Frequency Domain Identification of Multivariable Model for Aero-Engine using an Improved Maximum Likelihood Method
-
Nan Liu
,
Feng Lu
Abstract
For the linear modeling problem of multivariable system of aero-engine, considering the coupling between parameters, a multivariable maximum likelihood (ML) estimation method is researched. An improved expectation-maximization (EM) algorithm integrated genetic algorithm (GA) is proposed and applied to the process of ML identification of frequency domain. The amplitude, harmonic and phase vectors of odd-odd multi-sine exciting signal are designed and optimized. With the application of the proposed method, multivariable linear models of aero-engine at different operation states in flight envelope are established from nonlinear component-level model. The precision is demonstrated through simulations comparing to nonlinear model.
Funding statement: Funding: This work is supported by “the Fundamental Research Funds for the Central Universities (NS2013017)”.
References
1. SanjayG. Aircraft turbine engine control research at NASA Glenn research center. J Aerosp Eng2013;26:422–38.10.1061/(ASCE)AS.1943-5525.0000296Suche in Google Scholar
2. SanjayG. Turbofan engine control system design using the LQG/LTR methodology, NASA-CR–182303.Suche in Google Scholar
3. JamesT, JonathanA,L, IntelligentS. Robust Control of Deteriorated Turbofan Engines via Linear Parameter Varying Quadratic Lypunov Function Design, NASA/TM–2004–213375.Suche in Google Scholar
4. XiW, JieY, DaoliangT, XinfengT, WangH.Application of robust control to the design of jet engine digital controller. J Aerosp Power2005;20:679–83 (in Chinese).Suche in Google Scholar
5. HanzR, AnilS, JonathenLS. Multiplexed predictive control of a large commercial turbofan engine. J Guid Control Dyn2008;31:273–81.10.2514/1.30591Suche in Google Scholar
6. SugiyamaN. Derivation of ABCD system matrices from nonlinear dynamic simulation of jet engines. AIAA 92–3319, 1992.10.2514/6.1992-3319Suche in Google Scholar
7. LuJ, GuoY, ChenX. Establishment of aero-engine state variable model based on linear fitting method. J Aerosp Power2011;26:1172–7.Suche in Google Scholar
8. LuF, HuangJ, SheY. State space modeling based on QPSO hybrid method for aero-engines. J Propul Technology2011;32:722–7.Suche in Google Scholar
9. MantonJH, HuaY. A frequency domain deterministic approach to channel identification. IEEE Signal Process Lett1999;6:323–6.10.1109/97.803436Suche in Google Scholar
10. WahlbergB, HjalmarssonH, StoicaP. On the performance of optimal input signal for frequency response estimation. IEEE Trans Autom Control2012;57:766–71.10.1109/TAC.2011.2166322Suche in Google Scholar
11. MitchellSD, WelshJS. Modeling power transformers to support the interpretation of frequency-response analysis. IEEE Trans Power Deliv2011;26:2705–17.10.1109/TPWRD.2011.2164424Suche in Google Scholar
12. DuY, FangJ, MiaoC. Frequency-domain system identification of an unmanned helicopter based on an adaptive genetic algorithm. IEEE Trans Ind Electron2014;61:870–81.10.1109/TIE.2013.2257135Suche in Google Scholar
13. AgueroJC, YuzAJ, GoodwinGC, DelgadoRA. On the equivalence of time and frequency domain maximum likelihood estimation. Automatica2010;46:260–70.10.1016/j.automatica.2009.10.038Suche in Google Scholar
14. EvansC, FlemingPJ, HillDC, NortonJP, PrattI, ReesD, et al. Application of system identification techniques to aircraft gas turbine engines. Control Eng Pract2001;9:135–48.10.1016/S0967-0661(00)00091-5Suche in Google Scholar
15. EvansC, ChirasN, GuillaumeP, ReesD. Multivariable modeling of gas turbine dynamics[C]//Symposium on Advances in Process control. York England. Advances in Process Control, ASME, 2001:1–8.Suche in Google Scholar
16. CappeO, MoulinesE. On-line expectation maximization algorithm for latent data models. J R Stat Soc Ser B2009;71:593–613.10.1111/j.1467-9868.2009.00698.xSuche in Google Scholar
17. WillsA, NinnessB, GibsonS. Maximum likelihood estimation of state space models from frequency domain data. IEEE Trans Autom Control2009;54:19–33.10.1109/TAC.2008.2009485Suche in Google Scholar
18. DenoeuxT. Maximum likelihood estimation from fuzzy data using the EM algorithm. Fuzzy Sets Syst2011;183:72–91.10.1016/j.fss.2011.05.022Suche in Google Scholar
19. NeeserFD, MasseyJL. Proper complex random processes with applications to information theory. IEEE Trans Inf Theory1993;39:1293–302.10.1109/18.243446Suche in Google Scholar
20. Van Den BosA. The real-complex normal distribution. IEEE Trans Inf Theory1998;44:1670–2.10.1109/18.681349Suche in Google Scholar
21. DebK, SrivastavaS. A genetic algorithm based augmented Lagrangian method for constrained optimization. Comput Optim Appl2012;53:869–902.10.1007/s10589-012-9468-9Suche in Google Scholar
22. ChenC-W, ChenP-C, ChiangW-L. Modified intelligent genetic algorithm-based adaptive neural network control for uncertain structural systems. J Vibr Control2013;19:1333–47.10.1177/1077546312442232Suche in Google Scholar
23. SchoukensJ, PintelonR, GuillaumeP. On the advantages of periodic excitation in system identification [C]//proceedings of SYSID 94. 10th IFAC Symposium on System Identification. Copenhagen and Oxford: Pergamon, 1994:1115–20.10.1016/S1474-6670(17)47857-8Suche in Google Scholar
24. SchroederMR. Synthesis of low peak-factor signals and binary sequences of low auto correlation. IEEE Trans Inf Theory1970;16:85–59.10.1109/TIT.1970.1054411Suche in Google Scholar
25. JawLC, MattinglyJD. Aircraft engine control: design, system analysis, and health monitoring. Reston, VA: American Institute of Aeronautics & Astronautics, 2009.Suche in Google Scholar
26. GuillaumeP, SchoukensJ, PintelonR, KollarI.Crest-factor minimization using nonlinear Chebyshev approximation methods. IEEE Trans Instrum Meas1991;40:982–9.10.1109/19.119778Suche in Google Scholar
27. EvansC, ReesD, HillD. Frequency-domain identification of gas turbine dynamics. IEEE Trans Control Syst Technol1998;6:651–62.10.1109/87.709500Suche in Google Scholar
28. LuJ, GuoY, ChenX. Establishment of aero-engine state variable model based on linear fitting method. J Aerosp Power2011;26:1172–7.Suche in Google Scholar
©2015 by De Gruyter
Artikel in diesem Heft
- Frontmatter
- Numerical Modeling of Unsteady Oil Film Motion Characteristics in Bearing Chambers
- Frequency Domain Identification of Multivariable Model for Aero-Engine using an Improved Maximum Likelihood Method
- Compressor Instability Active Control via Closed-Coupled Valve and Throttle Actuators
- Tab Aspect Ratio Effect on Supersonic Jet Mixing
- The Rotating Cavitation Performance of a Centrifugal Pump with a Splitter-Bladed Inducer under Different Rotational Speed
- Global Needs for Jet-Engine-Steered (JES) Strike Drones vs. Lack of Updated Textbooks to Design 6th Generation UCLASS Due to UCAV Failure
- Lightweight Magnesium Bipolar Plates of Direct NaBH4/H2O2 Fuel Cell for AIP Application
- Multidisciplinary Design Exploration for Sounding Launch Vehicle using Hybrid Rocket Engine in View of Ballistic Performance
Artikel in diesem Heft
- Frontmatter
- Numerical Modeling of Unsteady Oil Film Motion Characteristics in Bearing Chambers
- Frequency Domain Identification of Multivariable Model for Aero-Engine using an Improved Maximum Likelihood Method
- Compressor Instability Active Control via Closed-Coupled Valve and Throttle Actuators
- Tab Aspect Ratio Effect on Supersonic Jet Mixing
- The Rotating Cavitation Performance of a Centrifugal Pump with a Splitter-Bladed Inducer under Different Rotational Speed
- Global Needs for Jet-Engine-Steered (JES) Strike Drones vs. Lack of Updated Textbooks to Design 6th Generation UCLASS Due to UCAV Failure
- Lightweight Magnesium Bipolar Plates of Direct NaBH4/H2O2 Fuel Cell for AIP Application
- Multidisciplinary Design Exploration for Sounding Launch Vehicle using Hybrid Rocket Engine in View of Ballistic Performance
