CN110219736A - Aero-engine Direct Thrust Control Strategy based on Nonlinear Model Predictive Control - Google Patents
Aero-engine Direct Thrust Control Strategy based on Nonlinear Model Predictive Control Download PDFInfo
- Publication number
- CN110219736A CN110219736A CN201910531675.1A CN201910531675A CN110219736A CN 110219736 A CN110219736 A CN 110219736A CN 201910531675 A CN201910531675 A CN 201910531675A CN 110219736 A CN110219736 A CN 110219736A
- Authority
- CN
- China
- Prior art keywords
- control
- engine
- aero
- nonlinear model
- model predictive
- 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
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02C—GAS-TURBINE PLANTS; AIR INTAKES FOR JET-PROPULSION PLANTS; CONTROLLING FUEL SUPPLY IN AIR-BREATHING JET-PROPULSION PLANTS
- F02C9/00—Controlling gas-turbine plants; Controlling fuel supply in air- breathing jet-propulsion plants
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F02—COMBUSTION ENGINES; HOT-GAS OR COMBUSTION-PRODUCT ENGINE PLANTS
- F02C—GAS-TURBINE PLANTS; AIR INTAKES FOR JET-PROPULSION PLANTS; CONTROLLING FUEL SUPPLY IN AIR-BREATHING JET-PROPULSION PLANTS
- F02C9/00—Controlling gas-turbine plants; Controlling fuel supply in air- breathing jet-propulsion plants
- F02C9/26—Control of fuel supply
- F02C9/28—Regulating systems responsive to plant or ambient parameters, e.g. temperature, pressure, rotor speed
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2260/00—Function
- F05D2260/81—Modelling or simulation
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05D—INDEXING SCHEME FOR ASPECTS RELATING TO NON-POSITIVE-DISPLACEMENT MACHINES OR ENGINES, GAS-TURBINES OR JET-PROPULSION PLANTS
- F05D2270/00—Control
- F05D2270/70—Type of control algorithm
- F05D2270/709—Type of control algorithm with neural networks
Landscapes
- Engineering & Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Combustion & Propulsion (AREA)
- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a kind of aero-engine Direct Thrust Control Strategy based on Nonlinear Model Predictive Control.The method of the present invention directly with thrust be control target, rather than traditional control method take can not survey parameter for control mesh calibration method.Using online sliding window deep neural network as prediction model, which uses deep learning structure, model accuracy can be improved, and chooses training data online based on sliding window method, reduces the sensibility to training data noise.It is compared compared to currently a popular control method, the method proposed will shorten 0.425 second the acceleration time, and response speed improves 1.14 times or so.
Description
Technical field
The present invention relates to a kind of aero-engine control methods more particularly to a kind of based on Nonlinear Model Predictive Control
Aero-engine Direct Thrust Control Strategy.
Background technique
The major function of aero-engine is quickly and accurately to provide thrust for aircraft.Traditional engine control strategy
Be it is sensor-based, i.e., can survey parameter such as rotor speed, engine pressure ratio (EPR) or other by controlling engine and can survey ginseng
Number is to indirectly control thrust.But due to factors such as degeneration, manufacture and manufacturing tolerances, motor power and pair between parameter can be surveyed
Should be related to can change.Therefore, if using traditional control thought, motor power control error is certainly existed.In addition,
In order to guarantee engine the worst operating point can safe and stable operation, traditional control strategy often keeps biggish safety abundant
Degree, this strategy can greatly limit engine in the performance of other operating points.
For these disadvantages, researcher has invented a kind of new control strategy --- the engine control based on model
(ModelBasedEngineControl, MBEC), directly controls engine performance.Wherein Model Predictive Control
(ModelPredictive Control, MPC) is important a key technology and research in the engine control based on model
Field.MPC is caused in aero-engine control field and is widely ground with having better accelerating ability than traditional controller
Study carefully interest.MPC technology is applied in the gas turbine of laboratory by VroemenBG etc..Brunell etc. has studied the band of state estimation
The feasibility of the Nonlinear Model Predictive Control (Nonlinear ModelPredictive Control, NMPC) of constraint, and will
In its simulation model for being applied to turbojet engine.DeCastro proposes a kind of NMPC based on linear variation parameter, and applies
In commercial fanjet active tip clearance.Richter proposes a kind of implementation method of multiplexing, considerably reduces
The calculation amount of NMPC optimization algorithm.Di Cairano etc. develops a kind of MPC strategy, by electronic throttle and spark timing come
Engine speed is controlled to ground idle speed.The model prediction model to work above focuses primarily upon on linear model, achieves very
Good control effect.However, aero-engine is a strong nonlinearity power device, the modeling error of linear model can not be kept away
Exempt from.In addition, the control target of tradition NMPC is measurable parameter.And the corresponding pass of parameter with motor power or power can be surveyed
Engine active time changes at any time for system, controls accuracy decline so as to cause thrust.
Summary of the invention
It is pre- based on nonlinear model that the technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and to provide one kind
The aero-engine Direct Thrust Control Strategy of observing and controlling, can effectively improve the responding ability of engine.
The present invention specifically uses following technical scheme to solve above-mentioned technical problem:
A kind of aero-engine Direct Thrust Control Strategy based on Nonlinear Model Predictive Control is directly to control with thrust
Target processed, is controlled using nonlinear model predictive control method；It is realized especially by following rolling optimization problem is solved:
Wherein r is engine control instruction,To control target prediction value, u is control variable vector, Nf、NcRespectively wind
Fan revolving speed, compressor rotor revolving speed, Smf、SmcRespectively fan surge margin, compressor surge nargin, T41For high-pressure turbine into
Mouth temperature, Q and R positive definite is symmetrical, positive integer NuAnd NpRespectively control time domain and prediction time domain.
Preferably, use the online sliding window deep neural network of training in advance as the non-linear mould predictive control
The prediction model of method processed, the loss function description of the online sliding window deep neural network are as follows:
Wherein x is input vector, and y is output vector, and j is expressed as the jth moment, and L is to roll siding-to-siding block length, fDNNStatement is deep
The mapping of neural network is spent, W is weight matrix, and b is bias vector.
It is further preferred that the input x of the prediction modelDNNWith output yDNNIt is specific as follows:
Wherein, Wfb(k), Nf(k),Nc(k),Smf(k),Smc(k),T41(k), F (k) is respectively the engine fuel at k moment
Input, rotation speed of the fan, compressor rotor revolving speed, fan surge margin, compressor surge nargin, high-pressure turbine inlet temperature, hair
Motivation thrust, m1,m2,…,m7For preset positive integer.
Compared with prior art, technical solution of the present invention has the advantages that
The present invention is remarkably improved the control precision and response speed of aeroengine control system.
Detailed description of the invention
Fig. 1 is predictive control system of nonlinear model structural schematic diagram of the invention；
Fig. 2 is rolling optimization schematic illustration；
Fig. 3 is deep neural network figure；
Fig. 4 is sliding window schematic illustration；
Fig. 5 is back-propagation algorithm schematic illustration；
Fig. 6 (a)~Fig. 6 (g) is the simulation result of the method for the present invention, F, T therein41、Nf、Nc、Smf、SmcIt normalizes
Processing.
Specific embodiment
In view of the shortcomings of the prior art, it is to directly control target that thinking of the invention, which is with thrust, it is pre- using nonlinear model
It surveys control method to be controlled, to improve the control precision and response speed of aeroengine control system.
Technical solution of the present invention is described in detail with reference to the accompanying drawing:
Predictive control system of nonlinear model structure of the invention is as shown in Figure 1.The estimation of engine nonlinear model can not
Measurement parameter, NMP module include optimization submodule and prediction model submodule, and the present invention uses online sliding window depth nerve net
Network (OL-SW-DNN) is used as prediction model, has than other shallow-layer network structures such as traditional neural network and support vector regression
There is stronger capability of fitting, improves precision of prediction.In optimization submodule, the optimization aim of NMPC is thrust command.It can be with
Find out, the optimization submodule and prediction model submodule of NMPC is key technology, specific as follows.
Nonlinear Model Predictive Control is a closed loop, limited vehicle air-conditioning.As shown in Fig. 2, at the k moment,
Control target are as follows:
Wherein, r is control instruction,To control target prediction value, u is control variable vector, Q and R positive definite is symmetrical.It is just whole
Number NuAnd NpRespectively control interval and forecast interval.
The control target of traditional aero-engine always selects measurable parameter, as engine booster ratio or rotor turn
Speed.But thrust and the relationship that can be surveyed between parameter can change with the variation for using the time.Therefore, control target of the invention
It is to directly control thrust F.
The limitation limited when the operation of engine by mechanical and part temperatures, as the rotor speed of fan and compressor limits
System, the surge margin limitation of fan and compressor, the limitation such as inlet temperature of high-pressure turbine.In addition, it is also contemplated that executing agency
Physical constraint.Therefore, in order to guarantee engine health stable operation, engine, which is run, should meet following constraint condition:
Wherein Nf、NcRespectively fan, compressor rotor revolving speed, Smf、SmcRespectively fan, compressor surge nargin, T41
For high-pressure turbine inlet temperature.
Equation (1) and control target in equation (2) and restriction are calculated by prediction model.Prediction model
Accuracy will directly affect control effect.The prediction model of traditional NMPC is linear model.However, the transient state of aero-engine
Process is a very strong non-linear process.Therefore, it has invented a kind of based on online sliding window deep neural network (On
Line Sliding Window Deep Neural Network, OL-SW-DNN) NMPC as prediction model.
DNN has stronger capability of fitting to non-linear object, can be described as:
Y=fDNN(x) (3)
Wherein x is input vector, and y is output vector.In order to keep the dynamic characteristic of engine, the input packet of prediction model
Include current and the historical juncture engine fuel input WfbAnd the S of historical juncturemf,Smc,Nf,T41And F.Prediction model it is defeated
Enter and output be:
Wherein, m1,m2,…,m7Selection it is related with the nonlinearity of engine.As shown in Figure 3, Figure 4, DNN has multilayer
The hidden layer of network structure, DNN is more, and the capability of fitting of DNN is stronger.Each layer of DNN can be with is defined as:
al+1=Wlhl+bl (5)
hl+1=σ (al+1) (6)
Wherein, WlIt is weight matrix, blIt is bias vector, σ is activation primitive, hl(l > 0) is l layers of output, l=1,
2,…,nl,nlIt is every layer of implicit node number.
The loss function of OL-SW-DNN describes are as follows:
W and b updates as follows:
Wherein,It is respectivelyGradient, η is learning rate.
WithIt can be calculated by back-propagation algorithm shown in fig. 5:
Wherein, δlIt is:
Wherein, l=nnet,nnet- 1, L, 2,It is Hadamard product
It enablesAre as follows:
Wherein, nnetIt is the network number of plies.
For the validity for verifying this method, to the NMPC based on the method for the present invention and it is based on Extended Kalman filter respectively
The popular NMPC of (ExtendedKalman Filter, EKF) is emulated.The simulation process of both methods all selects
Engine accelerating course.Accelerator is steady with PLA=70 ° using PLA=26 ° of Thrust Level Angel of steady operation point as starting point
State operating point is terminal.Normal atmosphere shape when the service condition of both emulation is all height H=0 km, Mach number Ma=0
State.Attached drawing gives the accelerator emulation of the NMPC based on the EKF and NMPC of proposition, wherein ' NMPC-EKF ' expression is based on
The NMPC of EFK, ' NMPC-DNN ' indicate the NMPC based on OL-SW-DNN.For convenience's sake, both methods is distinguished below
It is described as the NMPC of traditional NMPC and proposition.Normalized has been carried out to engine parameter in figure.
As shown in Fig. 6 (a), in traditional NMPC and the NMPC of proposition, motor power increase to 100% thrust when
Between be respectively 3.025 seconds and 2.6 seconds.Compared with traditional NMPC, the NMPC of proposition reduces the acceleration time 0.425 second, adds
Degree of hastening almost increases 1.14 times.Main cause is that traditional NMPC uses linear model as prediction model.However, starting
The transient process of machine is a very strong non-linear process.It uses linear model as prediction model, predicts that error is inevitable
's.And invented NMPC uses OL-SW-DNN as prediction model.OL-SW-DNN has stronger fitting to non-linear object
Ability improves the precision of prediction of NMPC.
As shown in Fig. 6 (g), operating point of the engine in accelerator is moved along stall margin, this is considered as starting
The most fast path of machine acceleration responsive.For other limitations, as shown in Fig. 6 (c)~6 (f), engine does not reach overtemperature, surpasses
There is surge in speed.Therefore, control method of the present invention control precision with higher and response speed.
Claims (3)
1. a kind of aero-engine Direct Thrust Control Strategy based on Nonlinear Model Predictive Control, which is characterized in that push away
Power is to directly control target, is controlled using nonlinear model predictive control method；Especially by the following rolling optimization of solution
Problem is realized:
Wherein r is engine control instruction,To control target prediction value, u is control variable vector, Nf、NcRespectively fan turns
Speed, compressor rotor revolving speed, Smf、SmcRespectively fan surge margin, compressor surge nargin, T41For high-pressure turbine import temperature
Degree, Q and R positive definite is symmetrical, positive integer NuAnd NpRespectively control time domain and prediction time domain.
2. the aero-engine Direct Thrust Control Strategy based on Nonlinear Model Predictive Control as described in claim 1, special
Sign is, uses the online sliding window deep neural network of training in advance as the nonlinear model predictive control method
Prediction model, the loss function description of the online sliding window deep neural network are as follows:
Wherein x is input vector, and y is output vector, and j is expressed as the jth moment, and L is to roll siding-to-siding block length, fDNNState depth mind
Mapping through network, W are weight matrix, and b is bias vector.
3. the aero-engine Direct Thrust Control Strategy based on Nonlinear Model Predictive Control as claimed in claim 2, special
Sign is, the input x of the prediction modelDNNWith output yDNNIt is specific as follows:
Wherein, Wfb(k), Nf(k),Nc(k),Smf(k),Smc(k),T41(k), F (k) be respectively the k moment engine fuel input,
Rotation speed of the fan, compressor rotor revolving speed, fan surge margin, compressor surge nargin, high-pressure turbine inlet temperature, engine push away
Power, m1,m2,…,m7For preset positive integer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910531675.1A CN110219736B (en) | 2019-06-19 | 2019-06-19 | Aero-engine direct thrust control method based on nonlinear model predictive control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910531675.1A CN110219736B (en) | 2019-06-19 | 2019-06-19 | Aero-engine direct thrust control method based on nonlinear model predictive control |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110219736A true CN110219736A (en) | 2019-09-10 |
| CN110219736B CN110219736B (en) | 2020-02-18 |
Family
ID=67817808
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910531675.1A Active CN110219736B (en) | 2019-06-19 | 2019-06-19 | Aero-engine direct thrust control method based on nonlinear model predictive control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110219736B (en) |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111425304A (en) * | 2020-04-23 | 2020-07-17 | 南京航空航天大学 | Aero-engine direct thrust control method based on composite model predictive control |
| CN111498148A (en) * | 2020-04-23 | 2020-08-07 | 北京航空航天大学 | FDNN-based intelligent spacecraft controller and control method |
| CN112731915A (en) * | 2020-08-31 | 2021-04-30 | 武汉第二船舶设计研究所(中国船舶重工集团公司第七一九研究所) | Direct track control method for optimizing NMPC algorithm based on convolutional neural network |
| CN113282004A (en) * | 2021-05-20 | 2021-08-20 | 南京航空航天大学 | Neural network-based aeroengine linear variable parameter model establishing method |
| CN113864067A (en) * | 2020-06-30 | 2021-12-31 | 中国航发商用航空发动机有限责任公司 | Rolling optimization prediction closed-loop controller and system |
| CN114818205A (en) * | 2022-06-27 | 2022-07-29 | 南京航空航天大学 | Online sensing method for blade tip clearance of full life cycle of aero-engine |
| CN113741195B (en) * | 2021-09-14 | 2023-09-08 | 厦门大学 | Nonlinear control method and system for aero-engine |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1538319A1 (en) * | 2003-12-05 | 2005-06-08 | General Electric Company | Apparatus for model predictive control of aircraft gas turbine engines |
| CN103306822A (en) * | 2013-05-23 | 2013-09-18 | 南京航空航天大学 | Aerial turbofan engine control method based on surge margin estimation model |
| WO2014149043A1 (en) * | 2013-03-20 | 2014-09-25 | International Truck Intellectual Property Company, Llc | Smart cruise control system |
| US20180216558A1 (en) * | 2015-12-15 | 2018-08-02 | Mtu Friedrichshafen Gmbh | Method and device for predictive open-loop and/or closed-loop control of an internal combustion engine and internal combustion engine having the device for carrying out the method |
| CN109446605A (en) * | 2018-10-16 | 2019-03-08 | 南京航空航天大学 | Turboshaft engine nonlinear dynamic inversion control method and device |
| CN109583480A (en) * | 2018-11-08 | 2019-04-05 | 中国人民解放军空军航空大学 | One kind being used for aero-engine anti-asthma control system bathtub curve estimation method |
-
2019
- 2019-06-19 CN CN201910531675.1A patent/CN110219736B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1538319A1 (en) * | 2003-12-05 | 2005-06-08 | General Electric Company | Apparatus for model predictive control of aircraft gas turbine engines |
| WO2014149043A1 (en) * | 2013-03-20 | 2014-09-25 | International Truck Intellectual Property Company, Llc | Smart cruise control system |
| CN103306822A (en) * | 2013-05-23 | 2013-09-18 | 南京航空航天大学 | Aerial turbofan engine control method based on surge margin estimation model |
| US20180216558A1 (en) * | 2015-12-15 | 2018-08-02 | Mtu Friedrichshafen Gmbh | Method and device for predictive open-loop and/or closed-loop control of an internal combustion engine and internal combustion engine having the device for carrying out the method |
| CN109446605A (en) * | 2018-10-16 | 2019-03-08 | 南京航空航天大学 | Turboshaft engine nonlinear dynamic inversion control method and device |
| CN109583480A (en) * | 2018-11-08 | 2019-04-05 | 中国人民解放军空军航空大学 | One kind being used for aero-engine anti-asthma control system bathtub curve estimation method |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111425304A (en) * | 2020-04-23 | 2020-07-17 | 南京航空航天大学 | Aero-engine direct thrust control method based on composite model predictive control |
| CN111498148A (en) * | 2020-04-23 | 2020-08-07 | 北京航空航天大学 | FDNN-based intelligent spacecraft controller and control method |
| CN111425304B (en) * | 2020-04-23 | 2021-01-12 | 南京航空航天大学 | Aero-engine direct thrust control method based on composite model predictive control |
| CN113864067A (en) * | 2020-06-30 | 2021-12-31 | 中国航发商用航空发动机有限责任公司 | Rolling optimization prediction closed-loop controller and system |
| CN113864067B (en) * | 2020-06-30 | 2022-08-02 | 中国航发商用航空发动机有限责任公司 | Rolling optimization prediction closed-loop controller and system |
| CN112731915A (en) * | 2020-08-31 | 2021-04-30 | 武汉第二船舶设计研究所(中国船舶重工集团公司第七一九研究所) | Direct track control method for optimizing NMPC algorithm based on convolutional neural network |
| CN113282004A (en) * | 2021-05-20 | 2021-08-20 | 南京航空航天大学 | Neural network-based aeroengine linear variable parameter model establishing method |
| CN113282004B (en) * | 2021-05-20 | 2022-06-10 | 南京航空航天大学 | Neural network-based aeroengine linear variable parameter model establishing method |
| CN113741195B (en) * | 2021-09-14 | 2023-09-08 | 厦门大学 | Nonlinear control method and system for aero-engine |
| CN114818205A (en) * | 2022-06-27 | 2022-07-29 | 南京航空航天大学 | Online sensing method for blade tip clearance of full life cycle of aero-engine |
| CN114818205B (en) * | 2022-06-27 | 2022-09-09 | 南京航空航天大学 | Online sensing method for blade tip clearance of full life cycle of aero-engine |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110219736B (en) | 2020-02-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110219736A (en) | Aero-engine Direct Thrust Control Strategy based on Nonlinear Model Predictive Control | |
| CN109162813B (en) | One kind being based on the modified Aeroengine Smart method for controlling number of revolution of iterative learning | |
| CN103306822B (en) | Aerial turbofan engine control method based on surge margin estimation model | |
| De Giorgi et al. | Hybrid multigene genetic programming-artificial neural networks approach for dynamic performance prediction of an aeroengine | |
| CN110195660B (en) | Aero-engine control device based on deep Q learning | |
| CN107942653A (en) | Aviation electric fuel oil pump flow control system sensor fault robust Fault-Tolerant method | |
| CN110516395A (en) | A kind of aero-engine control method based on non-linear mould predictive | |
| Kobayashi et al. | Integration of on-line and off-line diagnostic algorithms for aircraft engine health management | |
| US20240077039A1 (en) | Optimization control method for aero-engine transient state based on reinforcement learning | |
| CN108196443A (en) | The nonlinear prediction method design method of variable cycle engine | |
| CN105404750A (en) | Turboshaft engine self-adaptive model building method based on modified Broyden algorithm to solve equation set | |
| CN110221537A (en) | Control method, device and aero-engine limitation protection control method, device | |
| CN109472062A (en) | A kind of variable cycle engine self-adaptive component grade simulation model construction method | |
| CN110488609A (en) | A kind of aero-engine direct Thrust inverse control method and device | |
| Pang et al. | Joint steady state and transient performance adaptation for aero engine mathematical model | |
| CN111679576B (en) | Variable cycle engine controller design method based on improved deterministic strategy gradient algorithm | |
| Zheng et al. | A study on aero-engine direct thrust control with nonlinear model predictive control based on deep neural network | |
| Liu et al. | Model reference adaptive control for aero-engine based on system equilibrium manifold expansion model | |
| Qian et al. | LPV/PI control for nonlinear aeroengine system based on guardian maps theory | |
| Yu et al. | A new method for the design of optimal control in the transient state of a gas turbine engine | |
| CN110985216A (en) | Intelligent multivariable control method for aero-engine with online correction | |
| Wang et al. | Gain scheduling controller of the aero-engine based on LPV model | |
| Zhu et al. | Design of Intelligent Controller for Aero-engine Based on TD3 Algorithm | |
| Panda et al. | Sliding mode control of gas turbines using multirate-output feedback | |
| Chen et al. | Design of Control Laws Based on Inverted Decoupling and LMI for a Turboprop Engine |
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 |