing intelligent predation imitating Harris hawk
S51 Harris eagle optimization algorithm
Harris eagle optimization is a biological population heuristic optimization algorithm, and the algorithm idea is to simulate the intelligent predation mechanism of harris eagle, and mainly comprises the steps of exploring prey, attacking suddenly and different attack strategies. Therefore, the harris eagle optimization algorithm is divided into three stages, namely an exploration stage, a transformation stage and a development stage. The position of each harris eagle represents a candidate solution for the optimization algorithm, and the position of the prey represents the optimal solution.
(1) The exploration phase, harris eagle observes and monitors the surrounding environment, waiting for the appearance of a hunting animal. The exploration process has two strategies, and Harris eagle randomly selects one strategy according to probability. The position update formula for harris eagle is as follows:
wherein X (t) represents the position vector of the eagle at the current moment, X (t +1) represents the position vector of the eagle at the next moment, and Xprey(t) represents the position of the prey, Xrand(t) represents the position of a random eagle in the current eagle group, r1,r2,r3,r4P is a random number between 0 and 1, and is randomly generated in each iteration, XbandminAnd XbandmaxRespectively the minimum and maximum position reachable by the hawk, i.e. the boundary limit to be solved, Xc(t) is the central position of the current eagle group, and the calculation formula is as follows:
wherein, Xi(t) represents the position vector of the ith eagle at the time t, and N is the total number of the eagles.
(2) And in the conversion stage, the eagle can be converted between the exploration stage and the development stage according to the escape energy change of the prey. The formula for calculating the escape energy of the prey is as follows:
wherein E represents the escape energy of the prey, E0And representing the initial state of energy in each iteration process, randomly generating the energy between-1 and 1, and taking T as the total iteration number.
The exploration phase is performed when | E | ≧ 1, and the development phase is performed when | E | < 1.
(3) In the development stage, the hawk can launch a sudden attack according to the position of the searched hunting animal, and the hunting animal always goes to the greatest extent to escape to avoid the attack, so the hawk can adopt different attack strategies according to different escape behaviors of the hunting animal, and the method is specifically divided into four types: soft-surrounding attack, hard-surrounding attack, fast dive soft-surrounding attack, and fast dive hard-surrounding attack.
The basis of the division among different strategies is the escape energy E of the prey and the probability r of successful escape, wherein r is a random number between 0 and 1 and is updated in each iteration process. The escape energy is used for dividing soft attack and hard attack, when | E | > 0.5, the escape energy of the prey is large, soft attack is adopted, and when | E | < 0.5, the prey is nearly exhausted, and hard attack is adopted. The probability of successful escape is used for determining whether to adopt rapid dive, when r is more than or equal to 0.5, the object is possible to escape and fail, the rapid dive is not needed, when r is less than 0.5, the object is possible to escape and adopt the rapid dive. The specific process is as follows.
1) Soft enclosure tap
When r is more than or equal to 0.5 and | E | > is more than or equal to 0.5, a soft attack strategy is adopted, and the position updating mode of the hawk is as follows:
X(t+1)＝Xprey(t)-X(t)-E|JXprey(t)-X(t)| (10)
wherein J represents the random jump intensity of the prey in the escape process, and J is 2 (1-r)5)，r5Is a random number between 0 and 1.
2) Hard enclosing tap
When r is more than or equal to 0.5 and | E | is less than 0.5, a soft surrounding attack strategy is adopted, and the position updating mode of the hawk is as follows:
X(t+1)＝Xprey(t)-E|Xprey(t)-X(t)| (11)
3) fast dive soft surrounding attack
When r is less than 0.5 and | E | ≧ 0.5, a rapid dive soft attack strategy is adopted, and the process is more intelligent than the previous pure soft attack.
To simulate the escape pattern and the motion of a jumping frog of a prey, the concept of Levy Flight (LF) is cited. LF is used to simulate the zig-zag cheating action of the game during the escape phase and the irregular, sudden and rapid dive of hawks around the escaping game.
The position updating formula of the hawk is as follows:
wherein, the fitness () is a fitness function, Y is a location update without a lave flight, and Z is a location update with a lave flight, and the specific calculation formula is as follows:
Y＝Xprey(t)-E|JXprey(t)-X(t)| (13)
Z＝Y+S×LF(D) (14)
where D represents a dimension and S is a random vector of size 1 × D. LF () is the Layvin flight function, u and v are random numbers between 0 and 1, and β is a constant.
4) Fast diving hard surrounding attack
When r is less than 0.5 and when the | E | is less than 0.5, a rapid dive hard attack strategy is adopted, and the eagle position updating strategy is as follows:
Y′＝Xprey(t)-E|JXprey(t)-Xc(t)| (18)
Z′＝Y′+S×LF(D) (19)
s52 Harris eagle intelligent predation optimization based on multi-dimensional learning
Aiming at the problem that the Harris eagle optimization algorithm is easy to fall into local optimum, the invention provides an improvement thought: changing the learning object in the exploration stage, and fully embodying the intelligent predation behavior of Harris hawk by utilizing a multidimensional learning mechanism.
In the exploration stage, the learning object in the original algorithm is an eagle in an eagle group, the improved idea is to utilize a multi-dimensional learning mechanism to update the position of the eagle in each dimension respectively, and to not learn other eagles blindly at random, but to determine the learning object according to the fitness function value, so that the intelligence of the eagle is reflected, the search efficiency is improved, the population diversity is increased, and the situation that the eagle is trapped in local optimum is avoided. The updating mode of the exploration phase is changed from the formula (7) to the formula (20).
Wherein,
indicating the position of the ith eagle in the d dimension at the t iteration, fi indicates the index of the number of the eagle,
representing the position in the d dimension of the fi eagle. And for each dimension update, randomly selecting two eagles in the eagle group, calculating the fitness value, taking the dominant eagle as a learning object, recording the number of the eagle as an index, and then, fi is equal to the index.
S53 unmanned aerial vehicle air combat maneuver decision imitating Harris eagle intelligent predation optimization
For the above problem, the position vector of the hawk is the mixing strategy in step four, the fitness function is the objective function in step four, and for the maximization problem, the reciprocal is converted into the minimization problem. It should be noted that the objective function in the fourth step is constrained, and the proposed optimization algorithm cannot be directly applied, so that the present invention adopts a skill process, the constraint requirement variable is between 0 and 1, and the sum is 1, then the position change of the eagle is set to be between 0 and 1, and the sum is ensured to be 1 by a normalization method, so as to solve the constrained optimization problem. The unmanned aerial vehicle air combat maneuver decision flow imitating Harris eagle intelligent predation optimization is shown in figure 2.
Step six: updating six-degree-of-freedom aircraft state
After the optimal maneuver is determined, the three-degree-of-freedom control instruction is input into the six-degree-of-freedom airplane controller, and then the three-degree-of-freedom control instruction is converted into six-degree-of-freedom airplane control quantity, so that the airplane state can be updated.
The invention provides an unmanned aerial vehicle air combat maneuver decision method for imitating Harris eagle intelligent predation optimization, which has the following main advantages: 1) the control object is a six-degree-of-freedom nonlinear airplane model for simulating a real airplane, and has higher practical application value compared with a three-degree-of-freedom airplane particle model; 2) constructing a maneuvering decision objective function by using a game mixing strategy, and processing the problem of converting constraint conditions into unconstrained optimization; 3) a Harris eagle intelligent predation optimization algorithm based on a multidimensional learning mechanism is designed, the population diversity is improved, and the situation that the intelligent predation optimization algorithm falls into a local optimal solution is avoided.
Drawings
FIG. 1 is a schematic diagram of the red and blue aspects
FIG. 2 is a flow chart of an unmanned aerial vehicle air combat maneuver decision method for imitating Harris eagle intelligent predation optimization
FIG. 3a, b partial curve analysis of air battle process of red and blue parties
FIG. 4 flight path diagram of air battle process of red and blue parties
Detailed Description
The invention relates to an unmanned aerial vehicle air combat maneuver decision method for imitating Harris eagle intelligent predation optimization, which comprises the following concrete implementation steps:
the method comprises the following steps: building six-freedom-degree airplane model and controller
S11, constructing a six-degree-of-freedom airplane model
The invention adopts a six-degree-of-freedom nonlinear model for simulating a real airplane instead of a particle model which is usually adopted.
The motion equation of the six-degree-of-freedom airplane comprises a dynamic equation and a kinematic equation, and can be specifically divided into displacement motion of the center of mass of the airplane and rotation motion around the center of mass. The control quantity U of the aircraft includes: throttle lever deltaTAngle of deflection delta of elevatoreViceWing deflection angle deltaaRudder deflection angle deltarAnd is recorded as: u shapeT＝[δT,δe,δa,δr]T. The controlled quantity of the airplane comprises 12 state quantities which are respectively as follows: three position quantities xg,ygH, roll angle phi, pitch angle theta, yaw angle psi, airflow velocity V, attack angle alpha, sideslip angle beta, roll angular velocity p, pitch angular velocity q, yaw angular velocity r, recorded as: xT＝[xg,yg,h,φ,θ,ψ,V,α,β,p,q,r]T. The following equation of motion for a six-degree-of-freedom aircraft is given without derivation:
(1) equation of motion of displacement
Kinematic equation:
kinetic equation:
(2) equation of rotational motion
Kinematic equation:
kinetic equation:
wherein,
is the differential of the corresponding variable x; x is the number ofg,ygH are the three-dimensional position coordinates (x) of the aircraft, respectivelygNorth is positive, ygEast is positive, h is positive); u, v, w are respectively in x, y, z three-axis directions under the body coordinate systemSpeed; v is the flying speed, alpha is the attack angle, beta is the sideslip angle; phi is a roll angle, theta is a pitch angle, and psi is a yaw angle; p is the rolling angular velocity, q is the pitch angular velocity, and r is the yaw angular velocity; i isx,Iy,IzThe moment of inertia, I, of the aircraft about the axes x, y, z, respectivelyxzIs the product of inertia;
m and N are moments of the body axes in the x, y and z directions respectively.
On the basis of the airplane model, the structural parameters and the aerodynamic parameters of the airplane are added, and the related functions of the real airplane can be simulated.
S12 controller design based on attack angle and roll angle
Designing an elevator channel controller by feeding back the incidence angle and the pitch angle rate and combining an incidence angle instruction to generate an elevator control instruction; designing an aileron channel controller by feeding back a roll angle and a roll angle rate and combining a roll angle instruction to generate an aileron control instruction; and designing a yaw channel controller by feeding back an aileron control command, an attack angle, a roll angle rate, an overload in the y-axis direction and a yaw angle rate to generate a yaw control command.
On the basis of the control quantity obtained by the airplane trim, combining the generated commands of the elevator, the aileron and the yaw channel, and obtaining four final control quantities through a control execution mechanism of the airplane: the control of the airplane is realized through a throttle lever, an elevator deflection angle, an aileron deflection angle and a rudder deflection angle.
The controller has the input of the attack angle and the roll angle command and the output of the four control quantities of the airplane, and the feedback information is some state quantities of the airplane.
Step two: design tactical planning maneuver instruction generator
The control commands of the six-degree-of-freedom nonlinear aircraft model are attack angle commands and roll angle commands, and the maneuver commands to be selected generated by the decision layer are converted into the control layer command form of the six-degree-of-freedom aircraft through the calculation of the tactical planning layer. The plane centroid kinematics equation set can describe the flight path, so that the flight path can be used as a maneuvering instruction generator to simplify a decision model. And designing a maneuvering base by the control quantity of the maneuvering instruction generator, and planning all possible states of the airplane.
S21 three-degree-of-freedom maneuvering instruction generator
Overload refers to the ratio of the combined force of aerodynamic force and engine thrust acting on an aircraft to the aircraft weight. The kinematic equation set for the center of mass of an aircraft expressed by overload is as follows:
wherein v is the flight velocity; n isxIs tangential overload; n isfNormal overload; mu is a track inclination angle;
is a course angle; gamma is the speed roll angle; x is the number ofg,ygH are the three-dimensional position coordinates (x) of the aircraft, respectivelygNorth is positive, ygEast is positive, h is positive); g is the acceleration of gravity.
From the above equation, it can be seen that the tangential overload nxNormal overload nfThe speed roll angle gamma can be used as the input of a maneuvering instruction generator, and the flight speed, the track inclination angle and the course angle are used as the output, so that the control of the motion trail of the airplane can be realized. The tangential overload is mainly used for adjusting the speed of the airplane, and the normal overload and the roll angle are mainly used for adjusting the pitch angle and the yaw angle of the airplane.
S22 tactical planning maneuvering library
According to the design idea of a typical tactical action library, in order to realize some typical tactical maneuvers, such as level flight, turning, climbing, diving and the like, maneuvering instructions can be converted into control layer instructions, namely normal overload nfcAnd the roll angle gamma of the velocitycAnd forming a maneuvering action library, and realizing corresponding maneuvering action through the combination of different normal overload and speed and roll angles. For the six-degree-of-freedom airplane nonlinear model, the design based on the attack angle alpha is obtained through the control law designcAnd roll angleInstruction phicAccording to the normal overload n already generated by the maneuver libraryfcAnd the roll angle gamma of the velocitycThe position of a throttle lever of the six-degree-of-freedom airplane is kept unchanged, and gamma is adjustedcAs phicThe normal overload instruction is converted into an attack angle instruction and then is input into an attack angle autopilot loop of the airplane, and the maneuvering action control of the six-degree-of-freedom airplane is realized.
The library of maneuvers may be represented as:
nf＝[nf1,nf2,...,nfu]u (6)
γ＝[γ1,γ2,...,γw]w (7)
wherein n isfAnd gamma respectively represents the numeric vectors of normal overload and speed roll angle, u and w respectively correspond to the dimensions of the normal overload and the speed roll angle, and different values are taken to combine different maneuvering actions. L is a game engine base, composed of nfAnd the corresponding values of gamma are combined to generate u × w motor actions.
The flexibility of the action library in design is expandability, and under the condition of meeting the performance limit of the airplane, a user can set the values of normal overload and speed and roll angle in the action library according to the requirement, and the values are spaced appropriately, so that strong maneuverability can be obtained.
All the action combinations in the red and blue mobile action libraries can form the following game mobile matrix:
wherein L isrmLbnThe method shows that the red party selects the mth motor action in the motor library, and the blue party selects the nth motor action in the motor library.
Step three: designing a scoring matrix of a red and blue game
S31 direct threat based situation assessment function design
The air combat situation is the comprehensive expression of the situations of the air combat two parties, namely the red and blue two parties, and the most direct threat in the air combat is reflected in the angle relationship and the distance relationship of the two parties, so that two components of an air combat situation evaluation function can be defined: the angle threat index and the distance threat index are shown in the attached figure 1. The specific definition is as follows:
angular threat index:
wherein S isAIs an angular threat index; a. theRThe included angle between the speed direction of the red square aircraft and the connecting line direction of the red and blue square aircraft is formed; a. theBThe included angle between the speed direction of the blue square airplane and the connecting line direction of the red and blue square airplanes is formed.
Distance threat index:
SR＝e-(R-r)/k (11)
wherein S isRIs a distance threat index; r is the distance between two machines; r is the average attack range of the red and blue machine cannons, and r is (r)r+rb) 2; k is the sensitivity.
The situation assessment function is the product of the two factor indexes, and is recorded as:
S＝SASR (12)
wherein S is a situation evaluation function, SAIs an angular threat index; sRIs a distance threat index.
The larger the value of the above evaluation function S, the more dominant is the red side, and conversely, the smaller S, the more dominant is the blue side.
S32, calculating game score matrix
And (3) a game scoring matrix, namely, aiming at the state quantities of the red and blue parties in each step, corresponding to the game maneuvering matrix in the step two, respectively calculating the situation evaluation functions under each action of the two parties, namely forming the game scoring matrix, and expressing the game scoring matrix as follows:
wherein, the SS is a game score matrix; smnThe evaluation function value corresponding to the maneuvering action of the mth row and the nth column in the game matrix shown in the expression (9) is shown.
In the invention, the red party is recorded as the own party, the blue party is recorded as the enemy, and the larger the evaluation function value of the expected situation of the own party is, the more beneficial the evaluation function value is, and the opposite blue party is.
Step four: designing hybrid strategy maneuver decision objective function
The probability of selecting each maneuver is described by a hybrid strategy, which is a vector with dimensions of the number of maneuvers, and the sum of which is 1. The formula of the mixture of the ingredients marked with red is Pr ═ Pr1,Pr2,...,Prm]TThe mixed strategy of the blue square is Pb ═ Pb1,Pb2,...,Pbn]T。
The maneuvering decision process is divided into two steps: the first step is to predict the mixing strategy of the blue party, and the second step is to calculate the mixing strategy of the red party according to the mixing strategy of the blue party. The two parties of the red and blue are in a zero-sum game state, and each party tries to maximize the benefit of the party and minimize the benefit of the other party.
S41 target function of prediction blue square mixing strategy
Assuming that the blue party selects the mixing strategy Pb, the yield obtained by the red party selecting the ith maneuver is shown as equation (14). Then all the options of the red party are selected, and the red party wants to maximize the income thereof, as shown in the formula (15).
Wherein s isijIs the element of the ith row and the jth column in the game scoring matrix; benefitriRepresenting the income obtained by the selection of the ith action by the red party under the condition of the blue party mixing strategy Pb; benefitrmaxRepresenting the maximum gain obtained by the red under all choices, under the blue-side mixing strategy Pb.
From a bluesquare perspective, Benefit is then expectedrmaxThe smaller the better, the objective function of the blue square is shown in formula (16), and the constraint condition is shown in formula (17).
In which Pb is*The optimal mixing strategy is the blue square, namely the predicted blue square mixing strategy.
S42, calculating an objective function of the red square mixing strategy
On the basis of the predicted blue square mixed strategy, the red square aims to find the optimal strategy Pr*The gain is maximized, the objective function is shown in equation (18), and the constraint condition is shown in equation (19).
Wherein, Pr*And S is a game score matrix.
After the hybrid strategy of the maneuver decision is determined, how to select the final maneuver is also a problem, and the final maneuver is determined by adopting a roulette mode in the invention, namely, the maneuver corresponding to the probability maximum in the hybrid strategy is selected. Because the roulette is selected according to probability in nature, the uncertainty in the game process is better met.
Step five: optimization algorithm for designing intelligent predation imitating Harris hawk
S51 Harris eagle optimization algorithm
Harris eagle optimization is a biological population heuristic optimization algorithm, and the algorithm idea is to simulate the intelligent predation mechanism of harris eagle, and mainly comprises the steps of exploring prey, attacking suddenly and different attack strategies. Therefore, the harris eagle optimization algorithm is divided into three stages, namely an exploration stage, a transformation stage and a development stage. The position of each harris eagle represents a candidate solution for the optimization algorithm, and the position of the prey represents the optimal solution.
(1) The exploration phase, harris eagle observes and monitors the surrounding environment, waiting for the appearance of a hunting animal. The exploration process has two strategies, and Harris eagle randomly selects one strategy according to probability. The position update formula for harris eagle is as follows:
wherein X (t) represents the position vector of the eagle at the current moment, X (t +1) represents the position vector of the eagle at the next moment, and Xprey(t) represents the position of the prey, Xrand(t) represents the position of a random eagle in the current eagle group, r1,r2,r3,r4P is a random number between 0 and 1, and is randomly generated in each iteration, XbandminAnd XbandmaxRespectively the minimum and maximum position reachable by the hawk, i.e. the boundary limit to be solved, Xc(t) is the central position of the current eagle group, and the calculation formula is as follows:
wherein, Xi(t) represents the position vector of the ith eagle at the time t, and N is the total number of the eagles.
(2) And in the conversion stage, the eagle can be converted between the exploration stage and the development stage according to the escape energy change of the prey. The formula for calculating the escape energy of the prey is as follows:
wherein E represents the escape energy of the prey, E0And representing the initial state of energy in each iteration process, randomly generating the energy between-1 and 1, and taking T as the total iteration number.
The exploration phase is performed when | E | ≧ 1, and the development phase is performed when | E | < 1.
(3) In the development stage, the hawk can launch a sudden attack according to the position of the searched hunting animal, and the hunting animal always goes to the greatest extent to escape to avoid the attack, so the hawk can adopt different attack strategies according to different escape behaviors of the hunting animal, and the method is specifically divided into four types: soft-surrounding attack, hard-surrounding attack, fast dive soft-surrounding attack, and fast dive hard-surrounding attack.
The basis of the division among different strategies is the escape energy E of the prey and the probability r of successful escape, wherein r is a random number between 0 and 1 and is updated in each iteration process. The escape energy is used for dividing soft attack and hard attack, when | E | > 0.5, the escape energy of the prey is large, soft attack is adopted, and when | E | < 0.5, the prey is nearly exhausted, and hard attack is adopted. The probability of successful escape is used for determining whether to adopt rapid dive, when r is more than or equal to 0.5, the object is possible to escape and fail, the rapid dive is not needed, when r is less than 0.5, the object is possible to escape and adopt the rapid dive. The specific process is as follows.
1) Soft enclosure tap
When r is more than or equal to 0.5 and when the | E | ≧ 0.5, a soft attack strategy is adopted, and the position updating mode of the hawk is as follows:
X(t+1)＝Xprey(t)-X(t)-E|JXprey(t)-X(t)| (23)
wherein J represents the random jump intensity of the prey in the escape process, and J is 2 (1-r)5)，r5Is a random number between 0 and 1.
2) Hard enclosing tap
When r is more than or equal to 0.5 and | E | is less than 0.5, a soft surrounding attack strategy is adopted, and the position updating mode of the hawk is as follows:
X(t+1)＝Xprey(t)-E|Xprey(t)-X(t)| (24)
3) fast dive soft surrounding attack
When r is less than 0.5 and | E | ≧ 0.And 5, adopting a rapid dive soft-surrounding attack strategy, wherein the process is more intelligent than the previous pure soft-surrounding attack.
To simulate the escape pattern and the motion of a jumping frog of a prey, the concept of Levy Flight (LF) is cited. LF is used to simulate the zig-zag cheating action of the game during the escape phase and the irregular, sudden and rapid dive of hawks around the escaping game.
The position updating formula of the hawk is as follows:
wherein, the fitness () is a fitness function, Y is a location update without a lave flight, and Z is a location update with a lave flight, and the specific calculation formula is as follows:
Y＝Xprey(t)-E|JXprey(t)-X(t)| (26)
Z＝Y+S×LF(D) (27)
where D represents a dimension and S is a random vector of size 1 × D. LF () is the Layvin flight function, u and v are random numbers between 0 and 1, and β is a constant.
4) Fast diving hard surrounding attack
When r is less than 0.5 and | E | is less than 0.5, a rapid dive hard attack strategy is adopted, and the eagle position updating strategy is as follows:
Y′＝Xprey(t)-E|JXprey(t)-Xc(t)| (31)
Z′＝Y′+S×LF(D) (32)
s52 Harris eagle intelligent predation optimization based on multi-dimensional learning
Aiming at the problem that the Harris eagle optimization algorithm is easy to fall into local optimum, the invention provides an improvement thought: changing the learning object in the exploration stage, and fully embodying the intelligent predation behavior of Harris hawk by utilizing a multidimensional learning mechanism.
In the exploration stage, the learning object in the original algorithm is an eagle in an eagle group, the improved idea is to utilize a multi-dimensional learning mechanism to update the position of the eagle in each dimension respectively, and to not learn other eagles blindly at random, but to determine the learning object according to the fitness function value, so that the intelligence of the eagle is reflected, the search efficiency is improved, the population diversity is increased, and the situation that the eagle is trapped in local optimum is avoided. The update method of the search stage is changed from equation (20) to equation (33).
Wherein,
indicating the position of the ith eagle in the d dimension at the t iteration, fi indicates the index of the number of the eagle,
representing the position in the d dimension of the fi eagle. For each dimension update, randomly selecting two eagle groups, calculating fitness value, and taking the dominant eagle as a studyFor the study, if the eagle is numbered as index, fi is index.
S53 unmanned aerial vehicle air combat maneuver decision imitating Harris eagle intelligent predation optimization
For the above problem, the position vector of the hawk is the mixing strategy in step four, the fitness function is the objective function in step four, and for the maximization problem, the reciprocal is converted into the minimization problem. It should be noted that the objective function in the fourth step is constrained, and the proposed optimization algorithm cannot be directly applied, so that the present invention adopts a skill process, the constraint requirement variable is between 0 and 1, and the sum is 1, then the position change of the eagle is set to be between 0 and 1, and the sum is ensured to be 1 by a normalization method, so as to solve the constrained optimization problem. The unmanned aerial vehicle air combat maneuver decision flow imitating Harris eagle intelligent predation optimization is shown in figure 2.
Step six: updating six-degree-of-freedom aircraft state
After the optimal maneuver is determined, the three-degree-of-freedom control instruction is input into the six-degree-of-freedom airplane controller, and then the three-degree-of-freedom control instruction is converted into six-degree-of-freedom airplane control quantity, so that the airplane state can be updated.
Example (b):
the effectiveness of the air combat maneuver decision method imitating the harris eagle intelligent predation optimization provided by the invention is verified through a specific example. In this example, two F16 airplane models were selected as both red and blue in the air battle. The simulation environment of the example is configured as an intel i9-9900K processor, 3.60Ghz dominant frequency, 32G memory, and software is MATLAB 2018a version.
The process block diagram of the unmanned aerial vehicle air combat maneuver decision method imitating Harris eagle intelligent predation optimization is shown in fig. 2, the result diagrams of the embodiment are shown in fig. 3a, b and 4, and the specific practical steps of the embodiment are as follows:
the method comprises the following steps: initializing red and blue two-party setting and air combat game parameters
Initial position of the red warplane [0, 3300](m), the flying speed is 152m/s, and the initial course angle is 15 degrees; initial position of Bluetooth warplane [25,1,3.3](km), flying speed 152m/s, initial heading angle 180 deg.. Wingspan of 10m for red and blue sides and length of 15 m for fuselagem, radar cross-sectional area 4.9m2Maximum flying speed 500m/s, maximum altitude limit 20km, minimum altitude limit 500 m. The range of a red square aircraft gun is 800m, the weight of a projectile is 106g, the caliber of the projectile is 20mm, the maximum found target distance is 100km, the search azimuth angle is 120 degrees, and the target finding probability is 0.85; the range of the blue-square gun is 800m, the weight of the projectile is 137g, the caliber of the projectile is 20mm, the maximum target finding distance is 74km, the searching azimuth angle is 120 degrees, and the target finding probability is 0.85. The simulation time length is 300s, the unit maneuvering time length is 2s, and the airplane sampling period is 10 ms.
Step two: design tactical planning maneuver instruction generator
Red and blue two-party normal overload mobile library [0.8,1,1.2,1.4 ]]Roll angle motor bank [ -45 °,0, 45 ° ]]Combined garage
And if the combined maneuvers of the red and the blue are 12, a game matrix L _ rb with dimension of m, n and 12 is obtained.
Step three: designing a scoring matrix of a red and blue game
Setting k to 1000, calculating the corresponding evaluation function value under each action according to the formula in the step three, and obtaining a game scoring matrix with dimension m to n to 12.
Step four: designing hybrid strategy maneuver decision objective function
And designing an air combat maneuver decision objective function based on the mixed strategy according to the method in the step four.
Step five: unmanned aerial vehicle air combat maneuver decision imitating Harris eagle intelligent predation optimization
Setting algorithm parameters: the total number N of the hawks is 20, the search space dimension is equal to the number of the motor actions in the action library, and the iteration number T is 100. And D, performing maneuver decision according to the process in the step five to obtain the optimal maneuver.
Step six: updating six-degree-of-freedom aircraft state
And 4, converting the maneuvering instruction selected in the step five into an attack angle instruction and a rolling angle instruction, and inputting the instructions into the six-degree-of-freedom airplane model to realize maneuvering control.
Claims (3)
1. An unmanned aerial vehicle air combat maneuver decision method simulating Harris eagle intelligent predation optimization is characterized in that: the method comprises the following steps:
the method comprises the following steps: building six-freedom-degree airplane model and controller
Adopting a six-degree-of-freedom nonlinear model for simulating a real airplane;
step two: design tactical planning maneuver instruction generator
S21 three-degree-of-freedom maneuvering instruction generator
The maneuvering instruction generator adopts a three-degree-of-freedom simplified airplane model, tangential overload, normal overload and speed roll angle are used as input of the maneuvering instruction generator, and flying speed, track inclination angle and course angle are used as output to realize control of the airplane motion track; the tangential overload is mainly used for adjusting the speed of the airplane, and the normal overload and the roll angle are mainly used for adjusting the pitch angle and the yaw angle of the airplane;
s22 tactical planning maneuvering library
The maneuvering action library is designed to take the normal overload and the speed roll angle as control instructions, and expected maneuvering actions can be generated through the combination of different normal overload and speed roll angles; all the actions in the action libraries of the red and blue parties are combined to form a game action matrix;
the flexibility of the action library in design is expandability, and under the condition of meeting the performance limit of the airplane, a user can automatically set the values of normal overload and speed and roll angle in the action library as required, and the values are spaced appropriately, so that strong maneuverability can be obtained;
step three: designing a scoring matrix of a red and blue game
S31 direct threat based situation assessment function design
Two components of the air war situation assessment function are defined: an angle threat index and a distance threat index, the overall situation assessment function being the product of the angle threat index and the distance threat index;
s32, calculating game score matrix
A game scoring matrix, namely, aiming at the state quantities of the red and blue parties in each step, corresponding to the game maneuvering matrix in the step two, and respectively calculating the situation evaluation function under each action of the two parties, namely forming the game scoring matrix;
in the invention, a red party is recorded as a party, a blue party is recorded as an enemy, and the larger the evaluation function value of the expected situation of the party is, the more beneficial the evaluation function value is, the opposite blue party is;
step four: designing hybrid strategy maneuver decision objective function
Describing the probability of selecting each maneuver by using a mixing strategy, wherein the mixing strategy is a vector with dimensionality being the number of the maneuvers, and the sum of the vectors is 1; the formula of the mixture of the ingredients marked with red is Pr ═ Pr1,Pr2,...,Prm]TThe mixed strategy of the blue square is Pb ═ Pb1,Pb2,...,Pbn]T；
The maneuvering decision process is divided into two steps: the first step is to predict the mixing strategy of the blue party, and the second step is to calculate the mixing strategy of the red party according to the mixing strategy of the blue party; the two parties of the red and the blue are in a zero-sum game state, and each party tries to maximize the benefit of the other party and minimize the benefit of the other party;
after the hybrid strategy of the maneuver decision is determined, how to select the final maneuver is also a problem, and the invention adopts a roulette mode to determine the final maneuver;
step five: optimization algorithm for designing intelligent predation imitating Harris hawk
S51 Harris eagle optimization algorithm
S52 Harris eagle intelligent predation optimization based on multi-dimensional learning
Aiming at the problem that the Harris eagle optimization algorithm is easy to fall into local optimum, the learning object in the exploration stage is changed, and the intelligent predation behavior of the Harris eagle is fully embodied by utilizing a multidimensional learning mechanism;
s53 unmanned aerial vehicle air combat maneuver decision imitating Harris eagle intelligent predation optimization
Step six: updating six-degree-of-freedom aircraft state
After the optimal maneuver is determined, the three-degree-of-freedom control instruction is input into the six-degree-of-freedom airplane controller, and then the three-degree-of-freedom control instruction is converted into six-degree-of-freedom airplane control quantity, so that the airplane state can be updated.
2. The unmanned aerial vehicle air combat maneuver decision method imitating harris eagle intelligent predation optimization according to claim 1, characterized in that: the specific process of the step four is as follows:
s41 target function of prediction blue square mixing strategy
Assuming that the blue party selects a mixing strategy Pb, the yield obtained by the red party selecting the ith motor action is shown as a formula (1); all the red parties are selected flexibly, and the red parties want to maximize the benefits of the red parties, as shown in a formula (2);
wherein s isijIs the element of the ith row and the jth column in the game scoring matrix; benefitriRepresenting the income obtained by the selection of the ith action by the red party under the condition of the blue party mixing strategy Pb; benefitrmaxRepresents the maximum gain obtained by the red under all choices, under the condition of the blue mixed strategy Pb;
from a bluesquare perspective, Benefit is then expectedrmaxThe smaller the better, the target function of the blue square is shown as a formula (3), and the constraint condition is shown as a formula (4);
in which Pb is*A blue-square optimal mixing strategy, namely a predicted blue-square mixing strategy;
s42, calculating an objective function of the red square mixing strategy
On the basis of the predicted blue square mixed strategy, the red square aims to find the optimal strategy Pr*The gain is maximized, the objective function is shown as formula (5), and the constraint condition is shown as formula (6);
wherein, Pr*And S is a game score matrix.
3. The unmanned aerial vehicle air combat maneuver decision method imitating harris eagle intelligent predation optimization according to claim 1, characterized in that: step S52, changing the learning object in the exploration phase, and fully embodying the intelligent predation behavior of Harris eagle by using a multidimensional learning mechanism, wherein the specific process is as follows:
respectively updating the position of the hawk in each dimension by utilizing a multi-dimension learning mechanism, and determining a learning object according to the fitness function value; the updating mode of the exploration phase is shown as a formula (20);
wherein,
indicating the position of the ith eagle in the d dimension at the t iteration, fi indicates the index of the number of the eagle,
representing the position of the fi eagle in the d dimension; for each dimension update, in eagle groupsAnd randomly selecting two of the eagles, calculating the fitness value, taking the dominant eagle as a learning object, recording the number of the eagle as index, and then, if fi is equal to index.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110995706.6A CN113741500B (en) | 2021-08-27 | 2021-08-27 | Unmanned aerial vehicle air combat maneuver decision-making method for intelligent predation optimization of simulated Harris eagle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110995706.6A CN113741500B (en) | 2021-08-27 | 2021-08-27 | Unmanned aerial vehicle air combat maneuver decision-making method for intelligent predation optimization of simulated Harris eagle |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113741500A true CN113741500A (en) | 2021-12-03 |
| CN113741500B CN113741500B (en) | 2024-03-29 |
Family
ID=78733446
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110995706.6A Active CN113741500B (en) | 2021-08-27 | 2021-08-27 | Unmanned aerial vehicle air combat maneuver decision-making method for intelligent predation optimization of simulated Harris eagle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113741500B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114444255A (en) * | 2021-12-13 | 2022-05-06 | 西北工业大学 | General calculation method for airplane air combat capability based on combat process |
| CN114489120A (en) * | 2021-12-31 | 2022-05-13 | 杭州电子科技大学 | Unmanned aerial vehicle deployment and tracking control method facing mobile network |
| CN115328197A (en) * | 2022-08-10 | 2022-11-11 | 北京航空航天大学 | Unmanned combat aircraft cluster attack method imitating intelligent Harris eagle hunting behaviors |
| CN116834037A (en) * | 2023-09-01 | 2023-10-03 | 广东技术师范大学 | Dynamic multi-objective optimization-based picking mechanical arm track planning method and device |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017068224A1 (en) * | 2015-10-23 | 2017-04-27 | Consejo Superior De Investigaciones Científicas | Biomimetic and zoosemiotic unmanned aircraft guided by automatic pilot for precision and/or pursuit flights |
| US20200031465A1 (en) * | 2018-07-26 | 2020-01-30 | California Institute Of Technology | Systems and Methods for Avian Flock Flight Path Modification using UAVs |
| CN112783209A (en) * | 2020-12-31 | 2021-05-11 | 北京航空航天大学 | Unmanned aerial vehicle cluster confrontation control method based on pigeon intelligent competition learning |
| CN113156985A (en) * | 2021-03-18 | 2021-07-23 | 南京航空航天大学 | Obstacle avoidance robust disturbance rejection flight control method of fixed-wing unmanned aerial vehicle based on preset performance |
| CN113190037A (en) * | 2021-04-08 | 2021-07-30 | 上海吞山智能科技有限公司 | Unmanned aerial vehicle optimal path searching method based on improved fluid disturbance and sparrow algorithm |
-
2021
- 2021-08-27 CN CN202110995706.6A patent/CN113741500B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2017068224A1 (en) * | 2015-10-23 | 2017-04-27 | Consejo Superior De Investigaciones Científicas | Biomimetic and zoosemiotic unmanned aircraft guided by automatic pilot for precision and/or pursuit flights |
| US20200031465A1 (en) * | 2018-07-26 | 2020-01-30 | California Institute Of Technology | Systems and Methods for Avian Flock Flight Path Modification using UAVs |
| CN112783209A (en) * | 2020-12-31 | 2021-05-11 | 北京航空航天大学 | Unmanned aerial vehicle cluster confrontation control method based on pigeon intelligent competition learning |
| CN113156985A (en) * | 2021-03-18 | 2021-07-23 | 南京航空航天大学 | Obstacle avoidance robust disturbance rejection flight control method of fixed-wing unmanned aerial vehicle based on preset performance |
| CN113190037A (en) * | 2021-04-08 | 2021-07-30 | 上海吞山智能科技有限公司 | Unmanned aerial vehicle optimal path searching method based on improved fluid disturbance and sparrow algorithm |
Non-Patent Citations (2)
| Title |
|---|
| 李世豪;丁勇;高振龙;: "基于直觉模糊博弈的无人机空战机动决策", ***工程与电子技术, no. 05 * |
| 王宏;李建华;: "无人机集群作战指挥决策博弈分析", 军事运筹与***工程, no. 02 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114444255A (en) * | 2021-12-13 | 2022-05-06 | 西北工业大学 | General calculation method for airplane air combat capability based on combat process |
| CN114444255B (en) * | 2021-12-13 | 2023-10-03 | 西北工业大学 | General calculation method for aircraft air combat capability based on combat process |
| CN114489120A (en) * | 2021-12-31 | 2022-05-13 | 杭州电子科技大学 | Unmanned aerial vehicle deployment and tracking control method facing mobile network |
| CN114489120B (en) * | 2021-12-31 | 2024-05-14 | 杭州电子科技大学 | Unmanned aerial vehicle deployment and tracking control method for mobile network |
| CN115328197A (en) * | 2022-08-10 | 2022-11-11 | 北京航空航天大学 | Unmanned combat aircraft cluster attack method imitating intelligent Harris eagle hunting behaviors |
| CN116834037A (en) * | 2023-09-01 | 2023-10-03 | 广东技术师范大学 | Dynamic multi-objective optimization-based picking mechanical arm track planning method and device |
| CN116834037B (en) * | 2023-09-01 | 2023-10-31 | 广东技术师范大学 | Dynamic multi-objective optimization-based picking mechanical arm track planning method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113741500B (en) | 2024-03-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113741500B (en) | Unmanned aerial vehicle air combat maneuver decision-making method for intelligent predation optimization of simulated Harris eagle | |
| CN107390706B (en) | Unmanned aerial vehicle near combat decision method based on rehearsal maneuver rule system | |
| Shin et al. | An autonomous aerial combat framework for two-on-two engagements based on basic fighter maneuvers | |
| CN111240353B (en) | Unmanned aerial vehicle collaborative air combat decision method based on genetic fuzzy tree | |
| CN113625740B (en) | Unmanned aerial vehicle air combat game method based on transfer learning pigeon swarm optimization | |
| Yang et al. | Evasive maneuver strategy for UCAV in beyond-visual-range air combat based on hierarchical multi-objective evolutionary algorithm | |
| Burgin et al. | Rule-based air combat simulation | |
| CN114063644B (en) | Unmanned fighter plane air combat autonomous decision-making method based on pigeon flock reverse countermeasure learning | |
| CN113050686A (en) | Combat strategy optimization method and system based on deep reinforcement learning | |
| Li et al. | Autonomous maneuver decision-making for a UCAV in short-range aerial combat based on an MS-DDQN algorithm | |
| CN113282061A (en) | Unmanned aerial vehicle air game countermeasure solving method based on course learning | |
| CN113625569B (en) | Small unmanned aerial vehicle prevention and control decision method and system based on hybrid decision model | |
| CN115903865A (en) | Aircraft near-distance air combat maneuver decision implementation method | |
| CN111773722B (en) | Method for generating maneuver strategy set for avoiding fighter plane in simulation environment | |
| CN113671825A (en) | Maneuvering intelligent decision missile avoidance method based on reinforcement learning | |
| Duan et al. | Autonomous maneuver decision for unmanned aerial vehicle via improved pigeon-inspired optimization | |
| GOODRICH et al. | Development of a tactical guidance research and evaluation system (TGRES) | |
| CN115859778A (en) | Air combat maneuver decision method based on DCL-GWOO algorithm | |
| Smith et al. | Discovering novel fighter combat maneuvers: simulating test pilot creativity | |
| CN116011315A (en) | Missile escape area fast calculation method based on K-sparse self-coding SVM | |
| Qian et al. | H3E: Learning air combat with a three-level hierarchical framework embedding expert knowledge | |
| Zhu et al. | Mastering air combat game with deep reinforcement learning | |
| GOODRICH et al. | An integrated environment for tactical guidance research and evaluation | |
| Zhang et al. | Intelligent Close Air Combat Design based on MA-POCA Algorithm | |
| Hu et al. | Research on application of lstm-qdn in intelligent air combat simulation |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |