航空发动机模型基优化控制技术研究
|
摘要
随着对航空发动机性能要求的不断提高，航空发动机控制系统已由传统独立于飞机控制系统的设计方法，向飞行/推进系统综合控制方向发展。航空发动机模型基优化控制作为飞行/推进系统综合控制中的重要内容，可在基本不增加机载硬件的条件下，仅通过控制算法充分挖掘发动机某项或综合性能潜力，提升飞机的飞行品质。本文以航空发动机模型基优化控制为主线，重点对固定翼飞机/发动机综合控制与直升机/涡轴发动机综合控制开展研究，具体包括机载发动机模型研究、综合优化控制、高稳定性控制、综合抗扰控制等内容。
论文首先对固定翼飞机/发动机综合优化控制进行研究。提出了基于相似理论建立线性与非线性相结合的发动机机载复合稳态模型，并将发动机部件蜕化因素纳入考虑，结合改进Kalman滤波器反映发动机性能蜕化时的真实状态。进而，提出了机载复合模型与FSQP算法相结合的发动机多模式性能优化控制，包括最大推力、最小油耗、最低涡轮温度等控制模式，并分别应用于飞机平飞加速、爬升、巡航、大马赫数飞行等飞行任务中，通过数字仿真验证了优化方案的有效性。
其次，论文研究了超机动飞行时发动机的高稳定性控制。建立了基于特征选择及在线攻角预测的发动机喘振裕度估计模型，并基于该模型设计了发动机直接喘振裕度控制与发动机进气畸变预测补偿控制两种高稳定性控制方法，通过实时调整发动机工作点保证发动机的稳定性。其中，前者改变了传统基于传感器的控制形态，将喘振裕度作为被控量直接加入控制回路，取得了更为高效的控制效果，后者则以预测补偿为中心，在发动机原控制回路不变的基础上进行压比损失补偿控制，使得控制体系的实现更为简单。
再者，对于直升机/涡轴发动机综合优化控制，论文采用数据驱动的方法建立了涡轴发动机简化模型与蜕化估计模块作为涡轴发动机机载模型，前者是基于包线划分设计小包线的神经网络映射模块，后者则通过设计先进的MRR-LSSVR算法离线训练，并以多输出特征选择算法筛选最具价值的模型输入来提升估计精度；进而基于上述机载模型设计了最小油耗、串行优化最小油耗、最大功率、最低涡轮温度等直/发综合优化控制模式，在涡轴发动机额定/非额定状态下均实现了优化精度与优化实时性兼优的良好效果。
最后，针对直升机/涡轴发动机综合抗扰控制，以旋翼扭矩为关联量，引入预测控制技术，通过设计滚动优化机制克服由旋翼扭矩测量滞后、发动机动态响应、数据传输等引起的时滞效应，改善系统的抗扰性能。论文先后提出了基于离线预测模型的涡轴发动机非线性模型预测控制与基于在线预测模型的串级+NMPC混合预测控制两种方法，前者的预测模型通过MRR-LSSVR离线算法设计，具有较好的泛化能力，后者的预测模型通过OSP-LSSVR在线算法设计，可根据发动机的实时信息实现模型在线自我更新，两种方法较串级控制均实现了更优的抗扰控制效果，尤其后者具有更好的动静态控制品质、响应速度及鲁棒性。
Traditionally, the aero-engine control system is designed to operate independently of aircraftflight control system. This design philosophy often produces a system where performance is compro-mised greatly to ensure operability and simplicity. Now, by the aid of advances in control, estimationand system modeling techniques, integrated flight/propulsion control (IFPC) has become an inevitabletrend, which can improve overall aircraft performance. As the heart of IFPC, model-based optimalcontrol enables an aero-engine to achieve its full potential using only control algorithms, withoutadding onboard hardware. This paper takes model-based optimal control as the main line, focusing onboth integrated control for fixed-wing aircraft/turbofan engine system and integrated control for hel-icopter/turbo-shaft engine system. In detail, some techniques of onboard engine modeling, perfor-mance seeking control (PSC), high stability engine control (HISTEC) and disturbance rejection con-trol (DRC) are designed respectively, and moreover, some satisfactory results are obtained.
In order to improve real-time capability of PSC for fixed-wing aircraft/turbofan engine system, asteady-state hybrid engine model is proposed based on similarity theory in the whole flight envelope,which takes engine’s working under non-nominal condition into account. An estimation module ofturbofan engine performance deterioration, meanwhile, is developed using improved Kalman filter.After combining the hybrid engine model with the estimation module, an onboard simplified enginemodel is built for PSC. With this model and feasible sequential quadratic programming (FSQP) algo-rithm, PSC for fixed-wing aircraft/turbofan engine system is designed and performed, including themaximum thrust mode, the minimum fuel-consumption mode and the minimum turbine temperaturemode. At last, these PSC modes are applied to various flying missions of aircraft, such as climbing,speeding up, cruising and so on. And simulation results demonstrate that flight performance can beimproved obviously with good real-time ability of optimizing process.
As to HISTEC for super-maneuvering flight mission, two control schemes are put forward basedon engine surge margin (Sm) estimation model, viz. direct Smcontrol and engine inlet distortion com-pensation control. The modeling process consists of two parts: Smbenchmark value model under rou-tine flight condition and Smloss value model under super-maneuvering flight condition. The former isdeveloped using nonlinear fitting method, and its input is fixed by Smfeature selection algorithmbased on least square support vector regression (LSSVR). The latter is obtained utilizing an attackangel predictive model which is established with online sliding parsimonious LSSVR (OSP-LSSVR).Direct Smcontrol for engine, which is different from conventional control scheme, puts Sminto engineclosed-loop and takes Smas controlled variable directly. In this way, direct Smcontrol can exploit po-tential performance of engine more effectively with the decrease of engine stability margin. As wellas direct Smcontrol, engine inlet distortion compensation control also can achieve a good control level based on the prediction and compensation of engine inlet distortion. To be more important, engineinlet distortion compensation control is able to correct turbine expansion ratio command on engineoriginal closed-loop, which can be realized easier than direct Smcontrol.
PSC for helicopter/turbo-shaft engine system is another important research topic. As the same asPSC for fixed-wing aircraft/turbofan engine system, it is necessary to design an onboard simplifiedturbo-shaft engine model with adaptive ability. In this paper, both the simplified engine model and theestimation module of engine performance deterioration are put forward with data-based method. Thesimplified engine model is performed using block partition of flight envelop, and engine mappingmodule in every block is trained by BP Neural Network; the estimation module of engine perfor-mance deterioration is trained offline using muti-input muti-output recursive reduced LSSVR(MRR-LSSVR), and the input of model is determined by feature selection algorithm. Considering thecomprehensive actions of multi-output variables to select support vectors, MRR-LSSVR is proposedto solve multi-output problems, and holds better sparseness and generalization performance thancommon algorithms due to combining reduced technique with iterative strategy. On the basis of thesimplified engine model and the estimation module, PSC for helicopter/turbo-shaft engine system isdesigned with FSQP algorithm, including the minimum fuel-consumption mode, the cascaded mini-mum fuel-consumption mode, the maximum power mode and the minimum turbine temperature mode.At last, the above control modes are carried out respectively based on UH-60A helicopter/T700tur-bo-shaft engine simulation platform, and simulation results show that the proposed optimizationscheme is feasible and valid.
Finally, constrained nonlinear model predictive control (NMPC) is applied to the engine controlsystem to improve dynamic disturbance rejection ability of integrated aircraft/turbo-shaft engine sys-tem. Taking the torque of helicopter rotor as the related variable, NMPC utilizes a predictive modeland a rolling optimizer to solve the time-lag effect caused by measuring lag of rotor torque, dynamicresponse of engine, data transmission and so on. In this essay, two kinds of NMPC schemes for tur-bo-shaft engine are put forward, i.e. NMPC based on offline predictive model and cascade+NMPChybrid predictive control based on online predictive model. As for the former, the predictive enginemodel is trained by adopting MRR-LSSVR algorithm, and the rolling optimizer is realized using SQPalgorithm libray. In comparison with cascade controller, NMPC based on offline predictive model candecrease the droop or overshoot of rotor speed remarkably during helicopter’s maneuver flight. Butfor the latter, OSP-LSSVR algorithm is used for the predictive engine model, and gives the model aself-renewal capacity and higher accuracy. Cascade+NMPC hybrid predictive control, therefore, hasbetter dynamic disturbance rejection ability and robustness in a large flight envelop, and enables thehelicopter to exhibit much greater maneuverability than the conventional control method.
论文首先对固定翼飞机/发动机综合优化控制进行研究。提出了基于相似理论建立线性与非线性相结合的发动机机载复合稳态模型，并将发动机部件蜕化因素纳入考虑，结合改进Kalman滤波器反映发动机性能蜕化时的真实状态。进而，提出了机载复合模型与FSQP算法相结合的发动机多模式性能优化控制，包括最大推力、最小油耗、最低涡轮温度等控制模式，并分别应用于飞机平飞加速、爬升、巡航、大马赫数飞行等飞行任务中，通过数字仿真验证了优化方案的有效性。
其次，论文研究了超机动飞行时发动机的高稳定性控制。建立了基于特征选择及在线攻角预测的发动机喘振裕度估计模型，并基于该模型设计了发动机直接喘振裕度控制与发动机进气畸变预测补偿控制两种高稳定性控制方法，通过实时调整发动机工作点保证发动机的稳定性。其中，前者改变了传统基于传感器的控制形态，将喘振裕度作为被控量直接加入控制回路，取得了更为高效的控制效果，后者则以预测补偿为中心，在发动机原控制回路不变的基础上进行压比损失补偿控制，使得控制体系的实现更为简单。
再者，对于直升机/涡轴发动机综合优化控制，论文采用数据驱动的方法建立了涡轴发动机简化模型与蜕化估计模块作为涡轴发动机机载模型，前者是基于包线划分设计小包线的神经网络映射模块，后者则通过设计先进的MRR-LSSVR算法离线训练，并以多输出特征选择算法筛选最具价值的模型输入来提升估计精度；进而基于上述机载模型设计了最小油耗、串行优化最小油耗、最大功率、最低涡轮温度等直/发综合优化控制模式，在涡轴发动机额定/非额定状态下均实现了优化精度与优化实时性兼优的良好效果。
最后，针对直升机/涡轴发动机综合抗扰控制，以旋翼扭矩为关联量，引入预测控制技术，通过设计滚动优化机制克服由旋翼扭矩测量滞后、发动机动态响应、数据传输等引起的时滞效应，改善系统的抗扰性能。论文先后提出了基于离线预测模型的涡轴发动机非线性模型预测控制与基于在线预测模型的串级+NMPC混合预测控制两种方法，前者的预测模型通过MRR-LSSVR离线算法设计，具有较好的泛化能力，后者的预测模型通过OSP-LSSVR在线算法设计，可根据发动机的实时信息实现模型在线自我更新，两种方法较串级控制均实现了更优的抗扰控制效果，尤其后者具有更好的动静态控制品质、响应速度及鲁棒性。
Traditionally, the aero-engine control system is designed to operate independently of aircraftflight control system. This design philosophy often produces a system where performance is compro-mised greatly to ensure operability and simplicity. Now, by the aid of advances in control, estimationand system modeling techniques, integrated flight/propulsion control (IFPC) has become an inevitabletrend, which can improve overall aircraft performance. As the heart of IFPC, model-based optimalcontrol enables an aero-engine to achieve its full potential using only control algorithms, withoutadding onboard hardware. This paper takes model-based optimal control as the main line, focusing onboth integrated control for fixed-wing aircraft/turbofan engine system and integrated control for hel-icopter/turbo-shaft engine system. In detail, some techniques of onboard engine modeling, perfor-mance seeking control (PSC), high stability engine control (HISTEC) and disturbance rejection con-trol (DRC) are designed respectively, and moreover, some satisfactory results are obtained.
In order to improve real-time capability of PSC for fixed-wing aircraft/turbofan engine system, asteady-state hybrid engine model is proposed based on similarity theory in the whole flight envelope,which takes engine’s working under non-nominal condition into account. An estimation module ofturbofan engine performance deterioration, meanwhile, is developed using improved Kalman filter.After combining the hybrid engine model with the estimation module, an onboard simplified enginemodel is built for PSC. With this model and feasible sequential quadratic programming (FSQP) algo-rithm, PSC for fixed-wing aircraft/turbofan engine system is designed and performed, including themaximum thrust mode, the minimum fuel-consumption mode and the minimum turbine temperaturemode. At last, these PSC modes are applied to various flying missions of aircraft, such as climbing,speeding up, cruising and so on. And simulation results demonstrate that flight performance can beimproved obviously with good real-time ability of optimizing process.
As to HISTEC for super-maneuvering flight mission, two control schemes are put forward basedon engine surge margin (Sm) estimation model, viz. direct Smcontrol and engine inlet distortion com-pensation control. The modeling process consists of two parts: Smbenchmark value model under rou-tine flight condition and Smloss value model under super-maneuvering flight condition. The former isdeveloped using nonlinear fitting method, and its input is fixed by Smfeature selection algorithmbased on least square support vector regression (LSSVR). The latter is obtained utilizing an attackangel predictive model which is established with online sliding parsimonious LSSVR (OSP-LSSVR).Direct Smcontrol for engine, which is different from conventional control scheme, puts Sminto engineclosed-loop and takes Smas controlled variable directly. In this way, direct Smcontrol can exploit po-tential performance of engine more effectively with the decrease of engine stability margin. As wellas direct Smcontrol, engine inlet distortion compensation control also can achieve a good control level based on the prediction and compensation of engine inlet distortion. To be more important, engineinlet distortion compensation control is able to correct turbine expansion ratio command on engineoriginal closed-loop, which can be realized easier than direct Smcontrol.
PSC for helicopter/turbo-shaft engine system is another important research topic. As the same asPSC for fixed-wing aircraft/turbofan engine system, it is necessary to design an onboard simplifiedturbo-shaft engine model with adaptive ability. In this paper, both the simplified engine model and theestimation module of engine performance deterioration are put forward with data-based method. Thesimplified engine model is performed using block partition of flight envelop, and engine mappingmodule in every block is trained by BP Neural Network; the estimation module of engine perfor-mance deterioration is trained offline using muti-input muti-output recursive reduced LSSVR(MRR-LSSVR), and the input of model is determined by feature selection algorithm. Considering thecomprehensive actions of multi-output variables to select support vectors, MRR-LSSVR is proposedto solve multi-output problems, and holds better sparseness and generalization performance thancommon algorithms due to combining reduced technique with iterative strategy. On the basis of thesimplified engine model and the estimation module, PSC for helicopter/turbo-shaft engine system isdesigned with FSQP algorithm, including the minimum fuel-consumption mode, the cascaded mini-mum fuel-consumption mode, the maximum power mode and the minimum turbine temperature mode.At last, the above control modes are carried out respectively based on UH-60A helicopter/T700tur-bo-shaft engine simulation platform, and simulation results show that the proposed optimizationscheme is feasible and valid.
Finally, constrained nonlinear model predictive control (NMPC) is applied to the engine controlsystem to improve dynamic disturbance rejection ability of integrated aircraft/turbo-shaft engine sys-tem. Taking the torque of helicopter rotor as the related variable, NMPC utilizes a predictive modeland a rolling optimizer to solve the time-lag effect caused by measuring lag of rotor torque, dynamicresponse of engine, data transmission and so on. In this essay, two kinds of NMPC schemes for tur-bo-shaft engine are put forward, i.e. NMPC based on offline predictive model and cascade+NMPChybrid predictive control based on online predictive model. As for the former, the predictive enginemodel is trained by adopting MRR-LSSVR algorithm, and the rolling optimizer is realized using SQPalgorithm libray. In comparison with cascade controller, NMPC based on offline predictive model candecrease the droop or overshoot of rotor speed remarkably during helicopter’s maneuver flight. Butfor the latter, OSP-LSSVR algorithm is used for the predictive engine model, and gives the model aself-renewal capacity and higher accuracy. Cascade+NMPC hybrid predictive control, therefore, hasbetter dynamic disturbance rejection ability and robustness in a large flight envelop, and enables thehelicopter to exhibit much greater maneuverability than the conventional control method.
引文
[1] L. C. Jaw, J. D. Mattingly. Aircraft engine controls, design, system analysis, and health moni-toring [M]. Virginia: American Institute of Aeronautics and Astronautics, Inc.,2009.
[2] L. C. Jaw, S. Garg. Propulsion control technology development in the United States: a histori-cal perspective [R]. NASA/TM-2005-213978,2005.
[3]刘大响.航空发动机技术的发展和建议[J].中国工程科学,1999,1(2):24-29.
[4]孙健国.面向21世纪航空动力控制展望[J].航空动力学报,2001,16(2):97-102.
[5] C. A. Skira, M. Agnello. Control system for the next century’s fighter engines [J]. Transactionsof the ASME Journal of Engineering for Gas Turbines and Power,1992,114(4):749-754.
[6] S. Garg. NASA Glenn Research in Controls and Diagnostics for Intelligent Aerospace Propul-sion Systems [R]. NASA/TM-2005-214036,2005.
[7] J. S. Litt, D. L. Simon, S. Garg, et al. A Survey of intelligent control and health managementtechnologies for aircraft propulsion systems [R]. NASA/TM-2005-213622,2005.
[8] K. Lietzau, A. Kreiner. Model based control concepts for jet engines [C]. Proceedings ofASME Turbo Expo, New Orleans, Louisiana, USA,2001.
[9] W. Troha, K. Kerner, G. Butler. Development status of the U.S. army small heavy fuel engine(SHFE) VAATE program [C]. AHS International63rd Annual Forum Proceedings-AmericanHelicopter Society, USA,2007.
[10]方昌德.航空发动机的发展前景[J].航空发动机,2004,30(1):1-5.
[11] S. Garg. Introduction to advanced engine control concepts [C]. Fundamentals of Aircraft En-gine Control Design Course, Oklahoma City,2007.
[12] G. B. Gilyard, J. S.Orme. Performance seeking control: program overview and future directions[R]. AIAA-93-3765-CP,1993.
[13] V. V. R. Silva, W. Khatib, P. J. Fleming. Performance optimization of gas turbine engine [J].Engineering Applications of Artificial Intelligence,2005,18(5):575-583.
[14] J. D. Chisholm. In-flight optimization of the total propulsion system[R]. AIAA-92-3744,1992.
[15] J. A. Ralph. Advanced control for airbreathing engines (volume1)[R]. NASA-CR-189203,1993.
[16] T. Brotherton, A. Volponi, R. Luppold, et al. eSTORM: Enhanced self-tuning on-boardreal-time engine model [C]. Proceedings of IEEE Aerospace Conference, Montana,2003.
[17] A. Vloponi. Enhanced self-tuning on-board real-time model (eSTORM) for aircraft engineperformance health tracking [R]. NASA/CR-2008-215272,2008.
[18] R. W. Vizzini. Integrated flight/propulsion control system considerations for future aircraft ap-plication [J]. Journal of Engineering for Gas Turbines and Power,1985,107(4):833-837.
[19] J. S. Orme, T. Conners. Supersonic flight test results of a performance seeking control algo-rithm on a NASA F-15aircraft [R]. AIAA-94-3210,1994.
[20] J. R. Webster. Opportunities for adaptive optimization of gas turbine systems [C]. Proceedingsof SPIE4763, Giens, France,2002.
[21] R. H. Smith, J. D. Chisholm, J. F. Stewart. Optimizing aircraft performance with adaptive, in-tegrated flight/propulsion control [J]. Journal of Engineering for Gas Turbines and Power,1991,113(1):87-94.
[22] J. Orme, G. Gilyard. Subsonic flight test evaluation of a propulsion system parameter estima-tion process for the F100Engine [R]. AIAA-92-3745,1992.
[23] F. W. Burcham Jr., E. A. Haering. Highly integrated digital engine control system on an F-15airplane [J]. AIAA-84-1259,1984.
[24] W. A. Yonke, R. J. Landy. HIDEC adaptive engine control system flight evaluation result [R].ASME-87-Gt-252,1987.
[25] S. G. Nobbs, S. W. Jacobs, D. J. Donahue. Development of the full-envelop performance seek-ing control algorithm [R]. AIAA-92-3745,1992.
[26] J. S. Orme, G. B. Gilyard. Preliminary supersonic flight test evaluation of performance seekingcontrol [R]. AIAA-93-1821,1992.
[27] G. Gilyard, J. Orme. Subsonic flight test evaluation of a performance seeking control algorithmon an F-15airplane [R]. AIAA-92-3743,1992.
[28]李松林.飞行/推进系统综合控制—性能寻优自适应控制研究[D].南京:南京航空航天大学博士学位论文,1998.
[29]袁春飞,孙健国,熊智,等.推进系统优化控制模式研究[J].航空动力学报,2003,19(1):159-163.
[30]袁春飞,孙健国,熊智,等.飞/推综合控制模式亚音速半物理仿真试验[J].推进技术,2003,24(4):353-356.
[31]王芳,樊思齐,吴丹,等.MAPS方法在航空发动机性能寻优控制中的应用[J].航空动力学报,2005,20(3):503-507.
[32] Y. Zhu, S. Fan, H. Li, et al. A hybrid optimization based on linear programming and mod-el-assisted pattern search method in PSC [C].44th AIAA Aerospace Sciences Meeting and Ex-hibit, Reno, Nevada,2006.
[33]朱玉斌,樊思齐,李华聪,等.航空发动机性能寻优控制混合优化算法[J].航空动力学报,2006,21(2):422-426.
[34]任新宇,杨育武,樊思齐.推进系统综合性能寻优控制研究[J].推进技术,2010,31(1):61-64.
[35]何黎明,樊丁.利用SQP控制涡扇发动机加速过程的多目标最优化研究[J].航空动力学报,2001,16(2):179-181.
[36]陈光明,樊丁.SQP方法在航空发动机加速过程控制中的应用研究[J].航空动力学报,1999,14(3):313-316.
[37]孙丰诚,孙健国.基于序列二次规划算法的发动机性能寻优控制[J].航空动力学报,2005,20(5):862-867.
[38]赵琳,樊丁,陕薇薇.航空发动机过渡态全局寻优控制方法研究[J].航空动力学报,2007,22(7):1200-1203.
[39]张海波,孙健国.可行SLP法在发动机在线优化中的应用[J].航空学报,2010,31(4):663-670.
[40]戚学锋,樊丁,陈耀楚,等.基于FSQP算法的涡扇发动机多变量最优加速控制[J].推进技术,2004,25(3):233-236.
[41] C. T. Lawrence, A. L. Tits. A computational efficient feasible sequential quadratic program-ming algorithm [J]. SIAM J.Optim,2001,11(4):1092-1118.
[42] Z. Zhu. A simple feasible SQP algorithm for inequality constrained optimization [J]. AppliedMathematics and Computation,2006,182(2):987-998.
[43]刘大响,叶培良,胡骏,等.航空燃气涡轮发动机稳定性设计与评定技术[M].北京:航空工业出版社,2004.
[44] J. C. DeLaat, R. D. Southwick, G. W. Gallops. High stability engine control (HISTEC)[R].NASA/TM-1996-107272,1996.
[45] J. S. Orme, J. C. DeLaat, R. D. Southwick, et al. Development and testing of a high stabilityengine control (HISTEC) system [R]. NASA/TM-1998-206562,1998.
[46] J. C. DeLaat, R. D. Southwick, G. W. Gallops, et al. High stability engine control (HISTEC):flight demonstration results [R]. NASA/TM-1998-208566,1998.
[47] R. D. Southwick, G. W. Gallops, L. J. Kerr, et al. High stability engine control (HISTEC): flighttest results [R]. NASA/TM-1998-208481,1998.
[48] J. C. DeLaat, R. D. Southwick, G. W. Gallops. The high stability engine control (HISTEC) pro-gram: flight demonstration phase [R]. NASA/TM-1998-208482,1998.
[49] Y. Liu, M. Dhingra, J. V. R. Prasad. Active compressor stability management via a stall margincontrol mode [J]. Journal of Engineering for Gas Turbines and Power,2010,132(5):1-10.
[50] M. Inoue, M. Kuroumaru, T. Tanino, et al. Propagation of multiple short-length-scale stall cellsin axial compressor rotor [J]. Journal of Turbomachinery,2000,122(1):45-54.
[51] M. Dhingra, Y. Neumeier, J. V. R. Prasad, et al. A stochastic model for a compressor stabilitymeasure [J]. Journal of Engineering for Gas Turbines and Power,2007,129(3):730-737.
[52]王立峰,张津.超机动飞行推进系统稳定性控制研究[J].推进技术,2000,21(4):1-4.
[53] L. Wang, Z. Li, Z. Lin. Enhancing the fidelity of post-stall flight simulation using detailedpropulsion system model [R]. AIAA-2004-4810,2004.
[54]袁春飞.飞行/推进系统综合优化控制模式及其关键技术[D].南京:南京航空航天大学博士学位论文,2004.
[55]陈霆昊,张海波,孙健国.基于攻角预测模型的航空发动机高稳定性控制[J].航空动力学报,2010,25(7):1676-1682.
[56] B. J. Smith, R. D. Zagranski. Next generation control system for helicopter engines [C]. AHSInternational57th Annual Forum Proceedings, DC, Washington,2001.
[57] B. J. Smith, R. D. Zagranski. Closed loop bench testing of the next generation control systemfor helicopter engines [C]. AHS International58th Annual Forum Proceedings, Montreal, Can-ada,2002.
[58] M. J. Scott. Performance benefits for a turboshaft engine using nonlinear engine control [R].NASA-20050192151,2005.
[59]姚文荣.涡轴发动机/旋翼综合建模控制及优化研究[D].南京:南京航空航天大学博士学位论文,2008.
[60]孙立国.直升机/发动机综合仿真平台下的发动机性能优化及控制[D].南京:南京航空航天大学硕士学位论文,2010.
[61]周剑波.变几何涡轴发动机综合控制技术研究[D].南京:南京航空航天大学博士学位论文,2011.
[62]王志峰,姜广伦,安锦文.直升机飞行/推进系统综合模型的建立[J].飞行力学,1999,17(4):17-21.
[63] W. A. Kuczynski, D. E. Cooper, W. J. Twomey, et al. The influence of engine/fuel control de-sign on helicopter dynamics and handling qualities [J]. Journal of the American Helicopter So-ciety,1980,25:26-34.
[64] W. J. Twomey, E. H. Ham. Review of engine/airframe/drive train dynamic interface develop-ment problems [R]. USARTL-TR-78-13,1978.
[65] D. A. Richardson, J. R. Alwang. Engine/airframe/drive train dynamic interface documentation[R]. USARTL-TR-78-11,1978.
[66] J. F. Needham, D. Banerjee. Engine/airframe/drive train dynamic interface documentation [R].USARTL-TR-78-12,1978.
[67] H. W. Hanson, R. W. Balke, B. D. Edwards, et al. Engine/airframe/drive train dynamic inter-face documentation [R]. USARTL-TR-78-15,1978.
[68] T. Morrison, R. D. Zagranski, A. H. White. Adaptive fuel control feasibility investigation [R].USAAVRADCOM-TR-83-D-1,1983.
[69] T. W. Yates. Adaptive fuel control feasibility investigation [R]. USAAVRADCOM-TR-82-D-39,1983.
[70] J. J. Howlett, T. Momson, R. D. Zagranski. Adaptive fuel control for helicopter applications [J].Journal of the American Helicopter Society,1984,29(4):43-54.
[71] D. H. Sweet. Flight testing of the Chandler Evans adaptive fuel control on the S-76A helicopter[C]. AHS International45th Annual Forum, Boston, USA,1989.
[72] W. K. Stephen. Flight tests of adaptive fuel control and decoupled rotor speed control systems[C]. AHS International45th Annual Forum, Boston, USA,1989.
[73] M. C. Desai, C. Crainic. Adaptive thermodynamic engine model for the next generation controlsystem for helicopter engines [C]. AHS International58th Annual Forum Proceeding, Montreal,Canada,2002.
[74]陈仁良.直升机飞行动力学数学建模及机动性研究[D].南京:南京航空航天大学博士学位论文,1998.
[75]孙传伟.直升机飞行动力学模型与飞行品质评估[D].南京:南京航空航天大学博士学位论文,2002.
[76] L. D. Corliss. A helicopter handling-qualities study of the effects of engine response characte-ristics, height-control dynamics, and excess power on nap-of-the-earth operations [R]. NASA-CP-2219,1982.
[77] L. D. Corliss, C. L. Blanken, K. Nelson. Effects of rotor inertia and RPM control on helicopterhandling qualities [R]. AIAA-83-2070,1983.
[78] L. D. Corliss. The effects of engine and height-control characteristics on helicopter handlingqualities [J]. Journal of the American Helicopter Society.1983,28(3):56-62.
[79]杨征山,李胜泉,章霖官.涡轴发动机功率涡轮转速控制回路方案研究[J].航空发动机,2005,31(2):46-50.
[80] J. J. Curran. T700fuel and control system [J]. Journal of the American Helicopter Society,1974,19(3):17-27.
[81]张海波,孙健国,孙立国.一种涡轴发动机转速抗扰控制器设计及应用[J].航空动力学报,2010,25(4):943-950.
[82]孙立国,孙健国,张海波,等.基于支持向量回归机的发动机/直升机扭矩超前控制[J].航空动力学报,2011,26(3):680-686.
[83] J. V. R. Prasad, A. J. Calise, S. Hajra, et al. Neural net based adaptive nonlinear control of aturboshaft engine [C]. AHS International57th Annual Forum, DC, Washington, USA,2001.
[84] W. H. Pfeil, M. Athans, S. H. Austin. Multi-variable control for the GE T700engine using theLQG/LTR design methodology [C]. American Control Conference, Seattle, Washington, USA,1986.
[85] W. H. Pfeil, G. D. L. Reyes, G. A. Bobula. The application of LQR synthesis techniques to theturboshaft engine control problem [R]. AIAA-84-1455,1984.
[86] D. G. C. Ruttledge, E. J. Hammon, D. R. Gilmore. A rotorcraft flight propulsion control inte-gration study [R]. NASA-CR-179574,1986.
[87] G. D. L. Reyes, D. R. Gouchoe. The Design of a turboshaft speed governor using modern con-trol techniques [R]. NASA-CR-175046,1986.
[88] J. R. Mihaloew, M. G. Ballin, D. C. G. Ruttledge. Rotorcraft flight propulsion control integra-tion: an eclectic design concept [R]. NASA-TP-2815,1988.
[89]孙立国,王日先,孙健国,等.基于ADRC的涡轴发动机/直升机子系统抗扰控制[J].北京航空航天大学学报,2011,37(10):1312-1316.
[90]张海波,孙立国,孙健国.直升机/涡轴发动机综合系统鲁棒抗扰控制设计[J].航空学报,2010,31(5):883-892.
[91]姚文荣,孙健国.涡轴发动机非线性模型预测控制[J].航空学报,2008,29(4):776-780.
[92] J. Sun, V.Vasilyev, B.Ilyasov. Advanced multivariable control system of aeroengines [M].北京:北京航空航天大学出版社,2005.
[93] E. J. Tich, P. D. Shaw, D. F. Berg, et al. Performance seeking control for cruise optimization infighter aircraft [R]. AIAA-87-1929,1987.
[94] J. F. Stewart. Integrated flight propulsion control research result using the NASA F-15HIDECflight research facility [R]. AIAA-92-4106,1992.
[95] R. E. Fennell. Integrated airframe propulsion control [R]. AIAA-83-2563,1983.
[96] S. Garg. Partitioning of centralized integrated flight/propulsion control design for decentralizedimplementation [J]. IEEE Trans. on Control Systems Technology,1993,1(2):93-100.
[97] S. Garg, D. L. Mattern. Application of an integrated methodology for propulsion and airframecontrol design to a STOVL aircraft [R]. AIAA-94-3611-CP,1994.
[98] D. G. Bates, S. L. Gately, I. Postlethwaite. Integrated flight and propulsion control system de-sign using H∞loop-shaping techniques [C]. Proceedings of the38th Conference on Decision&Control, Phoenix, Arizona USA,1999.
[99] W. K. Neighbors, S. M. Rock. Integrated flight/propulsion control specifications: accountingfor two-way coupling [R]. AIAA-94-3613-CP,1994.
[100]王曦,林永霖,吴永康.H∞控制在飞行/推进综合控制系统中的应用[J].航空动力学报,2004,19(5):698-702.
[101] P. D. Shaw, S. M. Rock, W. S. Fisk. Design methods for integrated control systems [R]. AF-WAL-TR-88-2061,1988.
[102] D. L. Mattern, S. Garg, R. E. Bullard. Integrated flight/propulsion control system design basedon a decentralized, hierarchical approach [R]. AIAA-89-3519,1989.
[103]单贵平.航空发动机建模与推力矢量控制研究[D].南京:南京航空航天大学硕士学位论文,2007.
[104]刘薇.飞/推综合控制系统仿真平台开发[D].南京:南京航空航天大学硕士学位论文,2006.
[105]孙丰诚.航空发动机性能寻优控制控制技术研究[D].南京:南京航空航天大学硕士学位论文,2007.
[106]廉筱纯,吴虎.航空发动机原理[M].西安:西北工业大学出版社,2005.
[107]杨刚,孙健国,李秋红.航空发动机控制系统中的增广LQR方法[J].航空动力学报,2004,19(1):153-158.
[108]杨刚.多变量鲁棒控制在涡扇发动机中的应用研究与实验验证[D].南京:南京航空航天大学博士学位论文,2004.
[109] F. D. Mueller, S. G. Nobbs. Dual engine application of the performance seeking control algo-rithm [R]. AIAA-93-1822,1993.
[110]王前宇.基于控制结构优化的航空发动机多变量鲁棒控制研究[D].南京:南京航空航天大学硕士学位论文,2010.
[111]王信德.某型涡扇发动机自适应建模研究[D].南京:南京航空航天大学硕士学位论文,2003.
[112]陈霆昊.发动机机载自适应模型与高稳定性控制技术研究[D].南京:南京航空航天大学硕士学位论文,2010.
[113] K. George. Adaptive performance seeking control using fuzzy model reference learning controland positive gradient control[R]. AIAA-97-3191,1997.
[114]施光燕,董加礼.最优化方法[M].北京:高等教育出版社,2005.
[115]杨冰.实用最优化方法及计算机程序[M].哈尔滨:哈尔滨船舶工程学院出版社,1994.
[116]袁亚湘,孙文瑜.最优化理论与方法[M].北京:北京科学技术出版社,2001.
[117]阳明盛,罗长童.最优化原理、方法及求解软件[M].北京:科学出版社,2006.
[118]申培萍.全局优化方法[M].北京:科学出版社,2006.
[119]姚文荣.涡轴发动机/旋翼综合建模、控制及优化研究[D].南京:南京航空航天大学博士论文,2008.
[120]杨刚,孙健国,姚华.航空发动机H∞/LTR控制试验验证[J].航空学报,2006,27(5):773-777.
[121] J. A. K. Suykens, T. Van Gestel, J. De Brabanter, et al. Least squares support vector machines[M]. Singapore: World Scientific,2002.
[122] E. L. Goldsmith, J. Seddon. Practical intake aero dynamic design [M]. US: AIAA and Black-well Scientific Publications,1993.
[123] F. Ojeda, J. A. K. Suykens, B. D. Moor. Low rank updated LS-SVM classifiers for fast variableselection [J]. Neural Networks,2008,21(2-3):437-449.
[124] E. K. Tang, P. Suganthan, X. Yao. Gene selection for microarray data based on least squaressupport vector machine [J]. BMC Bioinformatics,2006,7(95):1471-2105.
[125] X. Zhou, K. Z. Mao. LS bound based gene selection for DNA microarray data [J]. Bioinfor-matics,2005,21(8):1559-1564.
[126] I. Guyon, J. Weston, S. Barnhill, et al. Gene selection for cancer classification using supportvector machines [J]. Machine Learning,2002,46(1-3):389-422.
[127] J. A. K. Suykens, J. Vandewalle. Least squares support vector machine classifiers [J]. NeuralProcessing Letter,1999,9(3):293-300.
[128] Y. Zhao, J. Sun, J. Wang. Online parsimonious least squares support vector regression and itsapplication to sensor analytical redundancy for aeroengines [J]. Transactions of Nanjing Uni-versity of Aeronautics&Astronautics,2009,26(4):280-287.
[129]赵永平,孙健国.基于滚动窗法最小二乘支持向量机的稳健预测模型[J].模式识别与人工智能,2008,21(1):1-5.
[130]黄红选,韩继业.数学规划[M].北京:清华大学出版社,2006.
[131] W. G. Steenken, J. G. Williams, K. R. Walsh. Inlet flow characteristics during rapid maneuversfor an F/A-18A airplane [R]. NASA/TM-1999-206587,1999.
[132] F. L. Lewis. Applied optimal control and estimation: digital design and implementation [M].Englewood Cliffs, New Jersey: Prentice Hall,1992.
[133] D. E. Miller, M. Rossi. Simultaneous stabilization with near optimal LQR performance [J].IEEE Trans. Automatic Control,2001,46(10):1543-1555.
[134]杨刚,姚华.实用航空发动机LQR权阵选取方法[J].南京航空航天大学学报,2006,38(4):403-407.
[135]邹先全.涡轴发动机自适应模型的建立与鲁棒控制[D].南京:南京航空航天大学硕士论文,2009.
[136] S. M. Rock, K. Neighbors. Integrated flight/propulsion control for helicopters [C]. AHS Inter-national49th Annual Forum, St. Louis, Missouri,1993.
[137] R. H. Luppold, G. W. Gallops, L. J. Kerr, et al. Estimating in-flight engine performance varia-tions using Kalman Filter concepts [R]. AIAA-89-2584,1989.
[138]邹先全,孙健国,张海波.旋翼/涡轴发动机的自适应模型[J].北京航空航天大学学报,2009,35(10):1192-1196.
[139] T. Kobayashi, D. L. Simon, J. S. Litt. Application of a constant gain extended Kalman Filter forin-flight estimation of aircraft engine performance parameters [R]. NASA/TM2005-213865.
[140]陈恬,孙健国.粗糙集与神经网络在航空发动机气路故障诊断中的应用[J].航空动力学报,2006,21(1):207-212.
[141]徐启华,师军.基于支持向量机的航空发动机故障诊断[J].航空动力学报,2005,20(2):298-302.
[142] Y. Zhao, J. Sun. Recursive reduced least squares support vector regression [J]. Pattern Recogni-tion,2009,42(5):837-842.
[143] K. M. Lin, C. J. Lin. A study on reduced support vector machines [J]. IEEE Trans. NeuralNetworks,2003,14(6):1449-1459.
[144] L. Jiao, L. Bo, L. Wang. Fast sparse approximation for least squares support vector machine [J],IEEE Trans. Neural Networks,2007,18(3):685-697.
[145] F. Lu, J. Huang, X. Qiu. Application of multi-outputs LSSVR by PSO to the aero-engine model[J]. Journal of Systems Engineering and Electronics,2009,20(5):1153-1158.
[146]李丽娟.最小二乘支持向量机建模及预测控制算法研究[D].杭州:浙江大学博士论文,2008.
[147]姚文荣,孙健国.基于在线可变导叶角的涡轴发动机优化控制[J].航空动力学报,2007,22(11):1930-1934.
[148]周剑波,时瑞军,张秋贵,等.进口导叶可调的涡轴发动机性能寻优控制模式[J].航空动力学报,2011,26(10):2371-2375.
[149] T. Iwata, S. M. Rock. Benefits of variable rotor speed in integrated helicopter/engine control [J].AIAA-93-3851-CP,1993.
[150] R. T. N. Chen. An exploratory investigation of the flight dynamics effects of rotor RPM varia-tions and rotor state feedback in hover [R]. NASA-TM-103968,1992.
[151] G. Tatsuhiko, K. Kawakami. Variable rotor speed transmission with high speed traction drive[C]. AHS International55th Annual Forum Proceeding, Montreal, Canada,1999.
[152] C. G. Schaefer, F. H. Lutze. Enhanced energy maneuverability for attack helicopters using con-tinuous, variable rotor speed control [C]. AHS International47th Annual Forum Proceeding,Phoenix, AZ,1991.
[153] Q. S. Joe, T. A. Badgwell. A survey of industrial model predictive control technology [J]. Con-trol Engineering Practice,2003,11(7):733-764.
[154] D. Q. Mayne, J. B. Rawlings, C. V. Rao, et al. Constrained model predictive control: stabilityand optimality [J]. Automatica,2000,36(6):789-814.
[155] L. Magni, H. Nijmeijer, A. J. Schaft. A receding horizon approach to the nonlinear H∞controlproblem [J]. Automatica,2001,37(3):429-435.
[156] J. Mu, D. Rees. Approximate model predictive control for gas turbine engines [C]. Proceedingof the2004American Control Conference, Boston, Massachusetts,2004.
[157] B. J. Brunell. Model adaptation and nonlinear model predictive control of an aircraft engine [C].Proceeding of ASME Turbo Expo2004: Power for Land, Sea, and Air, Vienna, Austria,2004.
[158] H. Richter, A. Singaraju, J. S. Litt. Multiplexed predictive control of a large commercial turbo-fan engine [J]. Journal of Guidance, Control and Dynamics,2008,31(2):273-181.
[159] B. G. Vroemen, H. A. V. Essen, A. A. V. Steenhoven, et al. Nonlinear model predictive controlof a laboratory gas turbine installation [J]. Journal of Engineering for Gas Turbines and Power,1999,121:629-634.
[160]席裕庚.预测控制[M].北京:国防科技出版社,1993.
[161]李胜泉,杨征山,孙健国.基于内模原理的涡轴发动机状态反馈控制方法[J].航空动力学报,2007,22(5):829-832.
[162]韩京清.自抗扰控制技术[J].前沿科学,2007,1(1):24-31.
[2] L. C. Jaw, S. Garg. Propulsion control technology development in the United States: a histori-cal perspective [R]. NASA/TM-2005-213978,2005.
[3]刘大响.航空发动机技术的发展和建议[J].中国工程科学,1999,1(2):24-29.
[4]孙健国.面向21世纪航空动力控制展望[J].航空动力学报,2001,16(2):97-102.
[5] C. A. Skira, M. Agnello. Control system for the next century’s fighter engines [J]. Transactionsof the ASME Journal of Engineering for Gas Turbines and Power,1992,114(4):749-754.
[6] S. Garg. NASA Glenn Research in Controls and Diagnostics for Intelligent Aerospace Propul-sion Systems [R]. NASA/TM-2005-214036,2005.
[7] J. S. Litt, D. L. Simon, S. Garg, et al. A Survey of intelligent control and health managementtechnologies for aircraft propulsion systems [R]. NASA/TM-2005-213622,2005.
[8] K. Lietzau, A. Kreiner. Model based control concepts for jet engines [C]. Proceedings ofASME Turbo Expo, New Orleans, Louisiana, USA,2001.
[9] W. Troha, K. Kerner, G. Butler. Development status of the U.S. army small heavy fuel engine(SHFE) VAATE program [C]. AHS International63rd Annual Forum Proceedings-AmericanHelicopter Society, USA,2007.
[10]方昌德.航空发动机的发展前景[J].航空发动机,2004,30(1):1-5.
[11] S. Garg. Introduction to advanced engine control concepts [C]. Fundamentals of Aircraft En-gine Control Design Course, Oklahoma City,2007.
[12] G. B. Gilyard, J. S.Orme. Performance seeking control: program overview and future directions[R]. AIAA-93-3765-CP,1993.
[13] V. V. R. Silva, W. Khatib, P. J. Fleming. Performance optimization of gas turbine engine [J].Engineering Applications of Artificial Intelligence,2005,18(5):575-583.
[14] J. D. Chisholm. In-flight optimization of the total propulsion system[R]. AIAA-92-3744,1992.
[15] J. A. Ralph. Advanced control for airbreathing engines (volume1)[R]. NASA-CR-189203,1993.
[16] T. Brotherton, A. Volponi, R. Luppold, et al. eSTORM: Enhanced self-tuning on-boardreal-time engine model [C]. Proceedings of IEEE Aerospace Conference, Montana,2003.
[17] A. Vloponi. Enhanced self-tuning on-board real-time model (eSTORM) for aircraft engineperformance health tracking [R]. NASA/CR-2008-215272,2008.
[18] R. W. Vizzini. Integrated flight/propulsion control system considerations for future aircraft ap-plication [J]. Journal of Engineering for Gas Turbines and Power,1985,107(4):833-837.
[19] J. S. Orme, T. Conners. Supersonic flight test results of a performance seeking control algo-rithm on a NASA F-15aircraft [R]. AIAA-94-3210,1994.
[20] J. R. Webster. Opportunities for adaptive optimization of gas turbine systems [C]. Proceedingsof SPIE4763, Giens, France,2002.
[21] R. H. Smith, J. D. Chisholm, J. F. Stewart. Optimizing aircraft performance with adaptive, in-tegrated flight/propulsion control [J]. Journal of Engineering for Gas Turbines and Power,1991,113(1):87-94.
[22] J. Orme, G. Gilyard. Subsonic flight test evaluation of a propulsion system parameter estima-tion process for the F100Engine [R]. AIAA-92-3745,1992.
[23] F. W. Burcham Jr., E. A. Haering. Highly integrated digital engine control system on an F-15airplane [J]. AIAA-84-1259,1984.
[24] W. A. Yonke, R. J. Landy. HIDEC adaptive engine control system flight evaluation result [R].ASME-87-Gt-252,1987.
[25] S. G. Nobbs, S. W. Jacobs, D. J. Donahue. Development of the full-envelop performance seek-ing control algorithm [R]. AIAA-92-3745,1992.
[26] J. S. Orme, G. B. Gilyard. Preliminary supersonic flight test evaluation of performance seekingcontrol [R]. AIAA-93-1821,1992.
[27] G. Gilyard, J. Orme. Subsonic flight test evaluation of a performance seeking control algorithmon an F-15airplane [R]. AIAA-92-3743,1992.
[28]李松林.飞行/推进系统综合控制—性能寻优自适应控制研究[D].南京:南京航空航天大学博士学位论文,1998.
[29]袁春飞,孙健国,熊智,等.推进系统优化控制模式研究[J].航空动力学报,2003,19(1):159-163.
[30]袁春飞,孙健国,熊智,等.飞/推综合控制模式亚音速半物理仿真试验[J].推进技术,2003,24(4):353-356.
[31]王芳,樊思齐,吴丹,等.MAPS方法在航空发动机性能寻优控制中的应用[J].航空动力学报,2005,20(3):503-507.
[32] Y. Zhu, S. Fan, H. Li, et al. A hybrid optimization based on linear programming and mod-el-assisted pattern search method in PSC [C].44th AIAA Aerospace Sciences Meeting and Ex-hibit, Reno, Nevada,2006.
[33]朱玉斌,樊思齐,李华聪,等.航空发动机性能寻优控制混合优化算法[J].航空动力学报,2006,21(2):422-426.
[34]任新宇,杨育武,樊思齐.推进系统综合性能寻优控制研究[J].推进技术,2010,31(1):61-64.
[35]何黎明,樊丁.利用SQP控制涡扇发动机加速过程的多目标最优化研究[J].航空动力学报,2001,16(2):179-181.
[36]陈光明,樊丁.SQP方法在航空发动机加速过程控制中的应用研究[J].航空动力学报,1999,14(3):313-316.
[37]孙丰诚,孙健国.基于序列二次规划算法的发动机性能寻优控制[J].航空动力学报,2005,20(5):862-867.
[38]赵琳,樊丁,陕薇薇.航空发动机过渡态全局寻优控制方法研究[J].航空动力学报,2007,22(7):1200-1203.
[39]张海波,孙健国.可行SLP法在发动机在线优化中的应用[J].航空学报,2010,31(4):663-670.
[40]戚学锋,樊丁,陈耀楚,等.基于FSQP算法的涡扇发动机多变量最优加速控制[J].推进技术,2004,25(3):233-236.
[41] C. T. Lawrence, A. L. Tits. A computational efficient feasible sequential quadratic program-ming algorithm [J]. SIAM J.Optim,2001,11(4):1092-1118.
[42] Z. Zhu. A simple feasible SQP algorithm for inequality constrained optimization [J]. AppliedMathematics and Computation,2006,182(2):987-998.
[43]刘大响,叶培良,胡骏,等.航空燃气涡轮发动机稳定性设计与评定技术[M].北京:航空工业出版社,2004.
[44] J. C. DeLaat, R. D. Southwick, G. W. Gallops. High stability engine control (HISTEC)[R].NASA/TM-1996-107272,1996.
[45] J. S. Orme, J. C. DeLaat, R. D. Southwick, et al. Development and testing of a high stabilityengine control (HISTEC) system [R]. NASA/TM-1998-206562,1998.
[46] J. C. DeLaat, R. D. Southwick, G. W. Gallops, et al. High stability engine control (HISTEC):flight demonstration results [R]. NASA/TM-1998-208566,1998.
[47] R. D. Southwick, G. W. Gallops, L. J. Kerr, et al. High stability engine control (HISTEC): flighttest results [R]. NASA/TM-1998-208481,1998.
[48] J. C. DeLaat, R. D. Southwick, G. W. Gallops. The high stability engine control (HISTEC) pro-gram: flight demonstration phase [R]. NASA/TM-1998-208482,1998.
[49] Y. Liu, M. Dhingra, J. V. R. Prasad. Active compressor stability management via a stall margincontrol mode [J]. Journal of Engineering for Gas Turbines and Power,2010,132(5):1-10.
[50] M. Inoue, M. Kuroumaru, T. Tanino, et al. Propagation of multiple short-length-scale stall cellsin axial compressor rotor [J]. Journal of Turbomachinery,2000,122(1):45-54.
[51] M. Dhingra, Y. Neumeier, J. V. R. Prasad, et al. A stochastic model for a compressor stabilitymeasure [J]. Journal of Engineering for Gas Turbines and Power,2007,129(3):730-737.
[52]王立峰,张津.超机动飞行推进系统稳定性控制研究[J].推进技术,2000,21(4):1-4.
[53] L. Wang, Z. Li, Z. Lin. Enhancing the fidelity of post-stall flight simulation using detailedpropulsion system model [R]. AIAA-2004-4810,2004.
[54]袁春飞.飞行/推进系统综合优化控制模式及其关键技术[D].南京:南京航空航天大学博士学位论文,2004.
[55]陈霆昊,张海波,孙健国.基于攻角预测模型的航空发动机高稳定性控制[J].航空动力学报,2010,25(7):1676-1682.
[56] B. J. Smith, R. D. Zagranski. Next generation control system for helicopter engines [C]. AHSInternational57th Annual Forum Proceedings, DC, Washington,2001.
[57] B. J. Smith, R. D. Zagranski. Closed loop bench testing of the next generation control systemfor helicopter engines [C]. AHS International58th Annual Forum Proceedings, Montreal, Can-ada,2002.
[58] M. J. Scott. Performance benefits for a turboshaft engine using nonlinear engine control [R].NASA-20050192151,2005.
[59]姚文荣.涡轴发动机/旋翼综合建模控制及优化研究[D].南京:南京航空航天大学博士学位论文,2008.
[60]孙立国.直升机/发动机综合仿真平台下的发动机性能优化及控制[D].南京:南京航空航天大学硕士学位论文,2010.
[61]周剑波.变几何涡轴发动机综合控制技术研究[D].南京:南京航空航天大学博士学位论文,2011.
[62]王志峰,姜广伦,安锦文.直升机飞行/推进系统综合模型的建立[J].飞行力学,1999,17(4):17-21.
[63] W. A. Kuczynski, D. E. Cooper, W. J. Twomey, et al. The influence of engine/fuel control de-sign on helicopter dynamics and handling qualities [J]. Journal of the American Helicopter So-ciety,1980,25:26-34.
[64] W. J. Twomey, E. H. Ham. Review of engine/airframe/drive train dynamic interface develop-ment problems [R]. USARTL-TR-78-13,1978.
[65] D. A. Richardson, J. R. Alwang. Engine/airframe/drive train dynamic interface documentation[R]. USARTL-TR-78-11,1978.
[66] J. F. Needham, D. Banerjee. Engine/airframe/drive train dynamic interface documentation [R].USARTL-TR-78-12,1978.
[67] H. W. Hanson, R. W. Balke, B. D. Edwards, et al. Engine/airframe/drive train dynamic inter-face documentation [R]. USARTL-TR-78-15,1978.
[68] T. Morrison, R. D. Zagranski, A. H. White. Adaptive fuel control feasibility investigation [R].USAAVRADCOM-TR-83-D-1,1983.
[69] T. W. Yates. Adaptive fuel control feasibility investigation [R]. USAAVRADCOM-TR-82-D-39,1983.
[70] J. J. Howlett, T. Momson, R. D. Zagranski. Adaptive fuel control for helicopter applications [J].Journal of the American Helicopter Society,1984,29(4):43-54.
[71] D. H. Sweet. Flight testing of the Chandler Evans adaptive fuel control on the S-76A helicopter[C]. AHS International45th Annual Forum, Boston, USA,1989.
[72] W. K. Stephen. Flight tests of adaptive fuel control and decoupled rotor speed control systems[C]. AHS International45th Annual Forum, Boston, USA,1989.
[73] M. C. Desai, C. Crainic. Adaptive thermodynamic engine model for the next generation controlsystem for helicopter engines [C]. AHS International58th Annual Forum Proceeding, Montreal,Canada,2002.
[74]陈仁良.直升机飞行动力学数学建模及机动性研究[D].南京:南京航空航天大学博士学位论文,1998.
[75]孙传伟.直升机飞行动力学模型与飞行品质评估[D].南京:南京航空航天大学博士学位论文,2002.
[76] L. D. Corliss. A helicopter handling-qualities study of the effects of engine response characte-ristics, height-control dynamics, and excess power on nap-of-the-earth operations [R]. NASA-CP-2219,1982.
[77] L. D. Corliss, C. L. Blanken, K. Nelson. Effects of rotor inertia and RPM control on helicopterhandling qualities [R]. AIAA-83-2070,1983.
[78] L. D. Corliss. The effects of engine and height-control characteristics on helicopter handlingqualities [J]. Journal of the American Helicopter Society.1983,28(3):56-62.
[79]杨征山,李胜泉,章霖官.涡轴发动机功率涡轮转速控制回路方案研究[J].航空发动机,2005,31(2):46-50.
[80] J. J. Curran. T700fuel and control system [J]. Journal of the American Helicopter Society,1974,19(3):17-27.
[81]张海波,孙健国,孙立国.一种涡轴发动机转速抗扰控制器设计及应用[J].航空动力学报,2010,25(4):943-950.
[82]孙立国,孙健国,张海波,等.基于支持向量回归机的发动机/直升机扭矩超前控制[J].航空动力学报,2011,26(3):680-686.
[83] J. V. R. Prasad, A. J. Calise, S. Hajra, et al. Neural net based adaptive nonlinear control of aturboshaft engine [C]. AHS International57th Annual Forum, DC, Washington, USA,2001.
[84] W. H. Pfeil, M. Athans, S. H. Austin. Multi-variable control for the GE T700engine using theLQG/LTR design methodology [C]. American Control Conference, Seattle, Washington, USA,1986.
[85] W. H. Pfeil, G. D. L. Reyes, G. A. Bobula. The application of LQR synthesis techniques to theturboshaft engine control problem [R]. AIAA-84-1455,1984.
[86] D. G. C. Ruttledge, E. J. Hammon, D. R. Gilmore. A rotorcraft flight propulsion control inte-gration study [R]. NASA-CR-179574,1986.
[87] G. D. L. Reyes, D. R. Gouchoe. The Design of a turboshaft speed governor using modern con-trol techniques [R]. NASA-CR-175046,1986.
[88] J. R. Mihaloew, M. G. Ballin, D. C. G. Ruttledge. Rotorcraft flight propulsion control integra-tion: an eclectic design concept [R]. NASA-TP-2815,1988.
[89]孙立国,王日先,孙健国,等.基于ADRC的涡轴发动机/直升机子系统抗扰控制[J].北京航空航天大学学报,2011,37(10):1312-1316.
[90]张海波,孙立国,孙健国.直升机/涡轴发动机综合系统鲁棒抗扰控制设计[J].航空学报,2010,31(5):883-892.
[91]姚文荣,孙健国.涡轴发动机非线性模型预测控制[J].航空学报,2008,29(4):776-780.
[92] J. Sun, V.Vasilyev, B.Ilyasov. Advanced multivariable control system of aeroengines [M].北京:北京航空航天大学出版社,2005.
[93] E. J. Tich, P. D. Shaw, D. F. Berg, et al. Performance seeking control for cruise optimization infighter aircraft [R]. AIAA-87-1929,1987.
[94] J. F. Stewart. Integrated flight propulsion control research result using the NASA F-15HIDECflight research facility [R]. AIAA-92-4106,1992.
[95] R. E. Fennell. Integrated airframe propulsion control [R]. AIAA-83-2563,1983.
[96] S. Garg. Partitioning of centralized integrated flight/propulsion control design for decentralizedimplementation [J]. IEEE Trans. on Control Systems Technology,1993,1(2):93-100.
[97] S. Garg, D. L. Mattern. Application of an integrated methodology for propulsion and airframecontrol design to a STOVL aircraft [R]. AIAA-94-3611-CP,1994.
[98] D. G. Bates, S. L. Gately, I. Postlethwaite. Integrated flight and propulsion control system de-sign using H∞loop-shaping techniques [C]. Proceedings of the38th Conference on Decision&Control, Phoenix, Arizona USA,1999.
[99] W. K. Neighbors, S. M. Rock. Integrated flight/propulsion control specifications: accountingfor two-way coupling [R]. AIAA-94-3613-CP,1994.
[100]王曦,林永霖,吴永康.H∞控制在飞行/推进综合控制系统中的应用[J].航空动力学报,2004,19(5):698-702.
[101] P. D. Shaw, S. M. Rock, W. S. Fisk. Design methods for integrated control systems [R]. AF-WAL-TR-88-2061,1988.
[102] D. L. Mattern, S. Garg, R. E. Bullard. Integrated flight/propulsion control system design basedon a decentralized, hierarchical approach [R]. AIAA-89-3519,1989.
[103]单贵平.航空发动机建模与推力矢量控制研究[D].南京:南京航空航天大学硕士学位论文,2007.
[104]刘薇.飞/推综合控制系统仿真平台开发[D].南京:南京航空航天大学硕士学位论文,2006.
[105]孙丰诚.航空发动机性能寻优控制控制技术研究[D].南京:南京航空航天大学硕士学位论文,2007.
[106]廉筱纯,吴虎.航空发动机原理[M].西安:西北工业大学出版社,2005.
[107]杨刚,孙健国,李秋红.航空发动机控制系统中的增广LQR方法[J].航空动力学报,2004,19(1):153-158.
[108]杨刚.多变量鲁棒控制在涡扇发动机中的应用研究与实验验证[D].南京:南京航空航天大学博士学位论文,2004.
[109] F. D. Mueller, S. G. Nobbs. Dual engine application of the performance seeking control algo-rithm [R]. AIAA-93-1822,1993.
[110]王前宇.基于控制结构优化的航空发动机多变量鲁棒控制研究[D].南京:南京航空航天大学硕士学位论文,2010.
[111]王信德.某型涡扇发动机自适应建模研究[D].南京:南京航空航天大学硕士学位论文,2003.
[112]陈霆昊.发动机机载自适应模型与高稳定性控制技术研究[D].南京:南京航空航天大学硕士学位论文,2010.
[113] K. George. Adaptive performance seeking control using fuzzy model reference learning controland positive gradient control[R]. AIAA-97-3191,1997.
[114]施光燕,董加礼.最优化方法[M].北京:高等教育出版社,2005.
[115]杨冰.实用最优化方法及计算机程序[M].哈尔滨:哈尔滨船舶工程学院出版社,1994.
[116]袁亚湘,孙文瑜.最优化理论与方法[M].北京:北京科学技术出版社,2001.
[117]阳明盛,罗长童.最优化原理、方法及求解软件[M].北京:科学出版社,2006.
[118]申培萍.全局优化方法[M].北京:科学出版社,2006.
[119]姚文荣.涡轴发动机/旋翼综合建模、控制及优化研究[D].南京:南京航空航天大学博士论文,2008.
[120]杨刚,孙健国,姚华.航空发动机H∞/LTR控制试验验证[J].航空学报,2006,27(5):773-777.
[121] J. A. K. Suykens, T. Van Gestel, J. De Brabanter, et al. Least squares support vector machines[M]. Singapore: World Scientific,2002.
[122] E. L. Goldsmith, J. Seddon. Practical intake aero dynamic design [M]. US: AIAA and Black-well Scientific Publications,1993.
[123] F. Ojeda, J. A. K. Suykens, B. D. Moor. Low rank updated LS-SVM classifiers for fast variableselection [J]. Neural Networks,2008,21(2-3):437-449.
[124] E. K. Tang, P. Suganthan, X. Yao. Gene selection for microarray data based on least squaressupport vector machine [J]. BMC Bioinformatics,2006,7(95):1471-2105.
[125] X. Zhou, K. Z. Mao. LS bound based gene selection for DNA microarray data [J]. Bioinfor-matics,2005,21(8):1559-1564.
[126] I. Guyon, J. Weston, S. Barnhill, et al. Gene selection for cancer classification using supportvector machines [J]. Machine Learning,2002,46(1-3):389-422.
[127] J. A. K. Suykens, J. Vandewalle. Least squares support vector machine classifiers [J]. NeuralProcessing Letter,1999,9(3):293-300.
[128] Y. Zhao, J. Sun, J. Wang. Online parsimonious least squares support vector regression and itsapplication to sensor analytical redundancy for aeroengines [J]. Transactions of Nanjing Uni-versity of Aeronautics&Astronautics,2009,26(4):280-287.
[129]赵永平,孙健国.基于滚动窗法最小二乘支持向量机的稳健预测模型[J].模式识别与人工智能,2008,21(1):1-5.
[130]黄红选,韩继业.数学规划[M].北京:清华大学出版社,2006.
[131] W. G. Steenken, J. G. Williams, K. R. Walsh. Inlet flow characteristics during rapid maneuversfor an F/A-18A airplane [R]. NASA/TM-1999-206587,1999.
[132] F. L. Lewis. Applied optimal control and estimation: digital design and implementation [M].Englewood Cliffs, New Jersey: Prentice Hall,1992.
[133] D. E. Miller, M. Rossi. Simultaneous stabilization with near optimal LQR performance [J].IEEE Trans. Automatic Control,2001,46(10):1543-1555.
[134]杨刚,姚华.实用航空发动机LQR权阵选取方法[J].南京航空航天大学学报,2006,38(4):403-407.
[135]邹先全.涡轴发动机自适应模型的建立与鲁棒控制[D].南京:南京航空航天大学硕士论文,2009.
[136] S. M. Rock, K. Neighbors. Integrated flight/propulsion control for helicopters [C]. AHS Inter-national49th Annual Forum, St. Louis, Missouri,1993.
[137] R. H. Luppold, G. W. Gallops, L. J. Kerr, et al. Estimating in-flight engine performance varia-tions using Kalman Filter concepts [R]. AIAA-89-2584,1989.
[138]邹先全,孙健国,张海波.旋翼/涡轴发动机的自适应模型[J].北京航空航天大学学报,2009,35(10):1192-1196.
[139] T. Kobayashi, D. L. Simon, J. S. Litt. Application of a constant gain extended Kalman Filter forin-flight estimation of aircraft engine performance parameters [R]. NASA/TM2005-213865.
[140]陈恬,孙健国.粗糙集与神经网络在航空发动机气路故障诊断中的应用[J].航空动力学报,2006,21(1):207-212.
[141]徐启华,师军.基于支持向量机的航空发动机故障诊断[J].航空动力学报,2005,20(2):298-302.
[142] Y. Zhao, J. Sun. Recursive reduced least squares support vector regression [J]. Pattern Recogni-tion,2009,42(5):837-842.
[143] K. M. Lin, C. J. Lin. A study on reduced support vector machines [J]. IEEE Trans. NeuralNetworks,2003,14(6):1449-1459.
[144] L. Jiao, L. Bo, L. Wang. Fast sparse approximation for least squares support vector machine [J],IEEE Trans. Neural Networks,2007,18(3):685-697.
[145] F. Lu, J. Huang, X. Qiu. Application of multi-outputs LSSVR by PSO to the aero-engine model[J]. Journal of Systems Engineering and Electronics,2009,20(5):1153-1158.
[146]李丽娟.最小二乘支持向量机建模及预测控制算法研究[D].杭州:浙江大学博士论文,2008.
[147]姚文荣,孙健国.基于在线可变导叶角的涡轴发动机优化控制[J].航空动力学报,2007,22(11):1930-1934.
[148]周剑波,时瑞军,张秋贵,等.进口导叶可调的涡轴发动机性能寻优控制模式[J].航空动力学报,2011,26(10):2371-2375.
[149] T. Iwata, S. M. Rock. Benefits of variable rotor speed in integrated helicopter/engine control [J].AIAA-93-3851-CP,1993.
[150] R. T. N. Chen. An exploratory investigation of the flight dynamics effects of rotor RPM varia-tions and rotor state feedback in hover [R]. NASA-TM-103968,1992.
[151] G. Tatsuhiko, K. Kawakami. Variable rotor speed transmission with high speed traction drive[C]. AHS International55th Annual Forum Proceeding, Montreal, Canada,1999.
[152] C. G. Schaefer, F. H. Lutze. Enhanced energy maneuverability for attack helicopters using con-tinuous, variable rotor speed control [C]. AHS International47th Annual Forum Proceeding,Phoenix, AZ,1991.
[153] Q. S. Joe, T. A. Badgwell. A survey of industrial model predictive control technology [J]. Con-trol Engineering Practice,2003,11(7):733-764.
[154] D. Q. Mayne, J. B. Rawlings, C. V. Rao, et al. Constrained model predictive control: stabilityand optimality [J]. Automatica,2000,36(6):789-814.
[155] L. Magni, H. Nijmeijer, A. J. Schaft. A receding horizon approach to the nonlinear H∞controlproblem [J]. Automatica,2001,37(3):429-435.
[156] J. Mu, D. Rees. Approximate model predictive control for gas turbine engines [C]. Proceedingof the2004American Control Conference, Boston, Massachusetts,2004.
[157] B. J. Brunell. Model adaptation and nonlinear model predictive control of an aircraft engine [C].Proceeding of ASME Turbo Expo2004: Power for Land, Sea, and Air, Vienna, Austria,2004.
[158] H. Richter, A. Singaraju, J. S. Litt. Multiplexed predictive control of a large commercial turbo-fan engine [J]. Journal of Guidance, Control and Dynamics,2008,31(2):273-181.
[159] B. G. Vroemen, H. A. V. Essen, A. A. V. Steenhoven, et al. Nonlinear model predictive controlof a laboratory gas turbine installation [J]. Journal of Engineering for Gas Turbines and Power,1999,121:629-634.
[160]席裕庚.预测控制[M].北京:国防科技出版社,1993.
[161]李胜泉,杨征山,孙健国.基于内模原理的涡轴发动机状态反馈控制方法[J].航空动力学报,2007,22(5):829-832.
[162]韩京清.自抗扰控制技术[J].前沿科学,2007,1(1):24-31.