CN112949161A - IGA-based engine minimum oil consumption control optimization method under gas circuit component fault - Google Patents
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Abstract
The invention provides an aero-engine minimum oil consumption control optimization method based on an improved genetic algorithm and considering gas circuit component faults. The improved genetic algorithm is used for optimizing the minimum oil consumption performance. And the engine model used for optimizing the minimum fuel consumption performance is a non-linear airborne engine model considering the faults of the air path components of the engine. The invention can still perform excellent control on the real engine under the condition of the failure of the engine air path component, can realize that the thrust of the engine is kept unchanged, reduce the oil consumption rate and improve the flight distance and the safety of the airplane under the condition of the failure of the engine air path component.
Description
Technical Field
The invention relates to the technical field of aero-engine control, in particular to an IGA-based engine minimum oil consumption control optimization method under a gas circuit component fault.
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 lowest oil consumption control mode of the engine is to ensure that the thrust of the engine is unchanged, reduce the oil consumption rate of the engine and improve the combat radius of the airplane on the premise of ensuring the safe operation of the engine.
Although the research of the minimum oil consumption 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.
Moreover, the requirements of modern warplanes on the performance of aircraft engines are continuously increased, the structures of the modern warplanes are more and more complex, and the engine faults account for 1/3 total faults of the aircraft due to the severe and variable working environments of the engines. Wherein, the gas circuit part failure accounts for more than 90% of the total failure of the engine, and the maintenance cost accounts for 60% of the total maintenance cost of the engine. In order to ensure the safe operation of the engine and to make the failed engine provide sufficient performance to ensure the safe flight of the aircraft or have high maneuverability, the performance of the failed engine must be recovered, and the fault-tolerant control of the engine is performed to ensure the normal and stable operation of the control system and good performance. Therefore, the research on the fault tolerance control method of the gas circuit component of the engine is of great significance.
According to the traditional fault-tolerant control method for the gas circuit component, when the gas circuit component of the aeroengine fails, the control rule is corrected, so that the thrust of the engine is always matched with the throttle lever, and the thrust of the engine is effectively guaranteed. However, these design methods do not address the issue of current controller and engine model mismatches that result in degraded or even unstable control system performance. When the engine has a gas path component fault, the linear model of the engine at the same working point is also changed greatly. Therefore, a controller designed according to an engine model in a normal state generally cannot guarantee the performance of the engine when a gas path component fails, or even cannot guarantee the closed loop stability of a control system.
In conclusion, the research on the minimum oil consumption performance optimizing control of the engine in the fault state of the gas circuit component has important significance.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides an IGA-based engine minimum oil consumption control optimization method under the failure of a gas circuit component, a genetic algorithm is improved, the improved genetic algorithm is applied to an engine minimum oil consumption optimization control mode, and under the condition of the failure of the gas circuit component of an engine, the thrust of the engine is ensured to be unchanged, the oil consumption rate of the engine is reduced, and the safety and the flight distance of an airplane are improved on the premise of ensuring the safe operation of the engine.
The technical scheme of the invention is as follows:
firstly, a gas path component fault diagnosis module of the aircraft engine is established, the gas path component fault diagnosis module comprises a nonlinear airborne engine model and a piecewise linearization Kalman filter, and then the nonlinear airborne engine model in the gas path component fault diagnosis module is combined to improve a genetic algorithm to carry out the minimum oil consumption optimization control of the engine so as to realize the minimum oil consumption rate of a certain type of aircraft turbofan engine under the condition of keeping the thrust unchanged.
The method for controlling and optimizing the lowest oil consumption of the engine based on IGA under the fault of the gas path component is characterized by comprising the following steps of: firstly, establishing a gas path component fault diagnosis module of an aeroengine, which comprises a nonlinear airborne engine model and a piecewise linearization Kalman filter; secondly, determining a target function and a constraint function of a lowest oil consumption control mode; thirdly, performing quadratic programming optimization calculation by using the improved sequence; and fourthly, outputting the optimal control variable to the aircraft engine.
The method for controlling and optimizing the lowest oil consumption of the engine based on IGA under the fault of the gas path component is characterized by comprising the following steps of: 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 gas circuit component fault diagnosis module comprises a nonlinear onboard engine model and a piecewise linearization Kalman filter;
the nonlinear airborne engine model is an engine nonlinear model with health parameters:
y＝g(x,u,h)
wherein
For controlling input vector, including regulating main fuel flowQuantity WfArea A of the tail nozzle9Fan guide vane angle dvgl and compressor guide vane angle dvgh,
in the form of a state vector, the state vector,
for output vectors including fuel consumption sfc and engine thrust F and other engine measurable outputs including inlet outlet, fan outlet, compressor outlet, temperature and pressure after high pressure turbine and low pressure turbine, fan speed and compressor speed,
for the health parameter vector, f (-) is an n-dimensional differentiable nonlinear vector function representing the system dynamics, and g (-) is an m-dimensional differentiable nonlinear vector function producing the system output; the nonlinear onboard engine model is input into a control input vector u and a health parameter h of the previous period, and the output health steady-state reference value (x) of the nonlinear onboard engine modelaug,NOBEM,yNOBEM) The method comprises the steps of taking the current period as an estimated initial value of a piecewise linearization Kalman filter;
the inputs of the piecewise linearization Kalman filter are a measurement parameter y and a healthy steady-state reference value (x) output by a nonlinear airborne engine modelaug,NOBEM,yNOBEM) According to the formula
Calculating to obtain a health parameter h of the engine in the current period; wherein
K is the gain of Kalman filtering
P is the Ricini equation
The solution of (1); coefficient AaugAnd CaugAccording to the formula
Determining, and A, C, L, M is an augmented linear state variable model reflecting engine performance degradation obtained by regarding the health parameter h as the control input of the engine and linearizing the nonlinear on-board engine model at a healthy steady-state reference point
Coefficient (c):
w is the system noise, v is the measurement noise, and the corresponding covariance matrices are the diagonal matrices Q and R.
The lowest oil consumption control mode is to ensure that the thrust of the engine is unchanged and the oil consumption rate 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:
constraint conditions are as follows: gimin≤gi(x)≤gimax,i＝1,2,...
Wherein, gi(x) The constraint conditions include no over-temperature of the turbine front temperature, no surge of the high-pressure compressor, no over-rotation of the high-pressure rotor, no over-rotation of the fan, no rich oil flameout of the combustion chamber, and no over-maximum fuel supply of the main combustion chamberOil supply, nozzle throat area not less than its minimum area, and the like, gimin，gimaxThe lower limit value and the upper limit value of the constraint condition are respectively.
Namely, the following nonlinear constraint problem needs to be solved for the lowest fuel consumption 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) CanPerforming mutation operation 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 method for controlling and optimizing the lowest oil consumption of the engine based on the IGA under the fault of the gas circuit component improves the genetic algorithm, applies the improved genetic algorithm to the optimization control of the lowest oil consumption mode of the engine, ensures that the engine still works safely when the gas circuit component of the engine has the fault, keeps the thrust of the engine unchanged, reduces the oil consumption rate, and improves the flight distance and the safety 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 schematic structural diagram of a fault diagnosis module of a gas path component in the embodiment;
fig. 2 is a schematic structural diagram of a kalman filter in the fault diagnosis module of the gas path component in the present embodiment;
FIG. 3 is a flow chart of the fuel consumption minimization optimizing control of the engine of the present invention;
FIG. 4 is a schematic diagram of the lowest fuel consumption control mode of the present invention;
FIG. 5 is a flow chart of the random direction method operation of the present invention;
FIG. 6 is a flow chart of the basic operation of the improved genetic algorithm of the present invention.
Detailed Description
The performance of gas circuit components can be degraded due to factors such as natural wear, corrosion, scale deposit, thermal creep and the like in the operation process of the aero-engine, and faults can be caused when the performance is degraded to a certain degree; in addition, the gas path member may also be damaged by foreign matter inhalation, mechanical fatigue fracture, or the like. The former failure occurs slowly, while the latter failure occurs rapidly. When the air path component of the engine fails and does not fail, part of the performance of the engine at the moment can seriously deviate from the rated state. Taking a turbine part as an example, when the turbine part fails, the working efficiency of the turbine part will be reduced, that is, the capability of converting the fuel gas with high temperature and high pressure into mechanical energy will be reduced, and corresponding power can be provided for a fan or a compressor part to enable the turbine part to work in a new balance state. At this time, the engine also deviates greatly from the original state. The failure of the gas circuit component can cause that a nonlinear model established during the design of the engine is seriously mismatched with a real engine during the failure of the gas circuit component, so that a gain scheduling controller designed according to the nonlinear model can not well control the engine with the failed gas circuit component, the performance of the engine is seriously reduced, the stability of a control system can not be even ensured, and the safe operation of the engine can not be ensured.
The invention solves the problem of lowest oil consumption optimizing control of an aircraft engine considering the faults of gas path components. The optimal control of the lowest oil consumption 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 keep the thrust of the engine unchanged and reduce the oil consumption rate to the lowestfArea A of the tail nozzle9Fan guide vane angle dvgl, compressor guide vane angle dvgh).
A nonlinear airborne engine model of a certain type of aviation turbofan engine is taken as a research object, a target function of a minimum oil consumption control mode is established, and optimization calculation is carried out on the engine by utilizing an optimization algorithm, so that an optimal control variable meeting minimum oil consumption performance indexes can be obtained. The lowest oil consumption control mode is to reduce the oil consumption rate of the engine on the premise that the engine is safe and the thrust is unchanged, and the mode is usually used in a cruising state, so that the cruising time and the combat radius can be increased.
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 optimization control of the lowest oil consumption of the engine.
1. Engine gas path component fault diagnosis
The failure of the gas path component can cause the corresponding characteristic parameter of the component to change. The engine gas circuit component faults are finally characterized on the changes of the working efficiency and the flow rate of different rotor components, namely the engine fault position and the fault degree can be revealed from the changes of the efficiency coefficients or the flow rate coefficients of the wind fan, the compressor, the main combustion, the high-pressure turbine and the low-pressure turbine components, and the efficiency coefficients or the flow rate coefficients of the fan, the compressor, the main combustion chamber, the high-pressure turbine and the low-pressure turbine components are called as health parameters.
Establishing a non-linear airborne engine model with health parameters based on a component method
y＝g(x,u,h)
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,
in the form of a state vector, the state vector,
for output vectors including fuel consumption sfc and engine thrust F and other engine measurable outputs including inlet outlet, fan outlet, compressor outlet, temperature and pressure after high pressure turbine and low pressure turbine, fan speed and compressor speed,
for the health parameter vector, f (-) is an n-dimensional differentiable nonlinear vector function representing the system dynamics, and g (-) is an m-dimensional differentiable nonlinear vector function producing the system output.
And (3) regarding the health parameter h as the control input of the engine, and linearizing the nonlinear model of the engine at a healthy steady-state reference point by adopting a small perturbation method or a fitting method.
Wherein
A′＝A,B′＝(B L),C′＝C,
D′＝(D M),Δu′＝(ΔuΔh)T
w is system noise, v is measurement noise, h is a health parameter, Δ h ═ h-h0(ii) a W and v are uncorrelated white gaussian noise, the mean value is 0, and the covariance matrix is diagonal matrices Q and R, which satisfies the following conditions:
E(w)＝0 E[wwT]＝Q
E(v)＝0 E[vvT]＝R
Δ represents the amount of change of the parameter, h0Representing an engine initial state health parameter.
Further obtains an augmented linear state variable model reflecting the performance degradation of the engine
Wherein the coefficient matrix is obtained by:
these coefficients have different values at different operating states of the engine.
In fact, the health parameters are difficult or even impossible to measure, and the pressure, temperature, speed, etc. of each part of the engine are easy to obtain by measurement, and are generally called "measurement parameters", mainly including the temperature and pressure at the outlet of the air inlet, the outlet of the fan, the outlet of the compressor, the temperature and pressure after the high-pressure turbine and the low-pressure turbine, the speed of the fan and the speed of the compressor. When the working environment of the engine does not change, the change of the health parameter can cause the corresponding change of the measured parameter, and an aerodynamic thermodynamic relation exists between the health parameter and the measured parameter. Thus, an optimal estimation filter can be designed to achieve optimal estimation of the health parameter by measuring the parameter.
For a graded component failure, the corresponding failed component health parameter changes slowly, so over the time period in which a single failure diagnosis is performed, it can be considered that the requirements are met
For the mutant component failure, the severity of the component failure is more concerned when the engine works stably again after the failure occurs, and the health parameter change of the failed component is still satisfied after the engine works stably again
Further converting the health parameters into state variables to obtain
Wherein
As shown in fig. 1, the established gas path component fault diagnosis module mainly comprises two parts, one part is a non-linear airborne engine model based on health parameters, and the other part is a piecewise linear kalman filter. The basic working principle is that the output of the nonlinear airborne engine model is used as a steady-state reference value of the piecewise linear Kalman filter, health parameters are expanded, online real-time estimation is carried out through the piecewise linear Kalman filter, and finally the online real-time update is fed back to the nonlinear airborne engine model, so that the real-time tracking of an actual engine is realized.
As shown in fig. 2, the kalman estimation equation is:
k is the gain of Kalman filtering
P is the Ricini equation
The solution of (1); healthy steady-state reference value (x) output by using nonlinear airborne modelaug,NOBEM,yNOBEM) As formula
The initial value of (a) can be obtained by the following calculation formula:
the health parameter h of the engine can be obtained according to the calculation formula, and the fault diagnosis of the gas circuit component of the engine is realized.
2. Design of improved genetic algorithms
The lowest oil consumption technology of the aircraft engine is a key technology for the 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 fuel consumption performance of the aircraft and the engine, a minimum fuel consumption control mode is generally adopted in the cruising 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 for carrying out minimum oil consumption optimizing control on the aircraft engine, and the basic idea is shown in figure 3.
The lowest oil consumption control mode is used for the cruise state, and the cruise time and the combat radius can be increased.
At high rotor speed n without applying forceHUnder the condition of constant, in order to realize the lowest fuel consumption control mode, the main fuel flow W needs to be adjustedfArea A of the tail nozzle9Fan guide vane angle dvgl and compressor guide vane angle dvgh. For the small bypass ratio turbofan engine, the area A of the tail nozzle is increased9Pressure ratio of enginecWill increase and simultaneously decrease the main fuel flow WfAdjusting the guide vane angle dvgl of the fan and the guide vane angle dvgh of the air compressor to reduce the rotating speed of the engine and reduce the air flow WaThe thrust can be kept basically unchanged by increasing. Engine pressure ratio pi of the control modecAnd WaThe relationship of (2) is shown in FIG. 4. The point a is the current working point, the optimizing path needs to be carried out along the equal thrust line, the oil consumption rate is the lowest point when reaching the point b, and the global optimal point of the lowest oil consumption control mode is not on the constraint boundary generally but the highest point of the global efficiency in the feasible region. Control module for minimum oil consumption under constant thrust conditionThe efficiency of the component can be improved, and the main fuel flow WfAnd thus the fuel consumption is reduced.
After considering the constraint conditions, the mathematical description of the lowest fuel consumption control mode is as follows:
performance indexes are as follows: min sfc
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 genetic algorithm is improved, the construction, the fitness function, the crossover operator, the mutation operator and other aspects of the initial population of the genetic algorithm are mainly improved, and the improved genetic algorithm is applied to the minimum oil consumption optimization control of the aircraft 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 small, one can not be foundxLLet 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 is a method in which the step sizes of successive iterations are increased in a certain proportion. 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. 5.
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. From the above, the invention providesThe improved crossover operator not only expands the search space, but also can effectively prevent the problem of premature and accelerate the convergence speed. 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. 6.
(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. Minimum oil consumption optimizing control based on improved genetic algorithm
The lowest fuel consumption mode is to reduce fuel consumption while keeping thrust constant, and this mode is used for the cruise state. A reduction in fuel consumption will increase the cruise time and the radius of engagement of the aircraft.
Fuel consumption sfc and fuel flow WfIn relation to the thrust force F, reducing the fuel consumption rate while maintaining the thrust force F constant is to reduce the fuel flow W as much as possiblefTo reduce the fuel flow WfThis results in a reduction of the thrust force F, which must be adjusted simultaneously to several other control variables in order to keep the thrust force F constant: area A of jet nozzle of tail jet pipe9Fan guide vane angle dvgl, compressor guide vane angle dvgh. Therefore, the invention selects the fuel oil flow W of the main combustion chamberfNozzle 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 lowest fuel consumption mode, the optimization objective is as follows:
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.
In the lowest fuel consumption mode, the engine thrust F remains constant. Minimizing sfc can be translated into engine main fuel flow WfAnd (4) minimizing. Thus, the optimization objective function can be converted into:
where JF represents an objective function that keeps engine thrust F constant, FdIndicating the desired thrust value at cruise conditions.
Thus, the objective function can be converted into:
in the above formula, ω1In order to be able to adjust the coefficients,
is designed to ensure at Wf，A9And when dvgl and dvgh are changed, the thrust of the engine is ensured to be changed within a small range of the required thrust.
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. The lowest oil consumption control optimization method of the engine based on IGA under the failure of the gas path component is characterized by comprising the following steps of: firstly, establishing a gas path component fault diagnosis module of an aeroengine, which comprises a nonlinear airborne engine model and a piecewise linearization Kalman filter;
secondly, determining a target function and a constraint function of a lowest oil consumption 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 method for controlling and optimizing the lowest oil consumption of the engine based on IGA under the fault of the gas path component is characterized by comprising the following steps of: 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 IGA-based engine minimum oil consumption control optimization method under the failure of the gas circuit component according to claim 1, characterized in that: the gas circuit component fault diagnosis module comprises a nonlinear onboard engine model and a piecewise linearization Kalman filter;
the nonlinear airborne engine model is an engine nonlinear model with health parameters:
y＝g(x,u,h)
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,
in the form of a state vector, the state vector,
for output vectors including fuel consumption sfc and engine thrust F and other engine measurable outputs including inlet outlet, fan outlet, compressor outlet, temperature and pressure after high pressure turbine and low pressure turbine, fan speed and compressor speed,
for the health parameter vector, f (-) is an n-dimensional differentiable nonlinear vector function representing the system dynamics, and g (-) is an m-dimensional differentiable nonlinear vector function producing the system output; the nonlinear onboard engine model is input into a control input vector u and a health parameter h of the previous period, and the output health steady-state reference value (x) of the nonlinear onboard engine modelaug,NOBEM,yNOBEM) The method comprises the steps of taking the current period as an estimated initial value of a piecewise linearization Kalman filter;
the inputs of the piecewise linearization Kalman filter are a measurement parameter y and a healthy steady-state reference value (x) output by a nonlinear airborne engine modelaug,NOBEM,yNOBEM) According to the formula
Calculating to obtain a health parameter h of the engine in the current period; wherein
K is the gain of Kalman filtering
P is the Ricini equation
The solution of (1); coefficient AaugAnd CaugAccording to the formula
Determining, A, C, L, M is an augmented linear state variable model reflecting engine gas path component faults, which is obtained by regarding the health parameter h as the control input of the engine and linearizing the nonlinear onboard engine model at a healthy steady-state reference point
Coefficient (c):
w is the system noise, v is the measurement noise, and the corresponding covariance matrices are the diagonal matrices Q and R.
3. The IGA-based engine minimum oil consumption control optimization method under the failure of the gas circuit component according to claim 1, characterized in that: the lowest oil consumption control mode is to ensure that the thrust of the engine is unchanged and the oil consumption rate 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:
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.
Namely, the following nonlinear constraint problem needs to be solved for the lowest fuel consumption 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 IGA-based engine minimum oil consumption control optimization method under the failure of the gas circuit component according to claim 1, 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 IGA-based engine minimum oil consumption control optimization method under the failure of the gas circuit component according to claim 1, characterized in that: the control variable being the regulation of the main fuel flow WfArea A of the tail nozzle9Wind and windFan vane angle dvgl and compressor vane angle dvgh.
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