CN109634107A - A kind of engine dynamic control law optimization method - Google Patents
A kind of engine dynamic control law optimization method Download PDFInfo
- Publication number
- CN109634107A CN109634107A CN201910055751.6A CN201910055751A CN109634107A CN 109634107 A CN109634107 A CN 109634107A CN 201910055751 A CN201910055751 A CN 201910055751A CN 109634107 A CN109634107 A CN 109634107A
- Authority
- CN
- China
- Prior art keywords
- control law
- sub
- optimization
- population
- individual
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0205—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system
- G05B13/024—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric not using a model or a simulator of the controlled system in which a parameter or coefficient is automatically adjusted to optimise the performance
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Combined Controls Of Internal Combustion Engines (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a kind of engine dynamic control law optimization methods, according to control law independent variable parameter that engine dynamic control law is discrete for several sub- control law points.Corresponding sub- population is established for every sub- control law point in addition to beginning and end to optimize, and elite individual of the optimum individual as the sub- population is chosen after the optimization of every step, freeze this sub- swarm optimization process and carry out next sub- swarm optimization, so point-by-point optimization of circulation is until Optimization Progress meets termination condition.Using engine dynamic control law optimization method of the invention, conventional dynamic control law design method can be overcome not to be suitable for the aero-engine dynamic control law design problem of advanced more tunable components, solve the problems, such as that conventional method is affected by human factors big, the reciprocal iterative process of needs and is unable to get optimal control law.
Description
Technical field
The present invention relates to aero-engine fields, in particular to a kind of engine dynamic control law optimization method.
Background technique
For aero-engine, is optimized for engine dynamic control law and engine final performance is showed
It is of great significance.And as engine variable component increases, control law has not been traditional simple fuel oil or revolving speed
At any time or the changing rule of other independents variable, and often multiple tunable components are adjusted jointly.Accelerate to control with engine
For system rule optimization, in engine accelerating course, not only need given fuel oil at any time or the change of revolving speed other independents variable
Law is also needed to parameters such as jet pipe minimum cross-sectional area, fan guider guide vane angle, nozzle ring critical cross-section areas
It is adjusted, that is, needs to give the changing rule of multiple control parameters simultaneously.Traditional engine control regularity design method, it is past
Toward being that engineering staff is designed point by point from starting point to the end, this method can not subtly consider between each point of control law
Influence, such as accelerate state control law design problem, the start, end lower state point of control law can to control advise
The design of rule generates constraint.On the one hand, the design object of general control rule requires engine most since control law starting point
Rotor accelerating power may be promoted fastly；And on the other hand, the presence of terminal make accelerating power need to reduce in advance again so that
The not excess revolutions in terminal.And beginning and end it is this constrain in point-by-point design during can not accurately account for simultaneously,
When every sub- control law point upper rotor part accelerating power begins to decline compared to upper lifting capacity and accelerating power,
These problems need engineering staff to be solved according to experience mostly, and generally requiring back and forth design can just be such that control law expires
All kinds of limitations of foot, and the control law that not can guarantee design is optimal.And traditional engine control regularity optimization method, it is past
It optimizes, is not can be used directly in the design of dynamic control law toward the stable state control law being only capable of for single-point.With mesh
Front engine controllable parameter more increases, it is necessary to develop new aero-engine dynamic control law optimization method, with
Under the premise of guaranteeing computational convergence and calculating speed, optimal engine dynamic control law is obtained.
Summary of the invention
Technical problem solved by the present invention is multiple to solve engine dynamic control law design process in the prior art
It is miscellaneous, towards more tunable component engine control regularity design problem poor feasibilities, and obtained control law and non-optimal
Problem, the present invention relates to a kind of engine control regularity optimization methods.
The technical scheme is that a kind of engine control regularity optimization method, comprising the following steps:
Step 1: assuming that it is A that engine dynamic process, which plays steady state point, whole steady state point is B, and optimization object is dynamic from A to B
In controllable parameter and tunable component regulated quantity changing rule, wherein several adjustment parameters and engine performance parameter include height
Rotational speed of lower pressure turbine rotor, jet pipe throat section area, compression member inlet guide vane angle, turbine part inlet guiding section face
Product, combustion chamber fuel delivery, turbine inlet temperature；
Step 2: determining design object and engine from A to B is required for dynamic process component or tractor parameter
The constraint condition of guarantee；Design object is taken as most short, constrained parameters the time required to the dynamic process from A to B and compression member is taken to breathe heavily
Nargin of shaking is not higher than threshold value not higher than threshold value, high-low pressure revolving speed rate of acceleration, and combustion chamber oil-gas ratio change rate is not higher than threshold value, three
Threshold value is different and differs.
Step 3: establishing initial control law, wherein initial control law can be arranged according to the following formula:
X=(XB-XA)×t/T
Wherein t is the independent variable time in dynamic control law, and T is the total time of dynamic control law, and X is in step 1
The vector of parameter composition, XBFor the control parameter vector of whole steady state point, XAFor the control parameter vector for playing steady state point；
Step 4: the control law established in step 3 is discrete for m sub- control law points according to independent variable, wherein m
A sub- control law point does not include A point and B point；
Step 5, for the m decomposed in step 4 sub- control law points establish corresponding m size as n and into
Change and optimizes sub- population；
Step 6 establishes elite individual collections.An individual is randomly selected as this from the sub- population of each evolutionary optimization
The elite individual of sub- population, elite individual collections are then the elite group of individuals of the sub- population of m evolutionary optimization.It needs to mention
It is the sub- control law point in the corresponding dynamic control law of any one individual in each sub- population of evolutionary optimization,
And the set being made of these individuals then corresponds to a dynamic control law, i.e. an elite individual collections also correspond to a dynamic
Control law.
Step 7, all sub- point-by-point Optimization Progress of population of initialization control law.Take i=1, wherein i represent will it is current and
The sub- population serial number of the evolutionary optimization that subsequent step is operated.
Step 8 carries out one-step optimization iteration to the sub- population of i-th of evolutionary optimization.To in the sub- population of i-th of evolutionary optimization
N current individual, referred to as parent individuality, are operated on it using the variation of differential evolution algorithm, crossover operator, generate n
A offspring individual.
Step 9 calculates the fitness of each individual in the sub- population of i-th of evolutionary optimization.
Step 10 screens the sub- population of i-th of evolutionary optimization.It is right according to the fitness being calculated in step 9
N individual in the sub- population of i-th of evolutionary optimization carries out fitness from as low as big sequence, in the sub- population of current evolutionary optimization only
N individual before retaining, and give up remaining individual.
Step 11 updates elite individual collections.The elite individual of the sub- population of i-th of evolutionary optimization is updated to the son kind
The smallest individual of fitness in group.
Step 12, judges whether primary point-by-point optimization circulation terminates.I=i+1 goes to step 13 if i > m, no
Then go to step 8.
Step 13, judges whether control law Optimization Progress terminates.If Optimization Progress reaches termination condition, step is gone to
Rapid 14, i=1 is otherwise enabled, and go to step 8.
Step 14, control law corresponding to current elite individual collections, as engine dynamic control law optimize
As a result.
Invention effect
The technical effects of the invention are that: engine dynamic control law optimization method of the invention is applied, can be overcome
The aero-engine dynamic control law design that conventional dynamic control law design method is not suitable for advanced more tunable components is asked
Topic solves conventional method and is affected by human factors big, the reciprocal iterative process of needs and is unable to get asking for optimal control law
Topic.
Detailed description of the invention
Fig. 1 is optional a kind of engine dynamic control law optimization method flow diagram according to embodiments of the present invention；
Fig. 2 is each sub- control rule in optionally a kind of engine dynamic control law optimization method according to embodiments of the present invention
Rule point Optimizing Flow schematic diagram；
It wherein include following parameter: i in attached drawing: the sub- population serial number of current evolutionary optimization；M: it decomposes to obtain by control law
Sub- control law always count, namely the corresponding sub- population total of evolutionary optimization.
Specific embodiment
Referring to Fig. 1-Fig. 2, in order to enable those skilled in the art to better understand the solution of the present invention, below in conjunction with this
Attached drawing in inventive embodiments, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that described
Embodiment only a part of the embodiments of the present invention, instead of all the embodiments.Based on the embodiments of the present invention, originally
This all should belong in field those of ordinary skill every other embodiment obtained without making creative work
Invent the range of protection.
Step 1, dynamic control law of the optimization object of this method between engine any two steady operation point.
Therefore firstly the need of each component adjustment parameter for playing steady state point A and whole steady state point B for obtaining engine dynamic process to be optimized
And engine performance parameter.By taking the dynamic control law optimization that fanjet ground idle speed to ground takes off as an example, hair is determined
Motivation dynamic process rises, whole steady state point is to obtain fanjet ground according to fanjet steady-state performance design result
Slow train steady state point and ground are taken off all parts adjustment parameter of steady state point.It include: HP&LP Rotor revolving speed, jet pipe throat section
Face area, compression member inlet guide vane angle, turbine part inlet guiding area of section, combustion chamber fuel delivery, variable area
Guider regulated quantity, compression member surge margin.
Step 2 determines dynamic control law optimization aim and constraint of the engine from steady state point A to steady state point B.With
For the dynamic control law optimization that fanjet ground idle speed to ground is taken off, design object is taken as entire dynamic and is taken
Between it is most short, i.e., engine transits to steady state point B from steady state point A as quickly as possible.And constraint condition is then taken as in entire dynamic,
Each compression member surge margin of engine is not higher than threshold value, and high-low pressure revolving speed rate of acceleration is not higher than threshold value, and combustion chamber oil-gas ratio becomes
Rate is not higher than threshold value, and three's threshold value is different and differs.
Step 3 establishes initial control law.General initial control law can be simply provided as control parameter and independent variable
Linear function.
Step 4, the control law established in decomposition step three are m sub- control law points, wherein m is represented decompose after
Sub- control law points.It should be noted that decomposing m obtained sub- control law points does not include dynamic, whole steady state point,
It rises, whole steady state point is control law optimization premise and constraint.
Step 5, for the m decomposed in step 4 sub- control law point establish corresponding m size be n and into
Change and optimizes sub- population.
Step 6 establishes elite individual collections.An individual is randomly selected as this from the sub- population of each evolutionary optimization
The elite individual of sub- population, elite individual collections are then the elite group of individuals of the sub- population of m evolutionary optimization.It needs to mention
It is the sub- control law point in the corresponding dynamic control law of any one individual in each sub- population of evolutionary optimization,
And the set being made of these individuals then corresponds to a dynamic control law, i.e. an elite individual collections also correspond to a dynamic
Control law.
Step 7, all sub- point-by-point Optimization Progress of population of initialization control law.Take i=1, wherein i represent will it is current and
The sub- population serial number of the evolutionary optimization that subsequent step is operated.
Step 8 carries out one-step optimization iteration to the sub- population of i-th of evolutionary optimization.To in the sub- population of i-th of evolutionary optimization
N current individual, referred to as parent individuality, are operated on it using the variation of differential evolution algorithm, crossover operator, generate n
A offspring individual.
Step 9 calculates the fitness of each individual in the sub- population of i-th of evolutionary optimization.
Step 10 screens the sub- population of i-th of evolutionary optimization.It is right according to the fitness being calculated in step 9
N individual in the sub- population of i-th of evolutionary optimization carries out fitness from as low as big sequence, in the sub- population of current evolutionary optimization only
N individual before retaining, and give up remaining individual.
Step 11 updates elite individual collections.The elite individual of the sub- population of i-th of evolutionary optimization is updated to the son kind
The smallest individual of fitness in group.
Step 12, judges whether primary point-by-point optimization circulation terminates.I=i+1 goes to step 13 if i > m, no
Then go to step 8.
Step 13, judges whether control law Optimization Progress terminates.If Optimization Progress reaches termination condition, step is gone to
Rapid 14, i=1 is otherwise enabled, and go to step 8.
Step 14, control law corresponding to current elite individual collections, as engine dynamic control law optimize
As a result.
It is according to time step or other independent variable parameters that engine dynamic control law is discrete for several sub- control laws
Point.It establishes corresponding sub- population for every sub- control law point in addition to beginning and end to optimize, and excellent in every step
Elite individual of the optimum individual as the sub- population is chosen after change, is freezed this sub- swarm optimization process and is carried out next sub- population
The point-by-point optimization of optimization, so circulation is until Optimization Progress meets termination condition.Its neutron swarm optimization target is set as each height
Population elite individual, i.e. each sub- control law point, the obtained engine target performance of the dynamic control law being composed
Parameter is optimal, and the restrictive condition of sub- swarm optimization can decompose to obtain from engine dynamic control law design limitation condition.
Further, stable state or dynamic control law are formed into discrete control law point in independent variable spatial discretization,
Corresponding sub- population is established for each control law point to optimize respectively.
Further, after the every step optimization of sub- population, current sub- population optimum individual is chosen as the sub- population
Elite individual, and the objective function of sub- swarm optimization is then set as each sub- population elite individual, i.e., each optimal sub- control rule
Point is restrained, the obtained engine performance parameter of the control law being composed is optimal, and the constraint condition of sub- swarm optimization is by controlling
Rule constraint condition decomposes to obtain.
Further, each one step of the sub- every progress of swarm optimization process updates elite after obtaining its elite individual at once
Body set, and suspend current sub- swarm optimization process, carry out next sub- swarm optimization.When all sub- populations optimize a step,
After obtaining elite individual after optimization and updating elite individual collections, continue optimization since first sub- population up to meeting
Termination condition.
The specific embodiment of the present invention program comprises the steps of.
Step 1, dynamic control law of the optimization object of this method between engine any two steady operation point.
Therefore firstly the need of each component adjustment parameter for playing steady state point A and whole steady state point B for obtaining engine dynamic process to be optimized
And engine performance parameter.By taking the dynamic control law optimization that fanjet ground idle speed to ground takes off as an example, hair is determined
Motivation dynamic process rises, whole steady state point is to obtain fanjet ground according to fanjet steady-state performance design result
Slow train steady state point and ground are taken off all parts adjustment parameter of steady state point.It include: HP&LP Rotor revolving speed, jet pipe throat section
Face area, compression member inlet guide vane angle, turbine part inlet guiding area of section, combustion chamber fuel delivery, variable area
Guider regulated quantity, compression member surge margin.
Step 2 determines dynamic control law optimization aim and constraint of the engine from steady state point A to steady state point B.With
For the dynamic control law optimization that fanjet ground idle speed to ground is taken off, design object is taken as entire dynamic and is taken
Between it is most short, i.e., engine transits to steady state point B from steady state point A as quickly as possible.And constraint condition is then taken as in entire dynamic,
Each compression member surge margin of engine is not higher than threshold value, and high-low pressure revolving speed rate of acceleration is not higher than threshold value, and combustion chamber oil-gas ratio becomes
Rate is not higher than threshold value, and three's threshold value is different and differs.
Step 3 establishes initial control law.General initial control law can be simply provided as control parameter and independent variable
Linear function.For using fuel flow as control parameter, independent variable is vacation for the dynamic control law initial value of time is chosen
If rising, the fuel flow of whole steady state point is respectively Wf_startAnd Wf_end, then initial control law can be arranged according to the following formula:
Wherein WfFor the control parameter fuel flow in dynamic control law, when t is the independent variable in dynamic control law
Between, T is the maximum value of independent variable t in dynamic control law, the i.e. total time of dynamic control law.
It should be noted that there are many selections for the control parameter and independent variable of dynamic control law, for example control parameter can
To select fuel flow, HP&LP Rotor revolving speed, each adjusting part regulated quantity etc., and independent variable can choose time, height
Rotational speed of lower pressure turbine rotor etc..The above control parameter and independent variable carry out any combination under the premise of not conflicting, and can form dynamic
Control law.And dynamic control law can also include the changing rule of multiple control parameters, the change of all control parameters simultaneously
Law requires to be initialized according to the previously described method of this step.Initial control law is only used as the first of subsequent optimization
Value, specific value do not influence final result, but need to guarantee that initial control law meets the control law determined in step 2
Constraint.
Step 4, the control law established in decomposition step three are m sub- control law points, wherein m is represented decompose after
Sub- control law points.The computing resource that this value influences the precision of final calculation result and expends required for calculating, knot
The time-consuming demand for closing computer nowadays performance and the optimization of general engine dynamic control law, it is proposed that its value range
For [10,100].It should be noted that decomposing m obtained sub- control law points does not include dynamic, whole steady state point, rise, eventually
Steady state point is control law optimization premise and constraint.
Step 5, for the m decomposed in step 4 sub- control law point establish corresponding m size be n and into
Change and optimizes sub- population.Wherein n represents the size of the sub- population of evolutionary optimization, i.e., each sub- population of evolutionary optimization includes n individual,
The value can be customized by users, and general value is [50,200].And each individual is comprising corresponding to the sub- population of its evolutionary optimization
Sub- control law point whole control parameters.
Step 6 establishes elite individual collections.An individual is randomly selected as this from the sub- population of each evolutionary optimization
The elite individual of sub- population, elite individual collections are then the elite group of individuals of the sub- population of m evolutionary optimization.It needs to mention
It is the sub- control law point in the corresponding dynamic control law of any one individual in each sub- population of evolutionary optimization,
And the set being made of these individuals then corresponds to a dynamic control law, i.e. an elite individual collections also correspond to a dynamic
Control law.
Step 7, all sub- point-by-point Optimization Progress of population of initialization control law.Take i=1, wherein i represent will it is current and
The sub- population serial number of the evolutionary optimization that subsequent step is operated.
Step 8 carries out one-step optimization iteration to the sub- population of i-th of evolutionary optimization.To in the sub- population of i-th of evolutionary optimization
N current individual, referred to as parent individuality, are operated on it using the variation of differential evolution algorithm, crossover operator, generate n
A offspring individual.
Step 9 calculates the fitness of each individual in the sub- population of i-th of evolutionary optimization.
Sub-step one initializes the sub- population's fitness calculation procedure of i-th of evolutionary optimization.J=1 is taken, wherein j is represented the
The individual serial number of fitness is currently calculated in the sub- population of i evolutionary optimization.It should be noted that i-th of evolutionary optimization is sub at this time
Comprising 2*n individual in population, i.e., comprising the n parent individuality mentioned in step 8 and n offspring individual.
Sub-step two calculates current individual fitness.By j-th of individual in the sub- population of i-th of evolutionary optimization, with remaining
The elite individual of the sub- population of evolutionary optimization is combined, and forms a set of control law.Engine is calculated according to this control law
Dynamic object performance, assess this target capabilities and obtain the fitness of the individual.Generally, if control law optimization aim is
Dynamic time is most short, then can use the inverse that fitness is dynamic time.Generally make optimization problem target Equivalent fitness most
It is small.
Sub-step three judges whether that all individual adaptation degree calculating finish.J=j+1 goes to step 10 if j > 2*n,
Otherwise sub-step two is gone to.
Step 10 screens the sub- population of i-th of evolutionary optimization.It is right according to the fitness being calculated in step 9
N individual in the sub- population of i-th of evolutionary optimization carries out fitness from as low as big sequence, in the sub- population of current evolutionary optimization only
N individual before retaining, and give up remaining individual.
Step 11 updates elite individual collections.The elite individual of the sub- population of i-th of evolutionary optimization is updated to the son kind
The smallest individual of fitness in group.
Step 12, judges whether primary point-by-point optimization circulation terminates.I=i+1 goes to step 13 if i > m, no
Then go to step 8.
Step 13, judges whether control law Optimization Progress terminates.If Optimization Progress reaches termination condition, step is gone to
Rapid 14, i=1 is otherwise enabled, and go to step 8.
Step 14, control law corresponding to current elite individual collections, as engine dynamic control law optimize
As a result.
The above is only the preferred embodiments that the present invention accelerates state control law to optimize for mixed exhaust turbofan, answer
When pointing out, for those skilled in the art, without departing from the principle of the present invention, can also make
Several improvements and modifications, these modifications and embellishments should also be considered as the scope of protection of the present invention.
Claims (1)
1. a kind of engine dynamic control law optimization method, which comprises the following steps:
Step 1: assuming that it is A that engine dynamic process, which plays steady state point, whole steady state point is B, and optimization object is transition state from A to B
In controllable parameter and tunable component regulated quantity changing rule, wherein several adjustment parameters and engine performance parameter include height
Rotational speed of lower pressure turbine rotor, jet pipe throat section area, compression member inlet guide vane angle, turbine part inlet guiding section face
Product, combustion chamber fuel delivery, variable area guider regulated quantity, compression member surge margin；
Step 2: determine that design object and engine from A to B guarantee required for dynamic process component or tractor parameter
Constraint condition；Design object is taken as most short, constrained parameters the time required to the dynamic process from A to B and takes compression member surge abundant
Degree is not higher than threshold value not higher than threshold value, high-low pressure revolving speed rate of acceleration, and combustion chamber oil-gas ratio change rate is not higher than threshold value, three's threshold value
It is different and differ.
Step 3: establishing initial control law, wherein initial control law can be arranged according to the following formula:
X=(XB-XA)×t/T
Wherein t is the independent variable time in dynamic control law, and T is the total time of dynamic control law, and X is parameter in step 1
The vector of composition, XBFor the control parameter vector of whole steady state point, XAFor the control parameter vector for playing steady state point；
Step 4: the control law established in step 3 is discrete for m sub- control law points according to independent variable,
Wherein m sub- control law points do not include A point and B point；
It is excellent to establish the evolution that corresponding m size is n for the m decomposed in step 4 sub- control law points for step 5
Beggar population；
Step 6 establishes elite individual collections.An individual is randomly selected from the sub- population of each evolutionary optimization as the son kind
The elite individual of group, elite individual collections are then the elite group of individuals of the sub- population of m evolutionary optimization.Need it is to be noted that
The sub- control law point in the corresponding dynamic control law of any one individual in each sub- population of evolutionary optimization, and by
The set of these individual compositions then corresponds to a dynamic control law, i.e. an elite individual collections also correspond to a dynamic and control
Rule.
Step 7, all sub- point-by-point Optimization Progress of population of initialization control law.I=1 is taken, wherein i representative wants current and subsequent
The sub- population serial number of the evolutionary optimization that step is operated.
Step 8 carries out one-step optimization iteration to the sub- population of i-th of evolutionary optimization.To current in the sub- population of i-th of evolutionary optimization
N individual, referred to as parent individuality operated on it using the variation of differential evolution algorithm, crossover operator, generate n it is sub
Generation individual.
Step 9 calculates the fitness of each individual in the sub- population of i-th of evolutionary optimization.
Step 10 screens the sub- population of i-th of evolutionary optimization.According to the fitness being calculated in step 9, to i-th
N individual in the sub- population of evolutionary optimization carries out fitness from as low as big sequence, only retains in the sub- population of current evolutionary optimization
Preceding n individual, and give up remaining individual.
Step 11 updates elite individual collections.The elite individual of the sub- population of i-th of evolutionary optimization is updated in the sub- population
The smallest individual of fitness.
Step 12, judges whether primary point-by-point optimization circulation terminates.I=i+1 goes to step 13, otherwise turns if i > m
To step 8.
Step 13, judges whether control law Optimization Progress terminates.If Optimization Progress reaches termination condition, step 10 is gone to
Four, i=1 is otherwise enabled, and go to step 8.
Step 14, control law, as engine dynamic control law optimum results corresponding to current elite individual collections.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910055751.6A CN109634107B (en) | 2019-01-22 | 2019-01-22 | Engine dynamic control rule optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910055751.6A CN109634107B (en) | 2019-01-22 | 2019-01-22 | Engine dynamic control rule optimization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109634107A true CN109634107A (en) | 2019-04-16 |
| CN109634107B CN109634107B (en) | 2021-07-16 |
Family
ID=66062260
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910055751.6A Active CN109634107B (en) | 2019-01-22 | 2019-01-22 | Engine dynamic control rule optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109634107B (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111783239A (en) * | 2020-06-05 | 2020-10-16 | 西北工业大学 | Fuzzy reliability analysis method for turbine tenon connection structure |
| CN112085145A (en) * | 2020-09-04 | 2020-12-15 | 北京航空航天大学 | Method for designing control law of mode conversion process of self-adaptive circulating aero-engine |
| WO2021097696A1 (en) * | 2019-11-20 | 2021-05-27 | 大连理工大学 | Adaptive dynamic programming method for aero-engine in optimal acceleration tracking control |
| CN115587446A (en) * | 2022-10-24 | 2023-01-10 | 南京航空航天大学 | Method for optimizing multi-task steady-state control rule of variable cycle engine |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104375421A (en) * | 2014-11-24 | 2015-02-25 | 西北工业大学 | Aircraft engine part modeling method based on control system development platform |
| CN104392039A (en) * | 2014-11-19 | 2015-03-04 | 中国航空工业集团公司沈阳飞机设计研究所 | Transition state engine simulation modeling method |
| US20150316905A1 (en) * | 2014-05-01 | 2015-11-05 | Aspen Technology, Inc. | Online control calculation for models containing near colinearity and uncertainty |
| CN105302124A (en) * | 2015-12-03 | 2016-02-03 | 西北工业大学 | Turboshaft engine control performance index extraction method based on test data |
| CN106970523A (en) * | 2017-03-01 | 2017-07-21 | 南京航空航天大学 | A kind of energy management strategies of aircraft self-adapting power and heat management system |
| CN107357176A (en) * | 2017-08-05 | 2017-11-17 | 中国科学院工程热物理研究所 | A kind of aeroengine test run Data Modeling Method |
| CN108573116A (en) * | 2018-05-11 | 2018-09-25 | 南京航空航天大学 | A kind of aero-engine transition state thrust algorithm for estimating based on long memory network in short-term |
| CN108762089A (en) * | 2018-06-15 | 2018-11-06 | 大连理工大学 | A kind of aero-engine on-line optimization and multivariable Control design method based on model prediction |
| CN108828947A (en) * | 2018-07-13 | 2018-11-16 | 南京航空航天大学 | A kind of uncertain dynamic fuzzy model modeling method of the aero-engine containing time lag |
-
2019
- 2019-01-22 CN CN201910055751.6A patent/CN109634107B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150316905A1 (en) * | 2014-05-01 | 2015-11-05 | Aspen Technology, Inc. | Online control calculation for models containing near colinearity and uncertainty |
| CN104392039A (en) * | 2014-11-19 | 2015-03-04 | 中国航空工业集团公司沈阳飞机设计研究所 | Transition state engine simulation modeling method |
| CN104375421A (en) * | 2014-11-24 | 2015-02-25 | 西北工业大学 | Aircraft engine part modeling method based on control system development platform |
| CN105302124A (en) * | 2015-12-03 | 2016-02-03 | 西北工业大学 | Turboshaft engine control performance index extraction method based on test data |
| CN106970523A (en) * | 2017-03-01 | 2017-07-21 | 南京航空航天大学 | A kind of energy management strategies of aircraft self-adapting power and heat management system |
| CN107357176A (en) * | 2017-08-05 | 2017-11-17 | 中国科学院工程热物理研究所 | A kind of aeroengine test run Data Modeling Method |
| CN108573116A (en) * | 2018-05-11 | 2018-09-25 | 南京航空航天大学 | A kind of aero-engine transition state thrust algorithm for estimating based on long memory network in short-term |
| CN108762089A (en) * | 2018-06-15 | 2018-11-06 | 大连理工大学 | A kind of aero-engine on-line optimization and multivariable Control design method based on model prediction |
| CN108828947A (en) * | 2018-07-13 | 2018-11-16 | 南京航空航天大学 | A kind of uncertain dynamic fuzzy model modeling method of the aero-engine containing time lag |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021097696A1 (en) * | 2019-11-20 | 2021-05-27 | 大连理工大学 | Adaptive dynamic programming method for aero-engine in optimal acceleration tracking control |
| AU2019436653B2 (en) * | 2019-11-20 | 2021-11-11 | Dalian University Of Technology | Adaptive dynamic programming method of aircraft engine in optimal acceleration tracking control |
| CN111783239A (en) * | 2020-06-05 | 2020-10-16 | 西北工业大学 | Fuzzy reliability analysis method for turbine tenon connection structure |
| CN112085145A (en) * | 2020-09-04 | 2020-12-15 | 北京航空航天大学 | Method for designing control law of mode conversion process of self-adaptive circulating aero-engine |
| CN112085145B (en) * | 2020-09-04 | 2023-02-21 | 北京航空航天大学 | Method for designing control law of mode conversion process of self-adaptive circulating aero-engine |
| CN115587446A (en) * | 2022-10-24 | 2023-01-10 | 南京航空航天大学 | Method for optimizing multi-task steady-state control rule of variable cycle engine |
| CN115587446B (en) * | 2022-10-24 | 2024-05-28 | 南京航空航天大学 | Multi-task steady-state control law optimization method for variable cycle engine |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109634107B (en) | 2021-07-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109634107A (en) | A kind of engine dynamic control law optimization method | |
| US20200362770A1 (en) | Design method for optimization of transient control law of aero-engine | |
| CN106485064B (en) | A kind of intelligent starting-up method of pump-storage generator hydraulic turbine condition | |
| CN105631140B (en) | A kind of analysis of variable cycle engine steady-state behaviour and optimization method | |
| CN103927580B (en) | Project constraint parameter optimizing method based on improved artificial bee colony algorithm | |
| CN110970911B (en) | Control method for mutual superposition of AGC and primary frequency modulation in opening degree mode | |
| CN107798199A (en) | A kind of Hydropower Unit parameter closed-loop identification method | |
| CN109611217A (en) | A kind of design method of aero-engine transition state control law optimization | |
| CN105676640B (en) | Fanjet acceleration control rule design method based on Bezier | |
| CN107272403A (en) | A kind of PID controller parameter setting algorithm based on improvement particle cluster algorithm | |
| CN111594322B (en) | Variable-cycle aero-engine thrust control method based on Q-Learning | |
| CN108280263A (en) | A kind of pump-storage generator distributor closing law Bi-Objective Optimization Method and system | |
| WO2024007465A1 (en) | Adaptive variable-parameter frequency modulation method and device for doubly fed variable-speed pumped-storage unit | |
| WO2021093493A1 (en) | Power flow intelligent adjustment method based on q-learning algorithm | |
| CN110059345A (en) | A kind of pump-storage generator successive load rejection ring-closing metathesis optimization method and system | |
| CN109814372A (en) | Governor power mode control method based on head aperture Yu active power combination curve | |
| CN110661259A (en) | Power system stabilizer parameter optimization method and system for suppressing frequency oscillation | |
| CN110262223A (en) | A kind of comprehensive model modelling approach of hydraulic turbine based on Fractional Order PID speed-regulating system | |
| CN111523749A (en) | Intelligent identification method for hydroelectric generating set model | |
| CN110097176A (en) | A kind of neural network structure searching method applied to air quality big data abnormality detection | |
| CN109857459A (en) | A kind of E grades of supercomputer ocean model transplants optimization method and system automatically | |
| CN109298747A (en) | SMESC wind power system MPPT method based on IIWO optimization | |
| CN116520909A (en) | High-value consumable cabinet temperature control method for optimizing fuzzy PID parameters by Harris eagle algorithm | |
| CN101140636A (en) | System and method for generating robot genome | |
| CN110361974A (en) | Turbine Governor System optimization method based on BP-FOA hybrid algorithm |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |