CN112836426A - Aero-engine maximum thrust control optimization method based on improved genetic algorithm - Google Patents
Aero-engine maximum thrust control optimization method based on improved genetic algorithm Download PDFInfo
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Abstract
The invention provides an aircraft engine maximum thrust control optimization method based on an improved genetic algorithm, which is characterized in that the genetic algorithm is improved according to the characteristics of an aircraft engine, the aspects of the initial population structure, the fitness function, the crossover operator, the mutation operator and the like of the genetic algorithm are mainly improved, the improved genetic algorithm can exert the advantages of the genetic algorithm, the defects of the genetic algorithm are avoided, the convergence speed is accelerated, and the quality of a search result is improved. And (3) applying the improved genetic algorithm to maximum thrust control optimization, and outputting an optimal control variable to the aero-engine. The invention can furthest promote the thrust of the engine and improve the maneuverability of the airplane on the premise of ensuring the safe operation of the engine.
Description
Technical Field
The invention relates to the technical field of aero-engine control, in particular to an aero-engine maximum thrust control optimization method based on an improved genetic algorithm.
Background
The aircraft engine is the heart of an aircraft and is one of important indexes for measuring the development level of a national aviation industry, so that the research on the reinforced power system has important significance for improving the integral level of the national aviation technology. Because the working process of the aero-engine is complex and changeable, and the aero-engine has the structural characteristics of strong nonlinearity, multiple control variables, time variation and complexity, the research on the engine control problem is more difficult than that of a common control system.
The characteristics of the current aeroengine control develop towards refinement, modularization and integration, and the current engine control is not simple integration based on a control module, but emphasizes optimization and promotion of the structure and function of a control system. One of the main ways to improve engine performance is engine performance optimization control. The engine performance optimizing control means that the performance of the existing or novel engine is optimized within a bearable range of control hardware on the premise of safe operation of the engine in order to optimize the performance index of the engine and further excavate the performance potential of the engine. Therefore, the key for improving the overall performance level of the aeroengine in China and mastering the world advanced aeroengine control technology lies in the research of an advanced engine performance optimization control mode and a control method.
Meanwhile, the air control right plays a vital role in modern war, and the key of war victory or defeat is held by mastering the air control right. With the rapid development of science and technology, modern air combat brings higher requirements on fighters, and the requirements are mainly embodied in the aspects of wider flight envelope, enlarged combat radius, improved maneuverability and flexibility, increased thrust-weight ratio, reduced oil consumption, short-distance starting, improved reliability and operability and the like. The purpose of the maximum thrust control mode of the engine is to improve the thrust of the engine as much as possible and improve the maneuverability and flexibility of the airplane on the premise of ensuring the safe operation of the engine.
Although the research of the maximum thrust optimizing control of the engine at home and abroad achieves certain results, a plurality of unsolved technical problems or points to be improved exist. The difficulty is to find an optimization algorithm which not only has stronger global convergence capability, but also can converge quickly. For example, the genetic algorithm has the disadvantages of large calculation amount, long time consumption, easy precocity and the like, and is not suitable for being applied to the performance optimization of a complex aircraft engine.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an aircraft engine maximum thrust control optimization method based on an improved genetic algorithm, which is used for improving the genetic algorithm and applying the improved genetic algorithm to an engine maximum thrust optimization control mode, so that the thrust of an engine is improved as much as possible on the premise of ensuring the safe operation of the engine, and the maneuverability and the flexibility of an aircraft are improved.
The technical scheme of the invention is as follows:
firstly, a nonlinear mathematical model of the aircraft engine is established, and then the maximum thrust optimizing control of the engine is carried out by improving a genetic algorithm, so that the thrust of the engine is improved as much as possible on the premise of ensuring the safe operation of the engine, and the maneuverability and the flexibility of the airplane are improved.
The aircraft engine maximum thrust control optimization method based on the improved genetic algorithm is characterized by comprising the following steps: firstly, establishing a nonlinear mathematical model of an aeroengine; secondly, determining an objective function and a constraint function of a maximum thrust control mode; the third step is to optimize calculation by improving a genetic algorithm; and fourthly, outputting the optimal control variable to the aircraft engine.
The aircraft engine maximum thrust control optimization method based on the improved genetic algorithm is characterized by comprising the following steps: the improved genetic algorithm is an improvement on a basic genetic algorithm, and mainly improves the aspects of the initial population structure, the fitness function, the crossover operator, the mutation operator and the like of the genetic algorithm.
The nonlinear mathematical model of the aircraft engine is
y＝f(x)
Wherein
For controlling input vector, including regulating main fuel flow WfArea A of the tail nozzle9Fan guide vane angle dvgl and compressor guide vane angle dvgh,
to output a vector, comprising the specific fuel consumption sfc and the engine thrust F, F (-) is a non-linear vector function that produces the system output.
The maximum thrust control mode is to ensure that the thrust of the engine is unchanged and the fuel consumption of the engine is reduced on the premise of ensuring the safe operation of the engine, and the mathematical description is as follows:
performance indexes are as follows: MaxF
Constraint conditions are as follows: gimin≤gi(x)≤gimax,i＝1,2,...
Wherein, gi(x) The constraint conditions include that the temperature in front of the turbine is not over-heated, the high-pressure compressor is not surged, the high-pressure rotor is not over-rotated, the fan is not over-rotated, the combustion chamber is not rich in oil and is extinguished, the oil supply of the main combustion chamber is not more than the maximum oil supply, the throat area of the nozzle is not less than the minimum area, and the like, gimin，gimaxThe lower limit value and the upper limit value of the constraint condition are respectively.
The following nonlinear constraint problem needs to be solved for the maximum thrust control mode:
wherein the control variable x ═ Wf,A9,dvgl,dvgh]TThe above variables are all initial values within the corresponding variation range.
The algorithm flow of the improved genetic algorithm is
(1) And (5) initializing. And assigning values to the parameters, and randomly generating an initial population containing M individuals.
(2) And (5) evaluating the fitness. Calculating the objective function value of each individual in the group
The value of the fitness function can be calculated, and the individuals are sorted from large to small according to the fitness value.
(3) And (4) selecting. And (3) adopting an optimal storage strategy in the selection operator, namely, replacing the individuals with the lowest fitness after operations such as crossing, mutation and the like in the group by the individuals with the highest fitness in the current group without participating in crossing operation and mutation operation.
(4) And (4) crossing. Using improved crossover operators, i.e. by
Crossover operations can be performed to generate "offspring" of superior individuals, which are used to replace the removed individuals.
(5) And (5) carrying out mutation. Using modified operators, i.e. from Ω ═ xk-s(t)(xk-Lk),xk+s(t)(Uk-xk) Mutation can be performed to generate new individuals.
(6) And calculating the fitness value of each individual in the filial generation population.
(7) If the algorithm has reached the allowed maximum evolution algebra or the optimal individuals of the continuous generations of groups have not evolved, outputting the final result and ending the iteration; otherwise, turning to (3) and continuing searching.
Further, the control variable is the adjustment of the main fuel flow WfArea A of the tail nozzle9Fan guide vane angle dvgl and compressor guide vane angle dvgh.
Advantageous effects
Compared with the prior art, the aero-engine maximum thrust control optimization method based on the improved genetic algorithm improves the genetic algorithm, applies the improved genetic algorithm to the engine maximum thrust mode optimization control, improves the thrust of the engine as much as possible on the premise of ensuring the safe work of the engine, and improves the maneuverability and the flexibility of an airplane.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of the engine maximum thrust optimization control of the present invention;
FIG. 2 is a schematic diagram of a maximum thrust control mode of the present invention;
FIG. 3 is a flow chart of the random direction method operation of the present invention;
FIG. 4 is a flow chart of the basic operation of the improved genetic algorithm of the present invention.
Detailed Description
The invention solves the problem of optimizing and controlling the maximum thrust performance of an aircraft engine. The optimization problem of the engine is to select an optimal control method to search a group of optimal control quantity (main fuel flow W) in order to optimize one or more combined indexes of the performance of the enginefArea A of the tail nozzle9Fan guide vane angle dvgl, compressor guide vane angle dvgh).
A nonlinear mathematical model of a certain type of aviation turbofan engine is taken as a research object, an objective function of a maximum thrust control mode is established, and the engine is optimized and calculated by using an optimization algorithm, so that the optimal control variable meeting the maximum thrust performance index can be obtained. The maximum thrust control mode refers to maximally improving the thrust of the engine on the premise of ensuring the safe work of the engine, and is usually used for climbing, accelerating flight and sudden impact of the airplane.
The control algorithm is a calculation method adopted by a control system to realize a control rule or a control mode and meet the system performance requirement. Many scholars have studied the application of linear and nonlinear optimization algorithms in optimization control, and the main research algorithms are: linear programming, genetic algorithms, model-assisted pattern search methods, genetic algorithms, and the like. On the basis of summarizing previous achievements, the genetic algorithm is improved according to the characteristics of the aero-engine, and the method is applied to the engine maximum thrust optimizing control.
1. Because the optimization control of the maximum thrust of the aero-engine needs to make a control decision according to the current working state parameters of the engine, when an optimal control method is researched, the true engine is usually replaced by an aero-engine mathematical model. As the modeling technology of the aeroengine is mature, the detailed description is omitted, and the established nonlinear model of the engine is directly provided
y＝f(x)
Wherein
For controlling input vector, including regulating main fuel flow WfArea A of the tail nozzle9Fan guide vane angle dvgl and compressor guide vane angle dvgh,
to output a vector, comprising the specific fuel consumption sfc and the engine thrust F, F (-) is a non-linear vector function that produces the system output.
2. Design of improved genetic algorithms
The aircraft engine performance optimizing technology is a key technology for comprehensive control of a flight/propulsion system. With the increase of aviation technology investment, the full-authority digital electronic control technology is widely applied to a new generation of engines. In order to optimize the maximum thrust of the engine, the maximum thrust optimizing control is generally adopted in the maximum thrust state of the engine. The genetic algorithm has the defects of large calculation amount, long time consumption, easy precocity and the like, and is not suitable for being applied to the performance optimization of a complex aero-engine. Therefore, the invention designs an improved genetic algorithm to carry out maximum thrust optimizing control on the aircraft engine, and the basic idea is shown in figure 1.
The remaining thrust is obtained by subtracting the flight resistance from the engine thrust. When the engine is in a working state of taking off, landing and re-flying, and the like, in order to shorten the climbing and accelerating flight time of the airplane and enable the airplane to obtain operational advantages, the airplane needs to obtain the largest possible residual thrust, and the engine at the moment must generate the largest possible thrust. Therefore, the maximum remaining thrust control mode is also referred to as the maximum thrust control mode. The control targets for the maximum thrust mode are: on the premise of ensuring the safe work of the engine, the thrust of the engine is improved to the maximum extent. The premise for safe engine operation is that the maximum thrust control mode is limited by the maximum turbine inlet temperature, the maximum converted air flow, the maximum converted fan speed, and the engine surge.
By increasing engine air flow WaAnd increasing the engine pressure ratio picIs the main way to achieve the maximum thrust control mode. Pi of maximum thrust control modecAnd WaThe relationship of (a) is shown in FIG. 2. In the maximum thrust control mode, mainly by increasing the main fuel flow WfWhile reducing the area A of the exhaust nozzle9Increase the pressure ratio of the enginecIncreasing the fan guide vane angle dvgl and the compressor guide vane angle dvgh can increase the engine converted air flow rate, thereby improving the thrust. Main fuel flow WfThis increase in pressure increases the high and low pressure turbine inlet temperatures and causes the high and low pressure rotational speeds to increase. Therefore, the increase of the thrust must ensure that the fan surge margin SMF and the compressor surge margin SMC are larger than the minimum allowable surge margin, and the total inlet temperature of the high-low pressure turbine is lower than the maximum limit temperature of the high-low pressure turbine, and the limit of the maximum rotating speed of the high-low pressure rotor of the engine is met. Figure 2 shows the seek from operating point a on the common operating line to the optimum operating point b, after the seek the pressure ratio increases, the thrust increases and the minimum surge margin limit or the maximum converted flow, speed or temperature limit boundary is reached.
The mathematical description of the maximum thrust control mode, taking into account the constraints, is as follows:
performance indexes are as follows: MaxF
Constraint conditions are as follows: gimin≤gi(x)≤gimax,i＝1,2,...
Wherein, gi(x) As a constraint, gimin，gimaxThe lower limit value and the upper limit value of the constraint condition are respectively.
The basic genetic algorithm is not described in detail in the invention. In order to fully exert the advantages of the genetic algorithm, avoid the disadvantages, accelerate the convergence speed and improve the quality of the search result. According to the characteristics of an engine model, the method improves the genetic algorithm, mainly improves the aspects of the initial population structure, the fitness function, the crossover operator, the mutation operator and the like of the genetic algorithm, and applies the improved genetic algorithm to the maximum thrust optimization control of the aero-engine.
Since the initial population is the starting point for genetic algorithm search optimization, the construction of the initial population is related to the execution efficiency of the genetic algorithm. The diversity of the initial population needs to be ensured in the construction process of the initial population, and the genetic algorithm is prevented from falling into local convergence. Since the population is composed of individuals, if the resulting individuals can spread over the entire search space, the diversity of the initial population can be guaranteed to some extent. The invention adopts a random direction method to construct the initial population, and the method can randomly generate individuals in the initial population in the search space and the individuals can spread over the whole search space.
Selection of initial point:
initial point x of random direction method0Must be a feasible point, namely satisfy gj(x) A point of 0(j ═ 1,2, …, m). The calculation steps are as follows:
(1) lower and upper limits of the input variables, i.e.
ai≤xi≤bi(i＝1,2,…,n)
(2) Generating n pseudo-random numbers q in the interval (0,1)i(i＝1,2,…,n)
(3) Computing the components of the random point x
xi＝ai+qi(bi-ai)(i＝1,2,…,n)
(4) And judging whether the random point x is feasible or not. If x is a feasible point, removing the initial point x0Axle 300,; if x is the infeasible point, go to step (2) until x is generated as the feasible point.
Generation of feasible search directions:
the method of generating feasible search directions is to choose a better direction among the k random directions. The calculation steps are as follows:
(1) generating pseudo-random numbers r in the (-1,1) intervali j(i 1,2, …, n; j 1,2, …, k) and the random unit vector e is obtained by the following equationj
(2) Taking an experimental step length alpha0K random points are calculated as follows
xj＝x0+α0ej(j＝1,2,…,k)
Obviously, k random points are distributed at the initial point x0Centered on α0Is a radius hypersphere.
(3) Examine k random points xj(j ═ 1,2, …, k) is a feasible point, the feasible points are removed, the objective function values of the remaining feasible random points are calculated, the values are compared, and the point x with the minimum objective function value is selectedL。
(4) Comparison xLAnd x0Two points of the objective function value, if f (x)L)＜f(x0) Then take xLAnd x0The connecting line direction of (1) is taken as a feasible searching direction; if f (x)L)≥f(x0) Then the step size alpha is adjusted0Reducing, turning to the step (1) until f (x)L)＜f(x0) Until now. If the size is reduced to be very small, still no x can be foundLLet f (x)L)＜f(x0) Then x is illustrated0Is a local minimum point, and the initial point can be replaced, and the step (1) is carried out.
In summary, when x isLPoint satisfies
The feasible search direction d is
d＝xL-x0
Determination of search step size:
after the feasible searching direction d is determined, the initial point is moved to xLI.e. x0←xLFrom x0Starting from the point, a search is made in the direction d, and the step size α used is generally determined by the acceleration step size method. The acceleration step method means that the step length of successive iteration is determined according to a certain valueThe method of increasing the ratio of (a). The step size for each iteration is calculated as:
α＝τα
wherein tau is step length acceleration coefficient, and tau can be 1.3;
α -step size, the initial step size being α ═ α0。
The steps of the random direction method are shown in fig. 3.
Different encoding modes may have a large influence on the optimization quality and the optimization efficiency of the algorithm. The invention adopts a decimal floating point number coding method. Because some performance parameters of the engine, such as thrust, have a large variation range, and the aeroengine optimization control problem involves more decision variables, and the precision requirement on part of the decision variables is high, the decimal floating point number coding is very suitable. In addition, the invention researches an online performance optimizing control technology of the aero-engine, has higher requirement on the calculation efficiency of the adopted optimization algorithm, and can improve the calculation efficiency by adopting the coding method.
The design of the fitness function is directly related to the selection operation in the genetic algorithm, so that the selection of the fitness function is crucial, and the selection not only directly influences the convergence speed of the genetic algorithm, but also is closely related to the iteration stop condition of the genetic algorithm and the constraint condition of the problem. In general, the fitness function is transformed from an objective function. The commonly used fitness function scale transformation method comprises the following steps: linear transformation, power function transformation, and exponential transformation.
The invention makes some improvements to the fitness function, and the operation is shown as the following formula:
wherein
In the formula, T is the maximum algebra of evolution, T is the current iteration algebra, b is a parameter, the value of the method is 3, and r belongs to [0,1 ]. As can be seen from the above formula, the improved fitness function is non-negative, and in the evolution process, the number of local optimal points is gradually reduced, and the probability of converging on the global optimal solution is increased. Meanwhile, as T is closer to T, the closer s (T) is to 0, the more the replication enforcement tends to the individual with larger fitness value.
The genetic operators mainly comprise a selection operator, a crossover operator and a mutation operator. The invention mainly improves the crossover operator and the mutation operator. The above three operators will be studied separately below.
The selection operator selects excellent individuals from the existing population to reserve, and eliminates the inferior individuals. The selection operator reflects the survival ability of the seeds, and the selection operator in the genetic algorithm adopts which selection strategy to select individuals with equal population scale from the parent population to form a next generation population, which has the greatest influence on the performance of the algorithm.
The problem solved by the selection operator is: a selection rule is established, and a plurality of individuals are selected from the previous generation group and are inherited to the next generation group. In order to reserve the individuals with the best fitness to the next generation group as much as possible, the invention adopts the optimal storage strategy to carry out the operation of winning or losing the best, namely, the individuals with the highest fitness in the current group do not participate in the cross operation and the mutation operation, but replace the individuals with the lowest fitness generated after the operations of cross operation, mutation and the like in the group of the generation by the individuals.
The specific operation process of the optimal storage strategy is as follows:
finding out the individuals with the highest fitness and the individuals with the lowest fitness in the current group.
Secondly, if the fitness of the best individual in the current group is higher than the total fitness of the best individuals so far, the best individual in the current group is taken as a new best individual so far.
③ replacing the worst individual in the current group with the best individual so far.
The purpose of crossover operations in genetic algorithms is to create new individuals. The basic process of the crossover operation is: the paired chromosomes exchange part of their genes in some way, thereby forming a new individual. When designing a cross-operation method for a specific problem, the following principles should be followed: a new pattern with better properties is efficiently generated without destroying the patterns with superior properties in the individual code strings too much.
The invention provides a new crossover operator on the basis of arithmetic crossover, the operator does not depend on the selected fitness function, the defect that the offspring individuals are limited between two parent individuals is overcome, the diversity of a group gene library is maintained, and the mutation operator is played. The method comprises the following specific steps:
let father generation be:
two children are first generated:
wherein
If it is not
Out of bounds, then the child is generated with the following arithmetic intersection:
when γ is 0, it is arithmetic crossing. It is demonstrated below that when γ < α or γ < β, the offspring obtained after crossing will not be limited to between the two parents: without being provided with
Then
It can be seen that when γ < α or γ < β, the offspring individuals obtained after crossing will not be limited to the distance determined by the two parents. If it is
The conclusion can be equally proven. By adopting the improved crossover operator provided by the invention, the search space is expanded, the problem of premature can be effectively prevented, and the convergence speed is accelerated. Different from a common crossover operator, the method adopts the competition of the parent generation and the offspring generation, selects the optimal individual and the suboptimal individual to enter the next generation, thus leading the group to be close to the region with high fitness value and accelerating the convergence speed.
Mutation operations in genetic algorithms refer to the replacement of gene values at certain loci in an individual's chromosomal code string with other alleles of that locus to form a new individual. The mutation operation determines the local search capability of the genetic algorithm. The mutation operator has two functions, and the main function is to enhance the population diversity to jump out a local minimum point, namely a global search function; the other function is to disturb the seeds so as to generate a proper optimized direction, and the essence is to enhance the directional diversity of the seed crossing, namely, the auxiliary crossing search function. Commonly used mutation operators include basic bit mutation, uniform mutation, non-uniform mutation, boundary mutation, Gaussian mutation, and the like.
The invention is improved on the basis of common mutation operators, the improved mutation operators not only have the advantages of the original operators, but also are simpler and more convenient to operate than the original operators, and the convergence rate of the genetic algorithm is effectively accelerated, and the method specifically comprises the following steps:
let the parent chromosome be x ═ x1,x2,…,xk,…,xn]Element xk∈[Lk,Uk]Is a variant element, variant element ykRandomly generated in the interval Ω:
Ω＝{xk-s(t)(xk-Lk),xk+s(t)(Uk-xk)}
wherein
In the formula, T is the maximum algebra of evolution, T is the current iteration algebra, b is a parameter, the value of the method is 3, and r belongs to [0,1 ]. When T is small, s (T) is approximately equal to 1, the variation space is relatively large, and when T is large, s (T) is approximately equal to 0, the variation space is small, and the searching speed is accelerated.
The operation parameters to be selected in the genetic algorithm mainly comprise the length l of an individual coding string, the size M of a population and the selection probability prProbability of variation pmThe termination algebra T, etc. These parameters have a large influence on the operation performance of the genetic algorithm and need to be carefully selected. The group size M is selected to be 20-100, and the selection probability prSelecting the mutation probability p to be 0.5-0.85mThe selection is 0.0001-0.1, and the termination algebra T is 100-1000. The length l of the code string is selected in relation to the coding method. The invention adopts floating-point number coding, and the length l of a coding string is equal to the number n of decision variables. For the mutation probability pmWhen p ismWhen the value is large, the better mode may be destroyed; when p ismIf the value is too small, the ability of the mutation operation to generate new individuals and the ability to inhibit premature phenomena will be poor.
The basic operational flow of the improved genetic algorithm is shown in fig. 4.
(1) And (5) initializing. And assigning values to the parameters, and randomly generating an initial population containing M individuals.
(2) And (5) evaluating the fitness. Calculating the objective function value of each individual in the group
The value of the fitness function can be calculated, and the individuals are sorted from large to small according to the fitness value.
(3) And (4) selecting. And (3) adopting an optimal storage strategy in the selection operator, namely, replacing the individuals with the lowest fitness after operations such as crossing, mutation and the like in the group by the individuals with the highest fitness in the current group without participating in crossing operation and mutation operation.
(4) And (4) crossing. Using improved crossover operators, i.e. by
Crossover operations can be performed to generate "offspring" of superior individuals, which are used to replace the removed individuals.
(5) And (5) carrying out mutation. Using modified operators, i.e. from Ω ═ xk-s(t)(xk-Lk),xk+s(t)(Uk-xk) Mutation can be performed to generate new individuals.
(6) And calculating the fitness value of each individual in the filial generation population.
(7) If the algorithm has reached the allowed maximum evolution algebra or the optimal individuals of the continuous generations of groups have not evolved, outputting the final result and ending the iteration; otherwise, turning to (3) and continuing searching.
3. Maximum thrust optimization control based on improved genetic algorithm
The maximum thrust control mode of the engine is to furthest improve the thrust of the engine on the premise of ensuring the safe work of the engine. The invention selects the main combustionFlow rate W of chamber fuelfNozzle area A of the tail nozzle9The fan guide vane angle dvgl and the compressor guide vane angle dvgh are used as control variables.
In the maximum thrust control mode, the optimization objective is as follows:
max F
in order to guarantee the optimality, stability and structural strength of the engine operating conditions, specific limitations must be imposed on the use of the engine. All these limitations can be divided into two categories, due to limitations imposed by flight conditions, mechanical loads, thermal loads and aerodynamic loads: one is the limitation of the aerodynamic stability condition in the working process of the power device components, and is related to some engine components such as a gas compressor, a combustion chamber and the like; the second type is intensity limitation. The necessary strength margin should be maintained under all conditions of use of the engine. For a steady operating state of the engine, the rotational speed limit value which has the greatest influence on the turbine blade strength margin is limited. Within a given flight envelope, the engine pressure and temperature must be limited for structural or aerodynamic considerations. Under normal operating conditions, over-temperature and over-rotation are limited.
In summary, the constraint conditions of the engine selected by the invention are as follows: the temperature in front of the turbine is not over-heated, the high-pressure compressor is not surging, the high-pressure rotor is not over-rotated, the fan is not over-rotated, the combustion chamber is not rich in oil and is flameout, the oil supply of the main combustion chamber is not more than the maximum oil supply, the throat area of the nozzle is not less than the minimum area, and the like.
Considering the influence of the objective function, constraint conditions and control variables, a suitable set of W needs to be foundf，A9Dvgl, dvgh, which makes the engine work at the lowest fuel consumption point, namely, the following nonlinear constraint problem needs to be solved:
wherein the control variable x ═ Wf,A9,dvgl,dvgh]TThe above variables are all initial values within the corresponding variation range.
Under the maximum thrust mode, on the premise of ensuring the safe working of the engine, the thrust of the engine is improved to the maximum extent. This goal can be described by the following mathematical expression:
max F
this objective function can be converted to the following form:
in the above formula, KfIs a positive constant.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.
Claims (5)
1. An aircraft engine maximum thrust control optimization method based on an improved genetic algorithm is characterized by comprising the following steps: firstly, establishing a nonlinear mathematical model of an aeroengine;
secondly, determining an objective function and a constraint function of a maximum thrust control mode;
the third step is to optimize calculation by improving a genetic algorithm;
and fourthly, outputting the optimal control variable to the aircraft engine.
The aircraft engine maximum thrust control optimization method based on the improved genetic algorithm is characterized by comprising the following steps: the improved genetic algorithm is an improvement on a basic genetic algorithm, and mainly improves the aspects of the initial population structure, the fitness function, the crossover operator, the mutation operator and the like of the genetic algorithm.
2. The aircraft engine maximum thrust control optimization method based on the improved genetic algorithm is characterized in that: the nonlinear mathematical model of the aircraft engine is
y＝f(x)
Wherein
For controlling input vector, including regulating main fuel flow WfArea A of the tail nozzle9Fan guide vane angle dvgl and compressor guide vane angle dvgh,
to output a vector, comprising the specific fuel consumption sfc and the engine thrust F, F (-) is a non-linear vector function that produces the system output.
3. The aircraft engine maximum thrust control optimization method based on the improved genetic algorithm is characterized in that: the maximum thrust control mode is to improve the thrust of the engine as much as possible on the premise of ensuring the safe operation of the engine, and the mathematical description is as follows:
performance indexes are as follows: max F
Constraint conditions are as follows: gimin≤gi(x)≤gimax,i＝1,2,...
Wherein, gi(x) The constraint conditions include that the temperature in front of the turbine is not over-heated, the high-pressure compressor is not surged, the high-pressure rotor is not over-rotated, the fan is not over-rotated, the combustion chamber is not rich in oil and is extinguished, the oil supply of the main combustion chamber is not more than the maximum oil supply, the throat area of the nozzle is not less than the minimum area, and the like, gimin，gimaxThe lower limit value and the upper limit value of the constraint condition are respectively.
The following nonlinear constraint problem needs to be solved for the maximum thrust control mode:
wherein the control variable x ═ Wf,A9,dvgl,dvgh]TThe above variables are all initial values within the corresponding variation range.
4. The aircraft engine maximum thrust control optimization method based on the improved genetic algorithm is characterized in that: the algorithm flow of the improved genetic algorithm is
(1) And (5) initializing. And assigning values to the parameters, and randomly generating an initial population containing M individuals.
(2) And (5) evaluating the fitness. Calculating the objective function value of each individual in the group
The value of the fitness function can be calculated, and the individuals are sorted from large to small according to the fitness value.
(3) And (4) selecting. And (3) adopting an optimal storage strategy in the selection operator, namely, replacing the individuals with the lowest fitness after operations such as crossing, mutation and the like in the group by the individuals with the highest fitness in the current group without participating in crossing operation and mutation operation.
(4) And (4) crossing. Using improved crossover operators, i.e. by
Crossover operations can be performed to generate "offspring" of superior individuals, which are used to replace the removed individuals.
(5) And (5) carrying out mutation. Using modified operators, i.e. from Ω ═ xk-s(t)(xk-Lk),xk+s(t)(Uk-xk) Mutation can be performed to generate new individuals.
(6) And calculating the fitness value of each individual in the filial generation population.
(7) If the algorithm has reached the allowed maximum evolution algebra or the optimal individuals of the continuous generations of groups have not evolved, outputting the final result and ending the iteration; otherwise, turning to (3) and continuing searching.
5. The method of claim 1An aircraft engine maximum thrust control optimization method based on an improved genetic algorithm is characterized in that: the control variable being the regulation of the main fuel flow WfArea A of the tail nozzle9Fan guide vane angle dvgl and compressor guide vane angle dvgh.
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