CN113283164B - Aircraft powerless section performance optimization method based on genetic algorithm - Google Patents
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Abstract
The invention discloses a method for optimizing the performance of an aircraft unpowered section based on a genetic algorithm, which comprises the following steps: s1, establishing a dynamic equation of a non-power section of the aircraft; s2, traversing a whole-course constant attack angle sequence through simulation based on a kinetic equation to obtain an optimal constant attack angle value of the aircraft; s3, acquiring a plurality of chromosomes based on a whole-course constant attack angle sequence to complete random initialization of a population; s4, selecting, crossing and mutating the initialized population as a parent population to obtain a child population so as to obtain an optimal solution, continuously and iteratively updating the parent population and the optimal solution record, and obtaining an approximately optimal solution after iteration is finished; and S5, based on the approximate optimal solution, carrying out local iterative optimization on the tail segment of the attack angle sequence to obtain a more optimal solution, and comparing the more optimal solution with the optimal constant attack angle. The method obviously improves the maximum range of the unpowered section of the aircraft, and an intelligent algorithm represented by a genetic algorithm has obvious advantages in solving the complex nonlinear engineering problem.
Description
Technical Field
The invention relates to the field of optimization of nonlinear problems of aircrafts, in particular to a method for optimizing the performance of an aircraft powerless section based on a genetic algorithm.
Background
The relevant theoretical technology of the aircraft flight without the power section is a research hotspot in the world at present, has important significance in researching the flight track characteristic, and can provide reference for tasks such as track forecasting, planning and guidance system design. The aircraft flies in a powerless section, namely glidingly flies, and has strong maneuvering penetration capability, however, whether the range of the aircraft is influenced by energy loss in the gliding process is also a remarkable problem, and meanwhile, the problem that the range is farthest under the control mode (the control mode is attack angle value in the flying process) is also involved, and the problem can be regarded as a complex nonlinear optimization problem.
The research of the nonlinear optimization problem is gradually deepened from the first univariate to the multivariable, small-scale to large-scale and unconstrained to constrained, and the nonlinear optimization algorithm is continuously improved, and the nonlinear optimization problem is formed by the traditional simple method, the interior point method, the Lagrangian method, the conjugate gradient method, the steepest descent method, the Newton method, the feasible direction method, the function approximation method and the trust domain method, and more intelligent optimization methods such as a genetic algorithm, a particle swarm algorithm, a simulated annealing algorithm, a tabu search algorithm, a neural network algorithm and the like are used in recent years.
For the prior art, the solving space of the complex engineering optimization problem is huge, the traditional optimization algorithm cannot solve within acceptable time, and the intelligent optimization algorithm is an algorithm which has global optimization performance, strong universality and is suitable for parallel processing. Among numerous intelligent algorithms, the genetic algorithm is simple in mechanism, easy to implement, and has potential parallelism and global property, and the improved genetic algorithm is particularly suitable for solving the complex nonlinear optimization problem. Therefore, the invention provides a method for optimizing the performance of the unpowered section of the aircraft based on the genetic algorithm, so as to overcome the defects in the prior art.
Disclosure of Invention
Aiming at the problems, the invention provides a method for optimizing the performance of the unpowered section of the aircraft based on the genetic algorithm, which aims to solve the technical problems in the prior art, can carry out local optimization on the end section of an attack angle sequence through the genetic algorithm, finally ensures that a more optimal solution with acceptable precision can be found within an acceptable time, and obviously improves the maximum range of the unpowered section of the aircraft.
In order to achieve the purpose, the invention provides the following scheme: the invention provides an aircraft powerless section performance optimization method based on a genetic algorithm, which comprises the following steps:
s1, establishing a dynamic equation of a non-power section of an aircraft;
s2, traversing a whole-course constant attack angle sequence through simulation circulation based on the kinetic equation to obtain an optimal constant attack angle value of the aircraft;
s3, acquiring a plurality of chromosomes based on the whole-course constant attack angle sequence to finish random initialization of a population;
s4, selecting, crossing and mutating the initialized population as a parent population to obtain a child population, obtaining an optimal solution based on the child population, continuously and iteratively updating the parent population and the optimal solution record, and obtaining an approximately optimal solution after iteration is finished;
and S5, based on the approximate optimal solution, carrying out local iterative optimization on the last section of the attack angle sequence to obtain a more optimal solution, and comparing the more optimal solution with the optimal constant attack angle, wherein the more optimal solution is the optimal constant attack angle of the unpowered section of the aircraft.
Preferably, the kinetic equation of S1 in the speed coordinate system is:
wherein, ax、ayAcceleration in the x and y directions under the speed system; x, Y are aerodynamic forces in the x and y directions of the aircraft system respectively; alpha, theta and eta are respectively an attack angle of the aircraft, a speed angle of the aircraft and a centroid-geocentric angle of the aircraft; ag is the magnitude of the acceleration of gravity.
Preferably, the specific process simulated in S2 is: and selecting integer values of the attack angles in a preset angle range to form the whole-course constant attack angle sequence, inputting the corresponding attack angles into a simulation platform, obtaining corresponding voyages under each attack angle, and further obtaining the optimal constant attack angle value.
Preferably, the chromosome is generated by a 6-segment value method.
Preferably, the step of S3 randomly initializing the population includes:
s3.1, generating a decimal chromosome;
constructing 6 random numbers within a first preset angle range through a random function, wherein each number represents an attack angle value within different preset time ranges, and generating a decimal chromosome;
s3.2, generating a binary chromosome;
respectively converting 6 random numbers generated in the decimal chromosome into 5-bit binary numbers, and splicing the binary numbers together in sequence to generate a binary chromosome, wherein each binary bit in the binary chromosome represents a gene;
s3.3, calculating the fitness and the priority probability;
inputting the binary attack angle sequence of each binary chromosome into the simulation platform, solving a corresponding voyage, calculating the fitness of the binary chromosomes, and calculating the priority probability of each chromosome based on the fitness;
the fitness calculation formula of the binary chromosome is as follows:
wherein, distance is a flight, and ideal _ distance is a constant;
s3.4, generating an initialization population;
repeating said S3.1-S3.3 and grouping said generated binary chromosomes into said initialisation population.
Preferably, the specific step of obtaining the approximate optimal solution of S4 is:
s4.1, selecting roulette;
selecting one individual from the parent population as a crossed parent individual by adopting a 'roulette' mode as a selection mode based on the priority probability, and then selecting another different individual from the parent population as a crossed mother individual by utilizing the 'roulette';
s4.2, exchange and crossing;
the specific crossing process of the binary chromosomes is as follows:
randomly generating two Cross points Cross1, Cross2, Cross1< Cross2 in the length range of a binary chromosome, exchanging gene loci in the father individual and the mother individual, wherein the gene loci are located between Cross1 and Cross2 to obtain 2 offspring individuals, repeating S4.2, and forming the offspring population by the generated offspring individuals;
s4.3, probability mutation;
randomly generating a random number within a second preset angle range, setting a variation probability threshold, if the random number is smaller than the threshold, performing variation, repeating the step S4.3, and performing probability variation on the filial generation population to generate a new filial generation population;
s4.4, updating the parent population;
and updating the parent population by adopting the new child population, recalculating and evaluating the best individual in the updated parent population, updating the best solution record if the best individual of the updated parent population is superior to the recorded best solution, setting an iteration threshold, and repeating S4.1-S4.4 until the iteration threshold is reached to obtain the approximate best solution.
Preferably, the specific process of the probability variation of S4.3 is: randomly generating 2 variation points in a third preset angle range, and taking a plurality of numerical values in each binary chromosome as a gene segment, wherein the probability variation process is 2 variation points for exchanging 6 gene segments.
Preferably, the local iterative optimization of S5 employs a genetic algorithm.
Preferably, the specific process of the local iterative optimization is as follows: and reserving a plurality of gene segments which are close to the optimal solution, and reconstructing the attack angle sequence to obtain a more optimal solution.
The invention discloses the following technical effects:
based on a classical intelligent optimization algorithm, namely a genetic algorithm, chromosome coding is carried out on an attack angle sequence of a powerless section of an aircraft, and then a population is continuously updated iteratively through roulette selection, crossover selection, probability selection and the like, so that inferior solutions are eliminated, the optimal solutions are approached, and after the iteration is finished, an approximate optimal solution is obtained; in order to further obtain a better solution, based on the approximate optimal solution, reducing the time interval, and then, using the genetic algorithm again to perform local optimization on the end of the attack angle sequence, thereby finally ensuring that the better solution with acceptable precision can be found within the acceptable time; by comparing with the optimal constant attack angle and the genetic algorithm without local optimization, the method obviously improves the maximum range of the aircraft without power section; meanwhile, the intelligent optimization algorithm represented by the genetic algorithm has remarkable advantages in solving the complex nonlinear engineering problem.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a chart of the farthest range at different constant angles of attack for comparison according to the present invention;
FIG. 3(a) is a ballistic comparison;
FIG. 3(b) is a height comparison graph;
FIG. 3(c) is a velocity contrast plot;
fig. 4 is an iterative comparison of the present invention with an optimal constant angle of attack.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
Referring to fig. 1 to 4, the present embodiment provides a method for optimizing the performance of an aircraft unpowered segment based on a genetic algorithm, including the following steps:
s1, establishing a dynamic equation of a non-power section of an aircraft;
s2, traversing a whole-course constant attack angle sequence through simulation based on the kinetic equation to obtain an optimal constant attack angle value of the aircraft;
s3, acquiring a plurality of chromosomes based on the whole-course constant attack angle sequence to complete random initialization of a population;
s4, selecting, crossing and mutating the initialized population as a parent population to obtain a child population, obtaining an optimal solution based on the child population, continuously and iteratively updating the parent population and the optimal solution record, and obtaining an approximately optimal solution after iteration is finished; the approximate optimal solution is a solution obtained by solving an optimization problem by using an approximate algorithm and finally converging the algorithm, and the genetic algorithm adopted by the method is a typical approximate algorithm, so the solution is the approximate optimal solution;
and S5, based on the approximate optimal solution, carrying out local iterative optimization on the tail section of the attack angle sequence to obtain a more optimal solution, and comparing the more optimal solution with the optimal constant attack angle value.
In a further optimization scheme, the kinetic equation of the S1 in the speed coordinate system is:
wherein, ax、ayAcceleration in the x and y directions under the speed system; x, Y are aerodynamic forces in the x and y directions of the aircraft body respectively; alpha, theta and eta are respectively an attack angle of the aircraft, a speed angle of the aircraft and a mass center-geocentric angle of the aircraft; the Ag is the acceleration of gravity.
In a further optimization scheme, the specific process of the simulation in S2 is as follows: and selecting integer values of the attack angle between 0 and 30 (degrees) in the whole-course constant attack angle sequence, inputting the corresponding attack angle into a simulation platform to obtain the corresponding maximum range index under each attack angle, drawing a maximum range diagram in the attack angle-x axis direction as shown in fig. 2, and further obtaining the optimal constant attack angle value, wherein when the attack angle is 11 degrees, the range is the maximum and is 778790.0 m.
In a further optimization scheme, the step S3 of randomly initializing the population includes:
s3.1, generating a decimal chromosome;
constructing 6 random numbers in the range of 0-30 through a random function, wherein the 1 st number represents the value of an attack angle in 0-100 s, the 2 nd number represents the value of the attack angle in 100-200 s, and so on;
s3.2, generating a binary chromosome;
respectively converting the 6 random numbers generated in the S3.1 into 5-bit binary numbers, splicing the binary numbers together in sequence, and connecting the binary numbers into a complete chromosome, wherein each binary position represents a gene;
s3.3, calculating the fitness and the priority probability;
and inputting the binary attack angle sequence of each binary chromosome into the simulation platform, solving a corresponding voyage, calculating the fitness of the binary chromosomes, and calculating the priority probability of each chromosome based on the fitness.
The fitness calculation formula of the binary chromosome is as follows:
wherein, distance is a voyage, and ideal _ distance is a constant;
s3.4, generating an initialization population;
repeating said S3.1-S3.3 and composing said generated binary chromosomes into said initialisation population.
Preferably, the specific acquiring step of S4 approximate to the optimal solution is:
s4.1, selecting roulette;
selecting one individual from the parent population as a crossed parent individual by adopting a 'roulette' mode as a selection mode based on the priority probability, and then selecting another different individual from the parent population as a crossed mother individual by utilizing the 'roulette';
s4.2, exchange and crossing;
binary chromosomes are 6 × 5 — 30 in length, so the specific crossover process for binary chromosomes is:
randomly generating two Cross points Cross1 and Cross2(Cross1< Cross2) between 1 to 31, exchanging the genetic loci from Cross1 to Cross2 of the parent individual selected in the S4.1 to obtain 2 offspring individuals, and repeating the process to generate 8 offspring individuals from the same parent individual and mother individual to form the offspring population;
s4.3, probability variation;
randomly generating a random number between 0 and 1, setting a variation probability threshold, if the random number is smaller than the threshold, performing variation, repeating the process, and performing probability variation on the offspring population obtained by the S4.2 crossing;
s4.4, updating the parent population;
and updating and iterating the parent population by using the child population obtained in the step S4.1, the step S4.2 and the step S4.3, recalculating the priority probability of each individual of the population, reevaluating the optimal individual of the population, updating the optimal solution record if the updated optimal individual of the parent population is superior to the recorded optimal solution, setting an iteration time threshold, and repeating the iteration process of the step S4.1, the step S4.2, the step S4.3 and the step S4.4 until the threshold is reached to obtain the approximate optimal solution.
In a further optimization scheme, the specific process of the probability variation of S4.3 is: 2 variation points l1 and l2 are randomly generated between 1 and 5 (l1< l2), each 5-digit number of each binary chromosome is taken as a gene segment (the attack angle corresponding to the decimal number of each gene segment is an attack angle input value with the interval of 100 s), and the probability variation process is the value of 2 variation points l1 and l2 for exchanging 6 gene segments.
In a further optimization scheme, the local iterative optimization of S5 employs a genetic algorithm.
In a further optimization scheme, the specific process of local iterative optimization is as follows: the first 5 segments of the approximate optimal solution, that is, the attack angle values of 500S in total, are kept unchanged, the last 120S of attack angle sequences are encoded in 6 segments and 20S time intervals, then population initialization, crossing, variation, selection and updating processes corresponding to the S4 are performed, finally a better solution is obtained, a trajectory, height and speed map of the better solution is output, and comparison is performed with the output of the optimal constant attack angle, and the result is shown in fig. 3-4:
fig. 3 is a comparison graph of the optimization result of the present invention and the optimal constant angle of attack, which can clearly show that the present invention significantly improves the maximum flight path of the aircraft without power section, and the iterative comparison graph of the present invention and the optimal constant angle of attack of fig. 4 shows that the intelligent algorithm represented by the genetic algorithm has significant advantages in solving the complex nonlinear engineering problem. Wherein, fig. 3(a) is a comparison graph of the optimization result of the present invention and the motion trajectory of the optimal constant attack angle, the graph shows that the performance of the genetic algorithm with local optimization is obviously superior to the optimal constant attack angle, table 1 also lists the optimal constant attack angle, the optimal solution of the genetic algorithm, the maximum flight path of the optimal solution of the genetic algorithm with local optimization, and the flight time index, and it can be known from table 1 that the optimization effect of the genetic algorithm with local optimization is obviously superior to that of the genetic algorithm.
TABLE 1
The comparison graph of the optimal solution of the genetic algorithm with local optimization and the altitude and speed change of the optimal constant attack angle is shown in the graph in FIG. 3(b) and FIG. 3 (c); the invention considers the time effectiveness of the optimizing algorithm besides the accuracy effectiveness.
The invention discloses the following technical effects:
based on a classical intelligent optimization algorithm, namely a genetic algorithm, chromosome coding is carried out on an attack angle sequence of a powerless section of an aircraft, and then a population is continuously updated iteratively through roulette selection, crossover selection, probability selection and the like, so that inferior solutions are eliminated, the optimal solutions are approached, and after the iteration is finished, an approximate optimal solution is obtained; in order to further obtain a better solution, based on the approximate optimal solution, reducing the time interval, and then, using the genetic algorithm again to perform local optimization on the end of the attack angle sequence, thereby finally ensuring that the better solution with acceptable precision can be found within the acceptable time; by comparing with the optimal constant attack angle and the genetic algorithm without local optimization, the method obviously improves the maximum range of the unpowered section of the aircraft; meanwhile, the intelligent optimization algorithm represented by the genetic algorithm has remarkable advantages in solving the complex nonlinear engineering problem.
The above-described embodiments are only intended to describe the preferred embodiments of the present invention, and not to limit the scope of the present invention, and various modifications and improvements made to the technical solution of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.
Claims (6)
1. A method for optimizing the performance of an aircraft unpowered section based on a genetic algorithm is characterized by comprising the following steps:
s1, establishing a dynamic equation of a non-power section of an aircraft;
s2, traversing a whole-course constant attack angle sequence through simulation circulation based on the kinetic equation to obtain an optimal constant attack angle value of the aircraft;
s3, acquiring a plurality of chromosomes based on the whole-course constant attack angle sequence to complete random initialization of a population;
s4, selecting, crossing and mutating the initialized population as a parent population to obtain a child population, obtaining an optimal solution based on the child population, continuously and iteratively updating the parent population and the optimal solution record, and obtaining an approximately optimal solution after iteration is finished;
s5, based on the approximate optimal solution, carrying out local iterative optimization on the last section of the attack angle sequence to obtain a more optimal solution, and comparing the more optimal solution with the optimal constant attack angle, wherein the more optimal solution is the optimal constant attack angle of the unpowered section of the aircraft;
generating chromosomes by adopting a 6-segment value-taking method;
the specific steps of S3 randomly initializing the population are:
s3.1, generating a decimal chromosome;
constructing 6 random numbers within a first preset angle range through a random function, wherein each number represents an attack angle value within different preset time ranges, and generating a decimal chromosome;
s3.2, generating a binary chromosome;
respectively converting 6 random numbers generated in the decimal chromosome into 5-bit binary numbers, and splicing the binary numbers together in sequence to generate a binary chromosome, wherein each binary bit in the binary chromosome represents a gene;
s3.3, calculating the fitness and the priority probability;
inputting the binary attack angle sequence of each binary chromosome into a simulation platform, solving a corresponding voyage, calculating the fitness of the binary chromosomes, and calculating the priority probability of each chromosome based on the fitness; the fitness calculation formula of the binary chromosome is as follows:
wherein, distance is a voyage, and ideal _ distance is a constant;
s3.4, generating an initialization population;
repeating said S3.1-S3.3 and grouping said generated binary chromosomes into said initialisation population;
the specific acquisition step of the approximate optimal solution of S4 is as follows:
s4.1, selecting roulette;
selecting one individual from the parent population as a parent individual for the cross based on the priority probability by adopting a 'roulette' mode as a selection mode, and selecting another different individual from the parent population as a mother individual for the cross by utilizing the 'roulette';
s4.2, exchange and crossing;
the specific crossing process of the binary chromosomes is as follows:
randomly generating two Cross points Cross1, Cross2, Cross1< Cross2 in the length range of a binary chromosome, exchanging gene loci in the father individual and the mother individual, wherein the gene loci are located between Cross1 and Cross2 to obtain 2 offspring individuals, repeating S4.2, and forming the offspring population by the generated offspring individuals;
s4.3, probability variation;
randomly generating random numbers in a second preset angle range, setting a variation probability threshold, if the random numbers are smaller than the variation probability threshold, performing variation, repeating the step S4.3, and performing probability variation on the filial generation population to generate a new filial generation population;
s4.4, updating the parent population;
and updating the parent population by adopting the new child population, recalculating and evaluating the best individual in the updated parent population, updating the best solution record if the best individual of the updated parent population is superior to the recorded best solution, setting an iteration threshold, and repeating S4.1-S4.4 until the iteration threshold is reached to obtain the approximate best solution.
2. The method for optimizing the performance of the unpowered section of the aircraft based on the genetic algorithm as recited in claim 1,
the kinetic equation of the S1 in the speed coordinate system is as follows:
wherein, ax、ayAcceleration in the x and y directions in the velocity system; x, Y are aerodynamic forces in the x and y directions of the aircraft system respectively; alpha, theta and eta are respectively an attack angle of the aircraft, a speed angle of the aircraft and a centroid-geocentric angle of the aircraft; g is the gravity acceleration.
3. The method for optimizing the performance of the unpowered section of the aircraft based on the genetic algorithm as recited in claim 1, wherein the specific process simulated in S2 is as follows: and selecting integer values of the attack angles in a preset angle range to form the whole-process constant attack angle sequence, inputting the corresponding attack angles into a simulation platform, obtaining corresponding voyages under all the attack angles, and further obtaining the optimal constant attack angle value.
4. The method for optimizing the performance of the unpowered section of the aircraft based on the genetic algorithm as recited in claim 1, wherein the specific process of the probability variation of S4.3 is as follows: randomly generating 2 variation points in a third preset angle range, and taking a plurality of numerical values in each binary chromosome as a gene segment, wherein the probability variation process is 2 variation points for exchanging 6 gene segments.
5. The method for optimizing the performance of the unpowered section of the aircraft based on the genetic algorithm as recited in claim 1, wherein the local iterative optimization of the step S5 adopts the genetic algorithm.
6. The method for optimizing the performance of the unpowered section of the aircraft based on the genetic algorithm as recited in claim 5, wherein the specific process of the local iterative optimization is as follows: and reserving a plurality of gene segments which are close to the optimal solution, and reconstructing the attack angle sequence to obtain the better solution.
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