航空发动机智能鲁棒控制研究
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摘要
随着对航空发动机性能要求越来越高,航空发动机控制已向多变量、智能、鲁棒控制等先进控制方式发展。而鲁棒控制能克服模型的不确定性,在非设计点也具有令人满意的性能,在航空发动机控制中具有良好的应用前景。将鲁棒控制和智能控制算法相结合,优化控制器参数,可以进一步减少回路之间的耦合,提高系统性能。本文围绕航空发动机智能多变量鲁棒控制这一主题,研究了航空发动机建模,鲁棒控制器智能参数优化方法,智能推力估计和控制方法,最后通过控制结构分析设计了航空发动机四变量分块解耦控制系统,并进行了数字仿真和半物理仿真验证。
论文首先建立了某型航空动机非线性部件级模型,为论文的研究工作提供了仿真平台。提出了基于智能优化的航空发动机状态变量模型建立方法,避免了传统拟合法公式推导的复杂性和对系统模态的要求,并将粒子群优化算法与最小二乘优化算法相结合,实现了航空发动机四输入系统状态变量模型的建立,提高了高阶系统建模的精度。
在多变量鲁棒控制系统设计中,提出了基于参考模型的智能参数优化方法,用于航空发动机转速控制系统,避免了基于性能指标加权优化中,性能指标与其权重没有明确对应关系的缺点,使系统具有明确的性能要求。设计了基于小波神经网络的航空发动机PID转速控制系统和解耦ALQR转速控制系统,提高了系统性能。提出了基于控制结构设计的模型逆控制方法,用于航空发动机直接推力控制系统。研究了推力估计的神经网络、支持向量机算法、Kalman跟踪滤波方法,并提出了基于控制器的跟踪滤波推力估计方法。采用神经网络逆模型和线性逆模型,结合PI控制算法,进行了推力控制研究。仿真结果表明推力估计和控制效果良好。
论文最后研究了航空发动机四输入鲁棒控制系统的控制结构设计方法,分析了四变量航空发动机控制系统中控制变量和被控制变量之间的单调性,提出了基于LQ/H∞的分块解耦控制算法,并通过数字仿真和半物理仿真验证了算法的有效性。
Aeroengine are becoming increasingly complex, with more control variables, to meet future demands on performance. To utilize the potential of the engines, it is also necessary to use more advanced control cenceptes such as multivariable, intelligent, robust control. Robust control can get good performance both at design point and off-design point. It has good application perspective at aeroengine control domain. Using the intelligent algorithm to optimize the paremeters of robust controller, can further eliminate the coupling between the control loops and enhance the system performance. The multivariable robust control of aeroengine is the topic of this paper. It includes aeroengine modelling, robust controller parameters optimization, intelligent thrust estimation and control. The four-input controller for aeroengine is designed and tested based on the control structure design at last.
The nonlinear component level model of aeroengine is researched in the paper. It provides the simulation platform. The state variable modeling method of aeroengine based on the intelligent optimization is proposed. It can get the linear model without complicated formula deduction and has no limitation on the system modes. The particle swarm optimization and the least square optimization are combined and used to build the aeroengine four-input state variable model. The precision of the model is improved by the combination optimization. In the design of multivariable robust control system, the intelligent parameters optimization method is proposed and used in aeroengine rotor speed control system. It can make the performance request clearly and avoid the shortcoming of performance indices weighted sum method that the weights have no clearly corresponding relationship with the system response. The aeroengine rotor speed control system is designed based on the wevelet network PID control and decoupling ALQR control. The inverse model control method is proposed based on the control structure design and used in aeroengine thrust control system. The neural network, support vector machine and Kalman filter are researched in thrust estimation. The tracking filter thrust estimate method based on controller is proposed. The neural network inverse model and the linear inverse model are adopted to control the thrust combined with the PI control algorithm. The simulation results show that the thrust estimate and control results are satisfied.
At last, the control structure design of aeroengine four-input robust control system is researched. The monotony of the input variables to the output variables of the enigne is analysed. The block decoupling control method base on LQ/H∞is proposed and used to design the four-variable engine control system. The method is validated by digital and semi-physical testing.
论文首先建立了某型航空动机非线性部件级模型,为论文的研究工作提供了仿真平台。提出了基于智能优化的航空发动机状态变量模型建立方法,避免了传统拟合法公式推导的复杂性和对系统模态的要求,并将粒子群优化算法与最小二乘优化算法相结合,实现了航空发动机四输入系统状态变量模型的建立,提高了高阶系统建模的精度。
在多变量鲁棒控制系统设计中,提出了基于参考模型的智能参数优化方法,用于航空发动机转速控制系统,避免了基于性能指标加权优化中,性能指标与其权重没有明确对应关系的缺点,使系统具有明确的性能要求。设计了基于小波神经网络的航空发动机PID转速控制系统和解耦ALQR转速控制系统,提高了系统性能。提出了基于控制结构设计的模型逆控制方法,用于航空发动机直接推力控制系统。研究了推力估计的神经网络、支持向量机算法、Kalman跟踪滤波方法,并提出了基于控制器的跟踪滤波推力估计方法。采用神经网络逆模型和线性逆模型,结合PI控制算法,进行了推力控制研究。仿真结果表明推力估计和控制效果良好。
论文最后研究了航空发动机四输入鲁棒控制系统的控制结构设计方法,分析了四变量航空发动机控制系统中控制变量和被控制变量之间的单调性,提出了基于LQ/H∞的分块解耦控制算法,并通过数字仿真和半物理仿真验证了算法的有效性。
Aeroengine are becoming increasingly complex, with more control variables, to meet future demands on performance. To utilize the potential of the engines, it is also necessary to use more advanced control cenceptes such as multivariable, intelligent, robust control. Robust control can get good performance both at design point and off-design point. It has good application perspective at aeroengine control domain. Using the intelligent algorithm to optimize the paremeters of robust controller, can further eliminate the coupling between the control loops and enhance the system performance. The multivariable robust control of aeroengine is the topic of this paper. It includes aeroengine modelling, robust controller parameters optimization, intelligent thrust estimation and control. The four-input controller for aeroengine is designed and tested based on the control structure design at last.
The nonlinear component level model of aeroengine is researched in the paper. It provides the simulation platform. The state variable modeling method of aeroengine based on the intelligent optimization is proposed. It can get the linear model without complicated formula deduction and has no limitation on the system modes. The particle swarm optimization and the least square optimization are combined and used to build the aeroengine four-input state variable model. The precision of the model is improved by the combination optimization. In the design of multivariable robust control system, the intelligent parameters optimization method is proposed and used in aeroengine rotor speed control system. It can make the performance request clearly and avoid the shortcoming of performance indices weighted sum method that the weights have no clearly corresponding relationship with the system response. The aeroengine rotor speed control system is designed based on the wevelet network PID control and decoupling ALQR control. The inverse model control method is proposed based on the control structure design and used in aeroengine thrust control system. The neural network, support vector machine and Kalman filter are researched in thrust estimation. The tracking filter thrust estimate method based on controller is proposed. The neural network inverse model and the linear inverse model are adopted to control the thrust combined with the PI control algorithm. The simulation results show that the thrust estimate and control results are satisfied.
At last, the control structure design of aeroengine four-input robust control system is researched. The monotony of the input variables to the output variables of the enigne is analysed. The block decoupling control method base on LQ/H∞is proposed and used to design the four-variable engine control system. The method is validated by digital and semi-physical testing.
引文
[1] John R. S, Kurt Seldner. Real-time simulation of F100-PW-100 turbofan engine using engine using the hybrid computer[R]. NASA TM X-3261.
[2] Keck, M.F., Fredlake, J.J. Schwent, G.V. A control concept combining the best of the current hydromechanical and electronic technologies[C]. SAE Paper 740380, 1974.
[3] Bernard L. Koff. Gas turbine technology evolution: A designer’s perspective[J]. Journal of propulsion and power. 2004, 20(4): 577-595.
[4]金茂贤.航空发动机先进控制概念及最新进展[J].航空科学技术, 2005(1): 24-27.
[5]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.
[6]郭虹.航空发动机控制系统的发展趋势[J].沈阳航空工业学院学报, 1997, 14(1):70-74.
[7] McKinnet, John S. Simulation of turbofan engine part I: Description of method and balancing technique[R]. AFAPL-TR-67-125, 1967.
[8] Koenig R W, Fisbbaco L H. Geneng-A program for calculating design and off-design performance for turbojet and turbofan engines[R]. NASA TND-6552, 1972.
[9] Fisbbacb L H, Koenig R W. Geneng II-A program for calculating design and off-design performance of two- and three-spool turbofans with as many as three nozzles[R]. NADA TND-6553, 1972.
[10] Sellers J F, Krosel S M, Szuch J R. Dyngen-a program for calculating steady-state and transient performance of turbojet and turbofan engines[R]. NASA TND-7901, 1975.
[11] Al Vloponi. Enhanced self tuning on-board real-time model(eSTORM) for aircraft engie performance health tracking[R]. NASA/CR-2008-215272.
[12]华清.某型涡扇发动机全包线实时仿真模型[J].航空发动机, 2002,(1):47-52.
[13]周文祥.航空发动机及控制系统建模与面向对象的仿真研究[D].南京:南京航空航天大学博士学位论文, 2006.
[14]袁鸯.发动机自适应建模及神经网络控制[D].南京:南京航空航天大学硕士学位论文, 2005.
[15] Lupold R H, Gallops G W, Kerr L J, et al. Estimating in-flight engine performance variations using Kalman filter concepts[R]. AIAA-1989-2584.
[16] Sugiyama N. Derivation of ABCD system matrices from nonlinear dynamic simulation of jet engines[R]. AIAA-1992-3319.
[17] P. Verboven, P. Guillaume, B. Cauberghe. Multi-variable frequency–response curve fittingwith application to modal parameter estimation[J]. Automatica, 2005, 41(10):1773– 1782.
[18]冯正平,孙健国,黄金泉,等.一种建立航空发动机状态变量模型的新方法[J].航空动力学报, 1998, 13(4):435-438.
[19]郑铁军,王曦,罗秀芹,等.建立航空发动机状态空间模型的修正方法[J].推进技术, 2005, 26(1):46-49.
[20]褚健,俞立,苏宏业.鲁棒控制理论及其应用[M].杭州:浙江大学出版社, 2000.
[21] G. Zames. Functional analysis applied to nonlinear feedback systems[J]. IEEE, 1963, 10(3):392-404.
[22] B.D.P. Anderson, J.B. Moore. Optimal control: linear quadratic methods[M]. New Jersey, 1979.
[23] Doyle, J.C., Stein, G. Multivariable feedback design: concepts for a classical/modern synthesis[J]. IEEE Trans. On Automatic Control, 1981, 26(1):4-16.
[24] T. T. Lee, M. S. Chen. Robustness recovery of LQ based multivariable control designs[J]. International Journal of Control, 1987, 45(4): 1131-1136.
[25] D Z.J. Palmor, Y. Halevi. On the recovery procedure for LQG systems[J]. Automatica, 1988, 24(2): 275-277.
[26] Garg, S., Athans, M. The LQG/LTR procedure for multivariable feedback control design[J]. IEEE Trans. On Automatic Control, 1986 , 32(2): 105-114.
[27] Michel Kinnaert, Youbin Peng. Discrete-time LQG/LTR technique for systems with time delays[J]. Systems & Control Letters, 1990, 15(4): 303-311.
[28] Athans, M., Petros, K., Efthimios, K., et al. Linear quadratic gaussian with loop transfer recovery methodology for the F-100 Engine[J]. Journal of Guidance, 1986, 9(1): 45-52.
[29] Gokcek, C. Kabamba, P.T. Meerkov, S.M. An LQR/LQG theory for systems with saturating actuators[J]. IEEE Trans. Automatic Control, 2001, 46(10): 1529-1542.
[30] Chaules, L. M., Peter, S. M. On an assumed convergence result in the LQG/LTR technique[J]. IEEE Trans. Automatic Control, 1991, 36(1): 951-952.
[31] Stein, G.; Athans, M. The LQG/LTR procedure for multivariable feedback control design[J]. IEEE, 1987, 32(2): 105-114.
[32] A.A. Rodriguez, M. Athans. Multivariable control of a twin lift helicopter system using the LQG/LTR design methodology[C]. IEEE, 1986, pp: 1325-1332.
[33] Hsiang-Jui Wu, Ying-Yu Tzou. LQG/LTR robust control of an AC induction servo drive[C]. IEEE, 1991, pp: 613-619.
[34] Tadjine M., M'Saad M., Dugard L. An overview on the LQG/LTR method using the delta operator[C]. IEEE, 1992, 4: 3728– 3733.
[35] Saberi, A., Sannuti, P. Observer design for loop transfer recovery and for uncertain systems[J]. IEEE Trans. Automatic Control, 1990, 35(8): 878-897.
[36] Doyle, J. C., Garg, S. Robustness with observers[J]. IEEE Trans. Automatic Control, 1979, 24(4): 607-611.
[37] Zhang, Z. H., Freudenberg, J. S. Loop transfer recovery for nonminimum phase plants[J]. IEEE Trans. Automatic Control, 1990, 35(5):547-533.
[38] Haessig, D. Selection of LQG/LTR weighting matrices through constrained optimization [C]. IEEE, 1995, 1: 458– 460.
[39] Prakash, R., Rao, S.V. LQG/LTR controller design using a reduced order model[C]. IEEE, 1989, pp: 343-346.
[40] Young-Moon Park, Myoen-Song Choi, Jeong-Woo Lee, et al. An auxiliary LQG/LTR robust controller design for cogeneration plants[J]. IEEE, 1996, 11(2): 407-413.
[41] Ray L. R. Stability robustness of uncertain LQG/LTR systems[J]. IEEE Trans. Automatic Control, 1993, 38(2): 304-308.
[42] C. Zhou, J.R. Whiteley, E.A. Misawa, et al. Application of enhanced LQG/LTR for distillation control [J]. IEEE, 1995, 15(4):56-63.
[43] Garg, S. Turbofan engine control system design using the LQG/LTR methodology[R]. NASA-CR-182303, 1989.
[44] Willian, H. P., Athans, M., Austin S. III. Multivariable control of the GE T700 engine using the LQG/LTR design methodology[C]. ACC, 1986, pp:1293-1312.
[45] James, A.M. Multivariable control of a submarine using the LQG/LTR method[C]. AC C, 1986, pp:1313-1324.
[46] Zhou, K. M. Essentials of robust control[M]. Prentice Hall, 1997.
[47]周克敏, Doyle, J. C., Glover, K.鲁棒与最优控制[M].北京:国防工业出版社,2002.
[48] Zames, G. Feedback and optimal sensitivity: model reference transformations, multiplicative seminorms, and Approximate Inverses[J]. IEEE Trans. Automatic Control, 1981, 26(2): 301-320.
[49] Zames, G., Francis, B. Feedback, minimax sensitivity and optimal robustness[J]. IEEE Trans. Automatol Control, 1983, 28(4): 585-601.
[50] Chang, B.C., Pearson, J.B. Optimal disturbance reduction in linear multivariable systems[J].IEEE Trans. Automatic Control, 1984, 29(6): 880-887.
[51] Glover, K. All optimal Hankel - norm approximations of linear multivariable systems and their L∞error bounds[J]. International Journal of Control, 1984, 39(6):1115-1193.
[52] Doyle, J. C. Lecture notes in advances in multivariable control. ONR/Honeywell Workshop, Minneapolis, 1984.
[53] Huibert Kwakernaak. Minimax frequency domain performance and robustness optimization of linear feedback systems[J]. IEEE, 1985, 30(10):994-1004.
[54] Francis, B. A., Doyle, J. C. Linear control theory with H∞optimality criterion[J]. SIAM J. Control Optimization, 1987, 25(4):815-844.
[55] Ball, J. A., Cohen, N. The sensitivity minimization in an H∞norm: parameterization of all optimal solutions[J]. Int. J. Control, 1987, 46:785-861.
[56] Green, M., Glover, K., D.J.N. Limebeer, et al. A J-spectral factorization approach to H∞control[J]. SIAM J. Control Opt, 1990, 28(1990):1350-1371.
[57] Kimura, H. (J,J')-lossless factorization based on conjugation[J]. System & Control Letters, 1992, 19(2):95-109.
[58] Doyle, J. C., Glover, K., Khargonekar, P. P. et al. State-space solutions to standard H2 and H∞control problems[J]. IEEE Trans. Automatic Control, 1989, 34(8):831-847.
[59] Basar, T., Bernhard, P. H∞optimal control and related minimax design problems: a dynamic game approach[M], Boston, MA :Birkhaeuser,2008.
[60] Tadmor, G. Worst case design in the time domain: the maximum principle and the standard H∞problem[J]. Mathematics of Control, Signals, and Systems (MCSS), 1990, 3(4):301-324.
[61] Guesalaga, A. R., Kropholler, H. W. Improved temperature and humidity control H∞synthesis[J]. IEEE Proc. Pt D, 1990, 137(3):374-380.
[62] Rotstein, H., Sideris A. MIMO H∞-control with time domain constraints[C]. IEEE Conference, 1992, 3:2601-2606.
[63] Eberhardt, R.L., Wise, K.A. High gain H∞feedback control for an anti-air missile[C]. IEEE Conference, 1993, 1: 449-455.
[64] Naidu, D.S., Charalambous, C.D., Moore, K.L., et al. H∞-optimal control of singularly perturbed discrete-time systems, and risk-sensitive control[C]. IEEE Conference,1994 , 2: 1706-1711.
[65] Xie, L., de Souza Carlos, E. Robust H∞control for linear systems with norm-bounded time-varying uncertainty [J]. IEEE, 1992, 37(8): 1188-1191.
[66] Safonov, M. G., Chung, R. Y., Flashner, H. H∞robust control synthesis for a large space structure[C]. IEEE Conferences, 1988, PP:2038-2045.
[67]王曦,陈华荣.不确定性系统鲁棒H∞/H2混合控制在航空发动机中的应用[J].航空动力学报, 2002, 17(2): 250-254.
[68]谢光华,曾庆福.基于LMI的航空发动机混合H2/H∞输出反馈控制[J].推进技术. 2000, 21(6): 28-31.
[69] M. Sojoodi, V.J. Majd. A technical approach to H2 and H∞control of a flexible transmission system[C]. IEEE Conferences, 2010, PP:124-128
[70] Ian Postlethwaite, Matthew C. Turner,Guido Herrmann. Robust control applications. Annual Reviews in Control, 31 (2007): 27-39.
[71] V. Suplin, U. Shaked. Robust H∞output-feedback control of systems with time-delay. Systems & Control Letters, 57 (2008): 193-199.
[72] Davide Fissore. Robust control in presence of parametric uncertainties: Observer- based feedback controller design. Chemical Engineering Science, 63(2008):1890-1900.
[73] Tao Liu, Furong Gao. Robust two-dimensional iterative learning control for batch processes with state delay and time-varying uncertainties[J]. Chemical Engineering Science, 65(2010): 6134-6144.
[74] H. Austin Spang, Harold Brown. Control of jet engines[J]. Control engineering practice 7(1999):1043-1059.
[75] W. Merrill, B. Lehtinen, J. Zeller. The role of modern control theory in the design of controls for aircraft turbine engines[R]. AIAA-1982-320.
[76] B. Lehtinen. Multivariable control altitude demonstration on the F100 turbofan engine[R]. AIAA-79-1204.
[77]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.
[78] Michael Athans, Petros Kapasouris, Efthimios Kappos, et al. Linear-Quadratic Gaussian with loop transfer recovery methodology for the F-100 engine[R]. Journal of Guidance, Control, and Dynamics, 1986, 9(1):45-52.
[79] Athans M., Cambridge Ma., Kappos E., et al. Multivariable control for the F-100 engine using the LQG/LTR methodology[R]. AIAA-1984-1910, pp: 434-444.
[80] W. F. Dellinger, A. T. Alouani. Application of linear multivariable control design for the GE-21 turbofan jet engine[C]. IEEE Conference, 1989, pp:167-171.
[81] Yuan Xiao-chuan, Guo Ying-qing. Non-fully recovering LQG/LTR method and its application in aeroengine control[R]. AIAA, 2007-5716.
[82]郭迎清,吴丹,张华.全飞行包线LQG/LTR多变量控制器设计[J].航空发动机, 2002, (4): 44-47.
[83] M.Harefors. Application of H∞robust control to the RM12 jet engine[J]. Control Eng. Practice, 1997, 5(9):1189-1201.
[84] Dean K. Frederich, Sanjay Garg, Shrider Adibhatla. Turbofan engine control design using robust multivariable control technologies[J]. IEEE transactions on control systems technology, 1996, 8(6):961-970.
[85] Shrider Adibhatla, George J. Collier, Xin Zhao, et al. H∞control design for a jet engine[R]. AIAA-98-3753.
[86] Yang Gang, Sun jianguo. Reduced order H∞/LTR method for aeroengine control system[J]. Chinese journal of aeronautics, 2004, 17(3):129-135.
[87] Krishna Kumar, Kulkarni Nilesh. Inverse adaptive neuro-contrl of a turbo-fan engine[R]. AIAA-1999-394.
[88]包睿,黄金泉.航空发动机反馈神经网络自适应控制方法[J].航空发动机, 2007, 33(4): 46-49.
[89]梁华,李应红,尉询楷,等.航空发动机的支持向量机自适应PID控制[J].航空动力学报,2007, 22(1):137-141.
[90] Richard Y. Chiang, Fred Y. Hadaegh. Robust jet engine control[R]. AIAA-96-3927.
[91]李华聪,荣立烨,朱玉斌.基于QDRNN网络的航空发动机多变量解耦控制[J].航空动力学报, 2007, 22(11): 1921-1924.
[92]傅强,樊丁.航空发动机PID神经网络解耦控制[J].推进技术, 2007, 28(2):208-210.
[93] Junxia Mu, David Rees, G P Liu. Advanced controller design for aircraft gas turbine engines[J]. Control Engineering Practice, 2005, 13(8): 1001–1015.
[94]姚彦龙,孙健国.基于神经网络逆控制的发动机直接推力控制[J].推进技术,2008, 29(2): 249-252.
[95]刘建勋,李应红,陈永刚,等.航空发动机LQR控制的模糊神经网络方法[J].航空动力学报, 2004, 19(6): 838-843.
[96]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, 2004.
[97]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.
[98]许东,吴铮.基于Matlab 6.X的系统分析与设计---神经网络[M].西安:西安电子科技大学出版社, 2003.
[99] Adibhatla S, Brown H, Gastineau Z. Intelligent engine control (IEC)[R]. AIAA-92-3484, 1992.
[100] Adibhatla S, Lewis T. Model-based intelligent digital engine control (MoBIDEC)[R]. AIAA-97-3192, 1997.
[101]王文娟,马洪忠,刘长林.无人机综合飞行/推力矢量控制[J].航空学报, 2008, 29(增刊):s150-s156.
[102] Barton S. Thrust Vectoring Control Concepts and Issues[J]. SAE-90-1848, 1990
[103]姚彦龙,孙健国.基于神经网络逆控制的发动机直接推力控制[J].推进技术,2008, 29(2):249-252.
[104] Manfredi Maggiore, Raul Ordonez, Kevin M. Passion, et al. Estimator design in jet engine applications[J]. Engineering Applications of Artificial Intelligence, 16(2003):579-593.
[105]姚彦龙,孙健国.自适应遗传神经网络算法在推力估计器设计中的应用[J].航空动力学报, 2007, 22(10):1748-1753.
[106] Timothy R. Conners, Robert L. Sims. Full flight envelope direct thrust measurement on a supersonic aircraft[R]. NASA/TM-1998-206560.
[107] Takahisa Kobayashi, Donald L. Simon, Jonathan S. Litt. Application of a constant gain extended Kalman filter for in-flight estimation of aircraft engine performance parameters[R]. NASA/TM-005-213865.
[108] Santanu Chatterjee, Jonathan S. Litt. Online model parameter estimation of jet engine degradation for autonomous propulsion control[R]. NASA/TM-2003-212608.
[109]陈恬,孙健国.基于相关性分析和神经网络的直接推力控制[J].南京航空航天大学学报, 2005, 37(2):183-187.
[110] Joanthan S.Litt, Neerav Shah, T.Shane Sowers. A demonstration of a retrofit architecture for intelligent control and diagnostics of a turbofan engine [R]. NASA/TM-2005-214019.
[111]孙健国, Vasilyev V., Hyasov B. Advanced multivariable control systems of aeroengines[M].北京:北京航空航天大学出版社, 2005.
[112]陈恬.无人作战飞机智能发动机控制技术研究[D].南京:南京航空航天大学博士论文,2006.
[113]兰春贤.发动机部件跟踪滤波器解析余度研究[D].南京:南京航空航天大学硕士学位论文, 1992.
[114] Tich, E. J. etc. Performance seeking control for cruise optimization in fighter aircraft[C]. AIAA-86-1929.
[115]兰春贤,孙健国.基于部件跟踪滤波器的解析余度技术[J].航空动力学报, 1994, 9(3):289-293.
[116] Martin D.E., Edwards CA. On the estimation algorithm for adaptive performance optimization of turbofan engines[R]. AIAA-93-1823.
[117]郁阳琳.基于部件跟踪滤波器的发动机数控系统故障检测、隔离和适应[D].南京:南京航空航天大学硕士学位论文, 1997.
[118] Yedavalli R.K. Robust estimation and fault diagnostics for aircraft engines with uncertain model data [J]. IEEE Conferences, 2007, PP:2822-2827.
[119] Donald Simon, Sanjay Garg. A systematic approach for model-based aircraft engine performance estimation[C]. AIAA-2009-1872.
[120] Trindel A. Maine, Gelnn B.Giyard, Heather H. Lambert. A preliminary evaluation of an F100 engine parameter estimation process using flight data[R]. AIAA-1990-1921.
[121] Takahisa Kobavashi, Donald L. Simon, Jonathan S. Litt. Application of a constant gain extended Kalman filter for in-flight estimation of aircraft engine performance parameters[R]. NASA/TM-2005-213865.
[122] Jonathan S. Litt. An optimal orthogonal decomposition method for Kalman Filter-based turbofan engine thrust estimation[R]. NASA/TM-2005-213864.
[123] Jonathan S. Litt, T.Shane Sower, Sanjay Garg. A retrofit control architecture to maintain engine peroformance with usage[R]. NASA/TM-2007-214977.
[124]姚彦龙.航空发动机神经网络直接推力逆控制[D].南京:南京航空航天大学硕士学位论文, 2005.
[125] Haiguo Xiong, Qitao Huang, Hongzhou Jiang, et al. Study on modeling and simulation of a flight simulator engine system[C]. IEEE Conferences, 2007, PP: 226-230.
[126] Mckinney, John S. Simulation of turbofan engine. Part II. User’s Manual and computer program listing[R]. AFAPL-TR-67-125-pt.-2.
[127] Szuch, John R. HYDES–A generalized hybrid computer program ofr studying turbojet or turbofan engine dynamics[R]. NASA TM x-3014.
[128] John S. Orme, Gerard S. Schkolnik. Flight assessment of the onboard propulsion system model for the performance seeking control algorithm on an F-15 aircraft[R]. NASA TechnicalMemorandum 4705.
[129]邹先权.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学位论文, 2008.12.
[130]陶金伟.航空发动机组态建模仿真技术研究[D].南京:南京航空航天大学硕士学位论文, 2009. 12.
[131]唐海龙,张津.面向对象的航空发动机性能仿真程序的设计方法研究[J].航空动力学报, 1999, 14(4):421-424.
[132]杨蔚华,孙健国.发动机实时建模技术的新发展[J].航空动力学报, 1995, 10(4) : 402-406.
[133]袁春飞,姚华,杨刚.航空发动机机载实时自适应模型研究[J].航空学报, 2006, 27(14): 561-564.
[134]周文祥,黄金泉,黄开明.航空发动机简化实时模型仿真研究[J].南京航空航天大学学报, 2005, 37(2): 251-255.
[135]于龙江,朴英.一种简化算法的航空发动机全状态数学模型[J].航空动力学报, 2008, 23(3): 510-515.
[136]杨刚,孙健国,黄向华,等.一种不需要迭代的发动机辅助变量建模方法[J].航空动力学报, 2003, 18(2): 289-294.
[137]李开宁.数值计算方法引论[M].北京:航空工业出版社, 2002.8.
[138]冯正平,孙健国.航空发动机小偏差状态变量模型的建立方法[J].推进技术, 2001, 22(1): 54-57.
[139]李秋红,孙健国.基于遗传算法的航空发动机状态变量模型建立方法[J].航空动力学报. 2006, 21(2): 427-431.
[140]周文祥,单晓明,耿志东,等.自寻优求解法建立涡轴发动机状态变量模型[J].航空动力学报. 2008, 23(12): 2314-2320.
[141]刘景茂,樊丁.基于SMI辨识的航空发动机模型建立[J].计算仿真, 2008, 23(3): 580-584.
[142] J.H.Holland. Adaptation in natural and artificial systems[M]. Michigan: University of MichiganPress, 1975.
[143] Goldberg D E. Genetic algorithms in search, optimization and machine learning[M]. Adssison-Wesley, 1989.
[144] James T., Brown E., Ragsdale C.T. Grouping genetic algorithm for the blockmodel problem[J]. IEEE, 2010,14(1):103-111.
[145] Chang Wook Ahn, Ramakrishna R.S. A genetic algorithm for shortest path routing problem and the sizing of populations[J]. IEEE, 2002, 6(6):566-579.
[146] Srinivas N, Deb K. Multi-objective optimization using nondominated sorting in genetic algorithms [J]. Evolutionary Computation, 1994, 2(3): 221-248.
[147] Yiu-Wing Leung, Yuping Wang. An orthogonal genetic algorithm with quantization for global numerical optimization[J]. IEEE, 2001, 5(1): 41-53.
[148] Shiu Yin Yuen, Chi Kin Chow. A genetic algorithm that adaptively nutates and never revisits[J]. IEEE, 2009, 13(2):454-472.
[149] Harik G.R., Lobo F.G., Goldberg D.E. The compact genetic algorithm[J]. IEEE, 1999, 3(4): 287-297.
[150] Chan K.C.C., Lee V., Leung H. Generating fuzzy rules for target tracking using a steady-state genetic algorithm[J]. IEEE, 1997, 1(3):189-200.
[151]陈国良,王煦法,庄镇泉,等.遗传算法及其应用[M].北京:人民邮电出版社,1996.
[152]云庆夏.进化计算[M].北京:冶金工业出版社, 2000.
[153]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社, 1999.
[154]王小平,曹立明.遗传算法--理论、应用与软件实现[M].西安:西安交通大学出版社,2002.
[155] Guo Pengfei, Wang Xuezhi, Han Yingshi. The enhanced genetic algorithms for the optimization design[C]. IEEE Conferences, 2010, 7: 2990 - 2994.
[156] Duan Bin, Sun Tongjing, Li Zhenhua, et al. Improved Chaos-GA-PID control in digital invert power supply[C]. IEEE Conferences, 2009 , PP: 2-315 - 2-318.
[157]张世英,胡宇,朱杰堂.基于遗传算法的航空发动机模糊优化控制[J].弹箭与制导学报, 2010, 30(02): 167-170.
[158]王海泉,郭迎清,祁新杰.多目标遗传算法在H_∞鲁棒控制器设计中的应用[J].推进技术, 2009, 30(04): 468-473.
[159]周斌,崔葛瑾.基于遗传算法的AVR片内振荡器校频处理[J].实验室研究与探索, 2009, 28(09): 30-34.
[160]颜超,连志伟.基于遗传算法的串联混合动力汽车参数优化[J].北京汽车, 2008, (04): 10-13.
[161]侯文英,秦驰越.基于遗传算法的农产品配送路线优化研究[J].内蒙古科技大学学报, 2008, 27(03): 290-292.
[162]黄颖为,李燕培,孙德强.改进遗传算法在包装件物流调度中应用的研究[J].包装工程, 2008, (01): 105-107.
[163]黄晋,夏斌,刘宇红.基于遗传算法的模糊神经网络在交流伺服中的设计[J].重庆工学院学报, 2006, 20(02) : 13-17.
[164]胡江华,常新龙,李进军,等.遗传算法在传感器优化配置中的应用[J].传感器与微系统, 2008, 27(03): 26-28.
[165]林鹰,苏日娜.基于遗传算法求解转移流量的多目标规划方法[J].重庆交通大学学报(自然科学版), 2007, 26(S1) : 147-150.
[166]李玥,孙健国.基于遗传算法的航空发动机多目标优化PID控制[J].航空动力学报, 2008, 23(1):174-178.
[167]陶永华.新型PID控制及其应用[M].北京:机械工业出版社, 2002.
[168]刘金琨.先进PID控制及其Matlab仿真[M].北京:电子工业出版社, 2004
[169] K.K. Ahn, D.Q. Truong. Online tuning fuzzy PID controller using robust extended Kalman filter[J]. Journal of Process Control 19 (2009): 1011-1023.
[170] V. Mukherjee, S.P. Ghoshal. Intelligent particle swarm optimized fuzzyPID controller for AVR system[J]. Electric Power Systems Research 77 (2007): 1689-1698.
[171] R.R. Sumar, A. A. R. Coelho, L. dos S. Coelho. Computational intelligence approach to PID controller design using the universal model[J]. Information Sciences 180 (2010): 3980-3991.
[172] Karam M. Elbayomy, Jiao Zongxia, Zhang Huaqing. PID controller optimization by GA and its performances on the electro-hydraulic servo control system[J]. Chinese Journal of Aeronautics 21(2008): 378-384.
[173] Duan Hai-bin, Wang Dao-bo, Yu Xiu-fen. Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm[J]. Journal of Bionic Engineering, 2006, 3(2):73-78.
[174]陈少白,谭光兴,毛宗源.基于一种人工免疫算法的PID参数优化[J].武汉科技大学学报, 2006, 29(3): 313-315.
[175]钟伟红,关宏伟.基于单纯形法的PID参数优化设计[J].安徽电子信息职业技术学院学报, 2006, 5(24): 101-102.
[176]刘东,冯全源,蒋启龙.基于改进PSO算法的磁浮列车PID控制器参数优化[J].西南交通大学学报, 2010, 45(3):405-410.
[177]曹志松,朴英.基于混合遗传算法的航空发动机PID控制参数寻优[J].航空动力学报,2007, 22(9):1588-1592.
[178]陈阳,杨敏.基于蚁群算法的PID控制器参数优化研究[J].计算机与数字工程, 2009, 37(5): 39-41.
[179] Cooper J, Hinde C. Improving genetic algorithms′efficiency using intelligent fitness functions [J]. IEA/AIE, 2003, 2718:636-643.
[180] Li Songlin, Sun Jianguo. Application of genetic algorithm to solving nonlinear model of aero-engines[J]. Chinese Journal of Aeronautics, 2003, 16(2):69-72.
[181]李英顺,邓长辉,伦淑娴,等.基于遗传算法的真空感应炉PID温度控制系统[J].冶金自动化, 2003(3): 12-15.
[182]张明君,张化光.基于遗传算法优化的神经网络PID控制器[J].吉林大学学报(工学版), 2005, 35(1): 91-96.
[183] Yas?ar Becerikli, Ahmet Ferit Konar, Tar?q Samad. Intelligent optimal control with dynamic neural networks[J]. Neural Networks, 2003, 16( 2): 251-259.
[184] Ralph Linsker. Neural network learning of optimal Kalman prediction and control[J]. Neural Networks, 2008, 21( 9):1328-1343.
[185] Dan Wang, Jialiang Huang, Weiyao Lan, et al. Neural network-based robust adaptive control of nonlinear systems with unmodeled dynamics[J]. Mathematics and Computers in Simulation, 2009, 79(5): 1745-1753.
[186] Anthony J. Calise, Naira Hovakimyan, Moshe Idan. Adaptive output feedback control of nonlinear systems using neural networks[J]. Automatica, 2001, 37(8):1201-1211.
[187] Wachira Daosud, Piyanuch Thitiyasook, Amornchai Arpornwichanop et al. Neural network inverse model-based controller for the control of a steel pickling process[J]. Computers & Chemical Engineering, 2005, 29(10): 2110-2119.
[188] Yu-Jia Zhai, Ding-Li Yu. Neural network model-based automotive engine air/fuel ratio control and robustness evaluation[J]. Engineering Applications of Artificial Intelligence, 2009, 22( 2): 171-180.
[189] V. S. Kodogiannis, P. J. G. Lisboa, J. Lucas. Neural network modelling and control for underwater vehicles[J]. Artificial Intelligence in Engineering, 1996, 10(3): 203-212.
[190] Junghui Chen, Tien-Chih Huang. Applying neural networks to on-line updated PID controllers for nonlinear process control[J]. Journal of Process Control, 2004, 14(2): 211-230.
[191] Ching-Hung Lee, Ching-Cheng Teng. Calculation of PID controller parameters by using a fuzzy neural network[J]. ISA Transactions, 2003, 42(3):391-400.
[192] Giulio D’Emilia, Antonio Marra, Emanuela Natale. Use of neural networks for quick and accurate auto-tuning of PID controller[J]. Robotics and Computer-Integrated Manufacturing, 2007, 23(2):170-179.
[193] Ota, T., Omatu, S. Tuning of the PID control gains by GA[C]. IEEE Conferences, 1996, 1: 272- 274.
[194]樊丁,傅强,戚学锋.遗传算法在航空发动机控制中的应用[J].推进技术,2005, 26(6):544-547.
[195]董劲,黄金泉.航空发动机多变量模糊PID控制[J].航空动力学报, 2003,18(4):538-541.
[196]刘建勋,李应红,陈永刚,等.航空发动机递归神经网络分路式解耦控制[J].航空动力学报, 2005, 20(2):287-292.
[197]李华聪,吴志琨,朱玉斌.涡扇发动机分路式多变量PI解耦控制[J].航空动力学报, 2008, 23(5): 957-960.
[198] Saeed Tavakoli, Ian Griffin, Peter J. Fleming. Decentralized PI Control of a Rolls-Royce Jet Engine[C]. IEEE Conference, 2005, PP:370-375.
[199]何玉彬,李新忠.神经网络控制技术及其应用[M].北京:科学出版社, 2000.
[200] Chatterjee Saibal, Chakravorti Sivaji, Chinmoy Kanti Roy, et al. Wavelet network-based classification of transients using dominant frequency signature[J]. Electric Power Systems Research, 2008, 78(1): 21-29.
[201] Yilmaz, S., Oysal, Y. Fuzzy wavelet neural network models for prediction and identification of dynamical systems[J]. IEEE, 2010, 21(10): 1599-1609
[202] Lixin Dong, Dengming Xiao, Yishan Liang,et al. Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers [J]. Electric Power Systems Research, 2008, 78(1): 129-136.
[203]李金屏,王风涛,杨波. BP小波神经网络快速学习算法研究[J].系统工程与电子技术, 2001, 23(8): 72-75.
[204]何苗,刘希莲,李金屏,等. BP小波神经网络自适应调节步长的改进算法[J].济南大学学报, 2001, 12(5): 315-318.
[205] Krishnaswamy P R, Shukla N V, Deshpande P B, et al. Reference system decoupling for multivariable control [J]. Industrial & Engineering Chemistry Research, 1991, 30(4): 662-670.
[206] Wu W T, Ko J W, Lee H G. Decoupling control of a multivariable system with a desensitizer[J], Industrial & Engineering Chemistry Research, 1993, 32(11): 2937-2941.
[207]张国云.支持向量机算法及其应用研究[D].湖南长沙:湖南大学博士学位论文,2006.1.
[208]牟少敏.核方法的研究及其应用[D].北京:北京交通大学博士学位论文, 2008.6.
[209]姜静清.最小二乘支持向量机及其算法研究[D].吉林长春:吉林大学博士学位论文, 2007.4.
[210] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Process Letter, 1999, 9(3): 293-300.
[211] Marc van de Wal, Bram de Jager. Control Structure Design: A Survey[C]. IEEE Conference, Seattle, Washington, 1995,1:225-229.
[212] Melker Harefors. Control structure design methods applied to a jet engine[C]. IEEE Conferences, 1999, 1:45-50.
[213] R.samar, I. Postlethwaite. Multivariable controller design for a high performance aero- engine[C]. IET coferences, 1994, 2:1312-1317.
[214] A.G.Shutler. control configuration design for the aircraft gas turbine engine[J], Computing & Control engineering journal, 1995, PP:22-28.
[215] Jingan Yang, Yanbin Zhuang. An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem[J]. Applied Soft Computing, 2010, 10(2): 653- 660.
[216]黄华娟,周永权.基于变异算子的人工鱼群混合算法[J].计算机工程与应用, 2009, 45(33): 28-31.
[217] Alireza Rahimi-Vahed, Ali Hossein Mirzaei. A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem[J]. Computers & Industrial Engineering. 2007, 53(4):642-666.
[218] Akbari, R., Mohammadi, A., Ziarati, K. A powerful bee swarm optimization algorithm[C]. IEEE Conferences, 2009 , PP: 1-6.
[219] J. Kennedy, R. Eberhart. Particle swarm optimization[C]. IEEE, 1995,4:1942-1948.
[220]张光澄,王文娟,韩会磊,等.非线性最优化计算方法[M].北京:高等教育出版社,2005.7.
[221]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, 2009,12.
[222]贾英民.鲁棒H∞控制[M].北京:科学出版社,2007.4.
[2] Keck, M.F., Fredlake, J.J. Schwent, G.V. A control concept combining the best of the current hydromechanical and electronic technologies[C]. SAE Paper 740380, 1974.
[3] Bernard L. Koff. Gas turbine technology evolution: A designer’s perspective[J]. Journal of propulsion and power. 2004, 20(4): 577-595.
[4]金茂贤.航空发动机先进控制概念及最新进展[J].航空科学技术, 2005(1): 24-27.
[5]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.
[6]郭虹.航空发动机控制系统的发展趋势[J].沈阳航空工业学院学报, 1997, 14(1):70-74.
[7] McKinnet, John S. Simulation of turbofan engine part I: Description of method and balancing technique[R]. AFAPL-TR-67-125, 1967.
[8] Koenig R W, Fisbbaco L H. Geneng-A program for calculating design and off-design performance for turbojet and turbofan engines[R]. NASA TND-6552, 1972.
[9] Fisbbacb L H, Koenig R W. Geneng II-A program for calculating design and off-design performance of two- and three-spool turbofans with as many as three nozzles[R]. NADA TND-6553, 1972.
[10] Sellers J F, Krosel S M, Szuch J R. Dyngen-a program for calculating steady-state and transient performance of turbojet and turbofan engines[R]. NASA TND-7901, 1975.
[11] Al Vloponi. Enhanced self tuning on-board real-time model(eSTORM) for aircraft engie performance health tracking[R]. NASA/CR-2008-215272.
[12]华清.某型涡扇发动机全包线实时仿真模型[J].航空发动机, 2002,(1):47-52.
[13]周文祥.航空发动机及控制系统建模与面向对象的仿真研究[D].南京:南京航空航天大学博士学位论文, 2006.
[14]袁鸯.发动机自适应建模及神经网络控制[D].南京:南京航空航天大学硕士学位论文, 2005.
[15] Lupold R H, Gallops G W, Kerr L J, et al. Estimating in-flight engine performance variations using Kalman filter concepts[R]. AIAA-1989-2584.
[16] Sugiyama N. Derivation of ABCD system matrices from nonlinear dynamic simulation of jet engines[R]. AIAA-1992-3319.
[17] P. Verboven, P. Guillaume, B. Cauberghe. Multi-variable frequency–response curve fittingwith application to modal parameter estimation[J]. Automatica, 2005, 41(10):1773– 1782.
[18]冯正平,孙健国,黄金泉,等.一种建立航空发动机状态变量模型的新方法[J].航空动力学报, 1998, 13(4):435-438.
[19]郑铁军,王曦,罗秀芹,等.建立航空发动机状态空间模型的修正方法[J].推进技术, 2005, 26(1):46-49.
[20]褚健,俞立,苏宏业.鲁棒控制理论及其应用[M].杭州:浙江大学出版社, 2000.
[21] G. Zames. Functional analysis applied to nonlinear feedback systems[J]. IEEE, 1963, 10(3):392-404.
[22] B.D.P. Anderson, J.B. Moore. Optimal control: linear quadratic methods[M]. New Jersey, 1979.
[23] Doyle, J.C., Stein, G. Multivariable feedback design: concepts for a classical/modern synthesis[J]. IEEE Trans. On Automatic Control, 1981, 26(1):4-16.
[24] T. T. Lee, M. S. Chen. Robustness recovery of LQ based multivariable control designs[J]. International Journal of Control, 1987, 45(4): 1131-1136.
[25] D Z.J. Palmor, Y. Halevi. On the recovery procedure for LQG systems[J]. Automatica, 1988, 24(2): 275-277.
[26] Garg, S., Athans, M. The LQG/LTR procedure for multivariable feedback control design[J]. IEEE Trans. On Automatic Control, 1986 , 32(2): 105-114.
[27] Michel Kinnaert, Youbin Peng. Discrete-time LQG/LTR technique for systems with time delays[J]. Systems & Control Letters, 1990, 15(4): 303-311.
[28] Athans, M., Petros, K., Efthimios, K., et al. Linear quadratic gaussian with loop transfer recovery methodology for the F-100 Engine[J]. Journal of Guidance, 1986, 9(1): 45-52.
[29] Gokcek, C. Kabamba, P.T. Meerkov, S.M. An LQR/LQG theory for systems with saturating actuators[J]. IEEE Trans. Automatic Control, 2001, 46(10): 1529-1542.
[30] Chaules, L. M., Peter, S. M. On an assumed convergence result in the LQG/LTR technique[J]. IEEE Trans. Automatic Control, 1991, 36(1): 951-952.
[31] Stein, G.; Athans, M. The LQG/LTR procedure for multivariable feedback control design[J]. IEEE, 1987, 32(2): 105-114.
[32] A.A. Rodriguez, M. Athans. Multivariable control of a twin lift helicopter system using the LQG/LTR design methodology[C]. IEEE, 1986, pp: 1325-1332.
[33] Hsiang-Jui Wu, Ying-Yu Tzou. LQG/LTR robust control of an AC induction servo drive[C]. IEEE, 1991, pp: 613-619.
[34] Tadjine M., M'Saad M., Dugard L. An overview on the LQG/LTR method using the delta operator[C]. IEEE, 1992, 4: 3728– 3733.
[35] Saberi, A., Sannuti, P. Observer design for loop transfer recovery and for uncertain systems[J]. IEEE Trans. Automatic Control, 1990, 35(8): 878-897.
[36] Doyle, J. C., Garg, S. Robustness with observers[J]. IEEE Trans. Automatic Control, 1979, 24(4): 607-611.
[37] Zhang, Z. H., Freudenberg, J. S. Loop transfer recovery for nonminimum phase plants[J]. IEEE Trans. Automatic Control, 1990, 35(5):547-533.
[38] Haessig, D. Selection of LQG/LTR weighting matrices through constrained optimization [C]. IEEE, 1995, 1: 458– 460.
[39] Prakash, R., Rao, S.V. LQG/LTR controller design using a reduced order model[C]. IEEE, 1989, pp: 343-346.
[40] Young-Moon Park, Myoen-Song Choi, Jeong-Woo Lee, et al. An auxiliary LQG/LTR robust controller design for cogeneration plants[J]. IEEE, 1996, 11(2): 407-413.
[41] Ray L. R. Stability robustness of uncertain LQG/LTR systems[J]. IEEE Trans. Automatic Control, 1993, 38(2): 304-308.
[42] C. Zhou, J.R. Whiteley, E.A. Misawa, et al. Application of enhanced LQG/LTR for distillation control [J]. IEEE, 1995, 15(4):56-63.
[43] Garg, S. Turbofan engine control system design using the LQG/LTR methodology[R]. NASA-CR-182303, 1989.
[44] Willian, H. P., Athans, M., Austin S. III. Multivariable control of the GE T700 engine using the LQG/LTR design methodology[C]. ACC, 1986, pp:1293-1312.
[45] James, A.M. Multivariable control of a submarine using the LQG/LTR method[C]. AC C, 1986, pp:1313-1324.
[46] Zhou, K. M. Essentials of robust control[M]. Prentice Hall, 1997.
[47]周克敏, Doyle, J. C., Glover, K.鲁棒与最优控制[M].北京:国防工业出版社,2002.
[48] Zames, G. Feedback and optimal sensitivity: model reference transformations, multiplicative seminorms, and Approximate Inverses[J]. IEEE Trans. Automatic Control, 1981, 26(2): 301-320.
[49] Zames, G., Francis, B. Feedback, minimax sensitivity and optimal robustness[J]. IEEE Trans. Automatol Control, 1983, 28(4): 585-601.
[50] Chang, B.C., Pearson, J.B. Optimal disturbance reduction in linear multivariable systems[J].IEEE Trans. Automatic Control, 1984, 29(6): 880-887.
[51] Glover, K. All optimal Hankel - norm approximations of linear multivariable systems and their L∞error bounds[J]. International Journal of Control, 1984, 39(6):1115-1193.
[52] Doyle, J. C. Lecture notes in advances in multivariable control. ONR/Honeywell Workshop, Minneapolis, 1984.
[53] Huibert Kwakernaak. Minimax frequency domain performance and robustness optimization of linear feedback systems[J]. IEEE, 1985, 30(10):994-1004.
[54] Francis, B. A., Doyle, J. C. Linear control theory with H∞optimality criterion[J]. SIAM J. Control Optimization, 1987, 25(4):815-844.
[55] Ball, J. A., Cohen, N. The sensitivity minimization in an H∞norm: parameterization of all optimal solutions[J]. Int. J. Control, 1987, 46:785-861.
[56] Green, M., Glover, K., D.J.N. Limebeer, et al. A J-spectral factorization approach to H∞control[J]. SIAM J. Control Opt, 1990, 28(1990):1350-1371.
[57] Kimura, H. (J,J')-lossless factorization based on conjugation[J]. System & Control Letters, 1992, 19(2):95-109.
[58] Doyle, J. C., Glover, K., Khargonekar, P. P. et al. State-space solutions to standard H2 and H∞control problems[J]. IEEE Trans. Automatic Control, 1989, 34(8):831-847.
[59] Basar, T., Bernhard, P. H∞optimal control and related minimax design problems: a dynamic game approach[M], Boston, MA :Birkhaeuser,2008.
[60] Tadmor, G. Worst case design in the time domain: the maximum principle and the standard H∞problem[J]. Mathematics of Control, Signals, and Systems (MCSS), 1990, 3(4):301-324.
[61] Guesalaga, A. R., Kropholler, H. W. Improved temperature and humidity control H∞synthesis[J]. IEEE Proc. Pt D, 1990, 137(3):374-380.
[62] Rotstein, H., Sideris A. MIMO H∞-control with time domain constraints[C]. IEEE Conference, 1992, 3:2601-2606.
[63] Eberhardt, R.L., Wise, K.A. High gain H∞feedback control for an anti-air missile[C]. IEEE Conference, 1993, 1: 449-455.
[64] Naidu, D.S., Charalambous, C.D., Moore, K.L., et al. H∞-optimal control of singularly perturbed discrete-time systems, and risk-sensitive control[C]. IEEE Conference,1994 , 2: 1706-1711.
[65] Xie, L., de Souza Carlos, E. Robust H∞control for linear systems with norm-bounded time-varying uncertainty [J]. IEEE, 1992, 37(8): 1188-1191.
[66] Safonov, M. G., Chung, R. Y., Flashner, H. H∞robust control synthesis for a large space structure[C]. IEEE Conferences, 1988, PP:2038-2045.
[67]王曦,陈华荣.不确定性系统鲁棒H∞/H2混合控制在航空发动机中的应用[J].航空动力学报, 2002, 17(2): 250-254.
[68]谢光华,曾庆福.基于LMI的航空发动机混合H2/H∞输出反馈控制[J].推进技术. 2000, 21(6): 28-31.
[69] M. Sojoodi, V.J. Majd. A technical approach to H2 and H∞control of a flexible transmission system[C]. IEEE Conferences, 2010, PP:124-128
[70] Ian Postlethwaite, Matthew C. Turner,Guido Herrmann. Robust control applications. Annual Reviews in Control, 31 (2007): 27-39.
[71] V. Suplin, U. Shaked. Robust H∞output-feedback control of systems with time-delay. Systems & Control Letters, 57 (2008): 193-199.
[72] Davide Fissore. Robust control in presence of parametric uncertainties: Observer- based feedback controller design. Chemical Engineering Science, 63(2008):1890-1900.
[73] Tao Liu, Furong Gao. Robust two-dimensional iterative learning control for batch processes with state delay and time-varying uncertainties[J]. Chemical Engineering Science, 65(2010): 6134-6144.
[74] H. Austin Spang, Harold Brown. Control of jet engines[J]. Control engineering practice 7(1999):1043-1059.
[75] W. Merrill, B. Lehtinen, J. Zeller. The role of modern control theory in the design of controls for aircraft turbine engines[R]. AIAA-1982-320.
[76] B. Lehtinen. Multivariable control altitude demonstration on the F100 turbofan engine[R]. AIAA-79-1204.
[77]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.
[78] Michael Athans, Petros Kapasouris, Efthimios Kappos, et al. Linear-Quadratic Gaussian with loop transfer recovery methodology for the F-100 engine[R]. Journal of Guidance, Control, and Dynamics, 1986, 9(1):45-52.
[79] Athans M., Cambridge Ma., Kappos E., et al. Multivariable control for the F-100 engine using the LQG/LTR methodology[R]. AIAA-1984-1910, pp: 434-444.
[80] W. F. Dellinger, A. T. Alouani. Application of linear multivariable control design for the GE-21 turbofan jet engine[C]. IEEE Conference, 1989, pp:167-171.
[81] Yuan Xiao-chuan, Guo Ying-qing. Non-fully recovering LQG/LTR method and its application in aeroengine control[R]. AIAA, 2007-5716.
[82]郭迎清,吴丹,张华.全飞行包线LQG/LTR多变量控制器设计[J].航空发动机, 2002, (4): 44-47.
[83] M.Harefors. Application of H∞robust control to the RM12 jet engine[J]. Control Eng. Practice, 1997, 5(9):1189-1201.
[84] Dean K. Frederich, Sanjay Garg, Shrider Adibhatla. Turbofan engine control design using robust multivariable control technologies[J]. IEEE transactions on control systems technology, 1996, 8(6):961-970.
[85] Shrider Adibhatla, George J. Collier, Xin Zhao, et al. H∞control design for a jet engine[R]. AIAA-98-3753.
[86] Yang Gang, Sun jianguo. Reduced order H∞/LTR method for aeroengine control system[J]. Chinese journal of aeronautics, 2004, 17(3):129-135.
[87] Krishna Kumar, Kulkarni Nilesh. Inverse adaptive neuro-contrl of a turbo-fan engine[R]. AIAA-1999-394.
[88]包睿,黄金泉.航空发动机反馈神经网络自适应控制方法[J].航空发动机, 2007, 33(4): 46-49.
[89]梁华,李应红,尉询楷,等.航空发动机的支持向量机自适应PID控制[J].航空动力学报,2007, 22(1):137-141.
[90] Richard Y. Chiang, Fred Y. Hadaegh. Robust jet engine control[R]. AIAA-96-3927.
[91]李华聪,荣立烨,朱玉斌.基于QDRNN网络的航空发动机多变量解耦控制[J].航空动力学报, 2007, 22(11): 1921-1924.
[92]傅强,樊丁.航空发动机PID神经网络解耦控制[J].推进技术, 2007, 28(2):208-210.
[93] Junxia Mu, David Rees, G P Liu. Advanced controller design for aircraft gas turbine engines[J]. Control Engineering Practice, 2005, 13(8): 1001–1015.
[94]姚彦龙,孙健国.基于神经网络逆控制的发动机直接推力控制[J].推进技术,2008, 29(2): 249-252.
[95]刘建勋,李应红,陈永刚,等.航空发动机LQR控制的模糊神经网络方法[J].航空动力学报, 2004, 19(6): 838-843.
[96]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, 2004.
[97]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.
[98]许东,吴铮.基于Matlab 6.X的系统分析与设计---神经网络[M].西安:西安电子科技大学出版社, 2003.
[99] Adibhatla S, Brown H, Gastineau Z. Intelligent engine control (IEC)[R]. AIAA-92-3484, 1992.
[100] Adibhatla S, Lewis T. Model-based intelligent digital engine control (MoBIDEC)[R]. AIAA-97-3192, 1997.
[101]王文娟,马洪忠,刘长林.无人机综合飞行/推力矢量控制[J].航空学报, 2008, 29(增刊):s150-s156.
[102] Barton S. Thrust Vectoring Control Concepts and Issues[J]. SAE-90-1848, 1990
[103]姚彦龙,孙健国.基于神经网络逆控制的发动机直接推力控制[J].推进技术,2008, 29(2):249-252.
[104] Manfredi Maggiore, Raul Ordonez, Kevin M. Passion, et al. Estimator design in jet engine applications[J]. Engineering Applications of Artificial Intelligence, 16(2003):579-593.
[105]姚彦龙,孙健国.自适应遗传神经网络算法在推力估计器设计中的应用[J].航空动力学报, 2007, 22(10):1748-1753.
[106] Timothy R. Conners, Robert L. Sims. Full flight envelope direct thrust measurement on a supersonic aircraft[R]. NASA/TM-1998-206560.
[107] Takahisa Kobayashi, Donald L. Simon, Jonathan S. Litt. Application of a constant gain extended Kalman filter for in-flight estimation of aircraft engine performance parameters[R]. NASA/TM-005-213865.
[108] Santanu Chatterjee, Jonathan S. Litt. Online model parameter estimation of jet engine degradation for autonomous propulsion control[R]. NASA/TM-2003-212608.
[109]陈恬,孙健国.基于相关性分析和神经网络的直接推力控制[J].南京航空航天大学学报, 2005, 37(2):183-187.
[110] Joanthan S.Litt, Neerav Shah, T.Shane Sowers. A demonstration of a retrofit architecture for intelligent control and diagnostics of a turbofan engine [R]. NASA/TM-2005-214019.
[111]孙健国, Vasilyev V., Hyasov B. Advanced multivariable control systems of aeroengines[M].北京:北京航空航天大学出版社, 2005.
[112]陈恬.无人作战飞机智能发动机控制技术研究[D].南京:南京航空航天大学博士论文,2006.
[113]兰春贤.发动机部件跟踪滤波器解析余度研究[D].南京:南京航空航天大学硕士学位论文, 1992.
[114] Tich, E. J. etc. Performance seeking control for cruise optimization in fighter aircraft[C]. AIAA-86-1929.
[115]兰春贤,孙健国.基于部件跟踪滤波器的解析余度技术[J].航空动力学报, 1994, 9(3):289-293.
[116] Martin D.E., Edwards CA. On the estimation algorithm for adaptive performance optimization of turbofan engines[R]. AIAA-93-1823.
[117]郁阳琳.基于部件跟踪滤波器的发动机数控系统故障检测、隔离和适应[D].南京:南京航空航天大学硕士学位论文, 1997.
[118] Yedavalli R.K. Robust estimation and fault diagnostics for aircraft engines with uncertain model data [J]. IEEE Conferences, 2007, PP:2822-2827.
[119] Donald Simon, Sanjay Garg. A systematic approach for model-based aircraft engine performance estimation[C]. AIAA-2009-1872.
[120] Trindel A. Maine, Gelnn B.Giyard, Heather H. Lambert. A preliminary evaluation of an F100 engine parameter estimation process using flight data[R]. AIAA-1990-1921.
[121] Takahisa Kobavashi, Donald L. Simon, Jonathan S. Litt. Application of a constant gain extended Kalman filter for in-flight estimation of aircraft engine performance parameters[R]. NASA/TM-2005-213865.
[122] Jonathan S. Litt. An optimal orthogonal decomposition method for Kalman Filter-based turbofan engine thrust estimation[R]. NASA/TM-2005-213864.
[123] Jonathan S. Litt, T.Shane Sower, Sanjay Garg. A retrofit control architecture to maintain engine peroformance with usage[R]. NASA/TM-2007-214977.
[124]姚彦龙.航空发动机神经网络直接推力逆控制[D].南京:南京航空航天大学硕士学位论文, 2005.
[125] Haiguo Xiong, Qitao Huang, Hongzhou Jiang, et al. Study on modeling and simulation of a flight simulator engine system[C]. IEEE Conferences, 2007, PP: 226-230.
[126] Mckinney, John S. Simulation of turbofan engine. Part II. User’s Manual and computer program listing[R]. AFAPL-TR-67-125-pt.-2.
[127] Szuch, John R. HYDES–A generalized hybrid computer program ofr studying turbojet or turbofan engine dynamics[R]. NASA TM x-3014.
[128] John S. Orme, Gerard S. Schkolnik. Flight assessment of the onboard propulsion system model for the performance seeking control algorithm on an F-15 aircraft[R]. NASA TechnicalMemorandum 4705.
[129]邹先权.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学位论文, 2008.12.
[130]陶金伟.航空发动机组态建模仿真技术研究[D].南京:南京航空航天大学硕士学位论文, 2009. 12.
[131]唐海龙,张津.面向对象的航空发动机性能仿真程序的设计方法研究[J].航空动力学报, 1999, 14(4):421-424.
[132]杨蔚华,孙健国.发动机实时建模技术的新发展[J].航空动力学报, 1995, 10(4) : 402-406.
[133]袁春飞,姚华,杨刚.航空发动机机载实时自适应模型研究[J].航空学报, 2006, 27(14): 561-564.
[134]周文祥,黄金泉,黄开明.航空发动机简化实时模型仿真研究[J].南京航空航天大学学报, 2005, 37(2): 251-255.
[135]于龙江,朴英.一种简化算法的航空发动机全状态数学模型[J].航空动力学报, 2008, 23(3): 510-515.
[136]杨刚,孙健国,黄向华,等.一种不需要迭代的发动机辅助变量建模方法[J].航空动力学报, 2003, 18(2): 289-294.
[137]李开宁.数值计算方法引论[M].北京:航空工业出版社, 2002.8.
[138]冯正平,孙健国.航空发动机小偏差状态变量模型的建立方法[J].推进技术, 2001, 22(1): 54-57.
[139]李秋红,孙健国.基于遗传算法的航空发动机状态变量模型建立方法[J].航空动力学报. 2006, 21(2): 427-431.
[140]周文祥,单晓明,耿志东,等.自寻优求解法建立涡轴发动机状态变量模型[J].航空动力学报. 2008, 23(12): 2314-2320.
[141]刘景茂,樊丁.基于SMI辨识的航空发动机模型建立[J].计算仿真, 2008, 23(3): 580-584.
[142] J.H.Holland. Adaptation in natural and artificial systems[M]. Michigan: University of MichiganPress, 1975.
[143] Goldberg D E. Genetic algorithms in search, optimization and machine learning[M]. Adssison-Wesley, 1989.
[144] James T., Brown E., Ragsdale C.T. Grouping genetic algorithm for the blockmodel problem[J]. IEEE, 2010,14(1):103-111.
[145] Chang Wook Ahn, Ramakrishna R.S. A genetic algorithm for shortest path routing problem and the sizing of populations[J]. IEEE, 2002, 6(6):566-579.
[146] Srinivas N, Deb K. Multi-objective optimization using nondominated sorting in genetic algorithms [J]. Evolutionary Computation, 1994, 2(3): 221-248.
[147] Yiu-Wing Leung, Yuping Wang. An orthogonal genetic algorithm with quantization for global numerical optimization[J]. IEEE, 2001, 5(1): 41-53.
[148] Shiu Yin Yuen, Chi Kin Chow. A genetic algorithm that adaptively nutates and never revisits[J]. IEEE, 2009, 13(2):454-472.
[149] Harik G.R., Lobo F.G., Goldberg D.E. The compact genetic algorithm[J]. IEEE, 1999, 3(4): 287-297.
[150] Chan K.C.C., Lee V., Leung H. Generating fuzzy rules for target tracking using a steady-state genetic algorithm[J]. IEEE, 1997, 1(3):189-200.
[151]陈国良,王煦法,庄镇泉,等.遗传算法及其应用[M].北京:人民邮电出版社,1996.
[152]云庆夏.进化计算[M].北京:冶金工业出版社, 2000.
[153]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社, 1999.
[154]王小平,曹立明.遗传算法--理论、应用与软件实现[M].西安:西安交通大学出版社,2002.
[155] Guo Pengfei, Wang Xuezhi, Han Yingshi. The enhanced genetic algorithms for the optimization design[C]. IEEE Conferences, 2010, 7: 2990 - 2994.
[156] Duan Bin, Sun Tongjing, Li Zhenhua, et al. Improved Chaos-GA-PID control in digital invert power supply[C]. IEEE Conferences, 2009 , PP: 2-315 - 2-318.
[157]张世英,胡宇,朱杰堂.基于遗传算法的航空发动机模糊优化控制[J].弹箭与制导学报, 2010, 30(02): 167-170.
[158]王海泉,郭迎清,祁新杰.多目标遗传算法在H_∞鲁棒控制器设计中的应用[J].推进技术, 2009, 30(04): 468-473.
[159]周斌,崔葛瑾.基于遗传算法的AVR片内振荡器校频处理[J].实验室研究与探索, 2009, 28(09): 30-34.
[160]颜超,连志伟.基于遗传算法的串联混合动力汽车参数优化[J].北京汽车, 2008, (04): 10-13.
[161]侯文英,秦驰越.基于遗传算法的农产品配送路线优化研究[J].内蒙古科技大学学报, 2008, 27(03): 290-292.
[162]黄颖为,李燕培,孙德强.改进遗传算法在包装件物流调度中应用的研究[J].包装工程, 2008, (01): 105-107.
[163]黄晋,夏斌,刘宇红.基于遗传算法的模糊神经网络在交流伺服中的设计[J].重庆工学院学报, 2006, 20(02) : 13-17.
[164]胡江华,常新龙,李进军,等.遗传算法在传感器优化配置中的应用[J].传感器与微系统, 2008, 27(03): 26-28.
[165]林鹰,苏日娜.基于遗传算法求解转移流量的多目标规划方法[J].重庆交通大学学报(自然科学版), 2007, 26(S1) : 147-150.
[166]李玥,孙健国.基于遗传算法的航空发动机多目标优化PID控制[J].航空动力学报, 2008, 23(1):174-178.
[167]陶永华.新型PID控制及其应用[M].北京:机械工业出版社, 2002.
[168]刘金琨.先进PID控制及其Matlab仿真[M].北京:电子工业出版社, 2004
[169] K.K. Ahn, D.Q. Truong. Online tuning fuzzy PID controller using robust extended Kalman filter[J]. Journal of Process Control 19 (2009): 1011-1023.
[170] V. Mukherjee, S.P. Ghoshal. Intelligent particle swarm optimized fuzzyPID controller for AVR system[J]. Electric Power Systems Research 77 (2007): 1689-1698.
[171] R.R. Sumar, A. A. R. Coelho, L. dos S. Coelho. Computational intelligence approach to PID controller design using the universal model[J]. Information Sciences 180 (2010): 3980-3991.
[172] Karam M. Elbayomy, Jiao Zongxia, Zhang Huaqing. PID controller optimization by GA and its performances on the electro-hydraulic servo control system[J]. Chinese Journal of Aeronautics 21(2008): 378-384.
[173] Duan Hai-bin, Wang Dao-bo, Yu Xiu-fen. Novel Approach to Nonlinear PID Parameter Optimization Using Ant Colony Optimization Algorithm[J]. Journal of Bionic Engineering, 2006, 3(2):73-78.
[174]陈少白,谭光兴,毛宗源.基于一种人工免疫算法的PID参数优化[J].武汉科技大学学报, 2006, 29(3): 313-315.
[175]钟伟红,关宏伟.基于单纯形法的PID参数优化设计[J].安徽电子信息职业技术学院学报, 2006, 5(24): 101-102.
[176]刘东,冯全源,蒋启龙.基于改进PSO算法的磁浮列车PID控制器参数优化[J].西南交通大学学报, 2010, 45(3):405-410.
[177]曹志松,朴英.基于混合遗传算法的航空发动机PID控制参数寻优[J].航空动力学报,2007, 22(9):1588-1592.
[178]陈阳,杨敏.基于蚁群算法的PID控制器参数优化研究[J].计算机与数字工程, 2009, 37(5): 39-41.
[179] Cooper J, Hinde C. Improving genetic algorithms′efficiency using intelligent fitness functions [J]. IEA/AIE, 2003, 2718:636-643.
[180] Li Songlin, Sun Jianguo. Application of genetic algorithm to solving nonlinear model of aero-engines[J]. Chinese Journal of Aeronautics, 2003, 16(2):69-72.
[181]李英顺,邓长辉,伦淑娴,等.基于遗传算法的真空感应炉PID温度控制系统[J].冶金自动化, 2003(3): 12-15.
[182]张明君,张化光.基于遗传算法优化的神经网络PID控制器[J].吉林大学学报(工学版), 2005, 35(1): 91-96.
[183] Yas?ar Becerikli, Ahmet Ferit Konar, Tar?q Samad. Intelligent optimal control with dynamic neural networks[J]. Neural Networks, 2003, 16( 2): 251-259.
[184] Ralph Linsker. Neural network learning of optimal Kalman prediction and control[J]. Neural Networks, 2008, 21( 9):1328-1343.
[185] Dan Wang, Jialiang Huang, Weiyao Lan, et al. Neural network-based robust adaptive control of nonlinear systems with unmodeled dynamics[J]. Mathematics and Computers in Simulation, 2009, 79(5): 1745-1753.
[186] Anthony J. Calise, Naira Hovakimyan, Moshe Idan. Adaptive output feedback control of nonlinear systems using neural networks[J]. Automatica, 2001, 37(8):1201-1211.
[187] Wachira Daosud, Piyanuch Thitiyasook, Amornchai Arpornwichanop et al. Neural network inverse model-based controller for the control of a steel pickling process[J]. Computers & Chemical Engineering, 2005, 29(10): 2110-2119.
[188] Yu-Jia Zhai, Ding-Li Yu. Neural network model-based automotive engine air/fuel ratio control and robustness evaluation[J]. Engineering Applications of Artificial Intelligence, 2009, 22( 2): 171-180.
[189] V. S. Kodogiannis, P. J. G. Lisboa, J. Lucas. Neural network modelling and control for underwater vehicles[J]. Artificial Intelligence in Engineering, 1996, 10(3): 203-212.
[190] Junghui Chen, Tien-Chih Huang. Applying neural networks to on-line updated PID controllers for nonlinear process control[J]. Journal of Process Control, 2004, 14(2): 211-230.
[191] Ching-Hung Lee, Ching-Cheng Teng. Calculation of PID controller parameters by using a fuzzy neural network[J]. ISA Transactions, 2003, 42(3):391-400.
[192] Giulio D’Emilia, Antonio Marra, Emanuela Natale. Use of neural networks for quick and accurate auto-tuning of PID controller[J]. Robotics and Computer-Integrated Manufacturing, 2007, 23(2):170-179.
[193] Ota, T., Omatu, S. Tuning of the PID control gains by GA[C]. IEEE Conferences, 1996, 1: 272- 274.
[194]樊丁,傅强,戚学锋.遗传算法在航空发动机控制中的应用[J].推进技术,2005, 26(6):544-547.
[195]董劲,黄金泉.航空发动机多变量模糊PID控制[J].航空动力学报, 2003,18(4):538-541.
[196]刘建勋,李应红,陈永刚,等.航空发动机递归神经网络分路式解耦控制[J].航空动力学报, 2005, 20(2):287-292.
[197]李华聪,吴志琨,朱玉斌.涡扇发动机分路式多变量PI解耦控制[J].航空动力学报, 2008, 23(5): 957-960.
[198] Saeed Tavakoli, Ian Griffin, Peter J. Fleming. Decentralized PI Control of a Rolls-Royce Jet Engine[C]. IEEE Conference, 2005, PP:370-375.
[199]何玉彬,李新忠.神经网络控制技术及其应用[M].北京:科学出版社, 2000.
[200] Chatterjee Saibal, Chakravorti Sivaji, Chinmoy Kanti Roy, et al. Wavelet network-based classification of transients using dominant frequency signature[J]. Electric Power Systems Research, 2008, 78(1): 21-29.
[201] Yilmaz, S., Oysal, Y. Fuzzy wavelet neural network models for prediction and identification of dynamical systems[J]. IEEE, 2010, 21(10): 1599-1609
[202] Lixin Dong, Dengming Xiao, Yishan Liang,et al. Rough set and fuzzy wavelet neural network integrated with least square weighted fusion algorithm based fault diagnosis research for power transformers [J]. Electric Power Systems Research, 2008, 78(1): 129-136.
[203]李金屏,王风涛,杨波. BP小波神经网络快速学习算法研究[J].系统工程与电子技术, 2001, 23(8): 72-75.
[204]何苗,刘希莲,李金屏,等. BP小波神经网络自适应调节步长的改进算法[J].济南大学学报, 2001, 12(5): 315-318.
[205] Krishnaswamy P R, Shukla N V, Deshpande P B, et al. Reference system decoupling for multivariable control [J]. Industrial & Engineering Chemistry Research, 1991, 30(4): 662-670.
[206] Wu W T, Ko J W, Lee H G. Decoupling control of a multivariable system with a desensitizer[J], Industrial & Engineering Chemistry Research, 1993, 32(11): 2937-2941.
[207]张国云.支持向量机算法及其应用研究[D].湖南长沙:湖南大学博士学位论文,2006.1.
[208]牟少敏.核方法的研究及其应用[D].北京:北京交通大学博士学位论文, 2008.6.
[209]姜静清.最小二乘支持向量机及其算法研究[D].吉林长春:吉林大学博士学位论文, 2007.4.
[210] Suykens J A K, Vandewalle J. Least squares support vector machine classifiers[J]. Neural Process Letter, 1999, 9(3): 293-300.
[211] Marc van de Wal, Bram de Jager. Control Structure Design: A Survey[C]. IEEE Conference, Seattle, Washington, 1995,1:225-229.
[212] Melker Harefors. Control structure design methods applied to a jet engine[C]. IEEE Conferences, 1999, 1:45-50.
[213] R.samar, I. Postlethwaite. Multivariable controller design for a high performance aero- engine[C]. IET coferences, 1994, 2:1312-1317.
[214] A.G.Shutler. control configuration design for the aircraft gas turbine engine[J], Computing & Control engineering journal, 1995, PP:22-28.
[215] Jingan Yang, Yanbin Zhuang. An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem[J]. Applied Soft Computing, 2010, 10(2): 653- 660.
[216]黄华娟,周永权.基于变异算子的人工鱼群混合算法[J].计算机工程与应用, 2009, 45(33): 28-31.
[217] Alireza Rahimi-Vahed, Ali Hossein Mirzaei. A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem[J]. Computers & Industrial Engineering. 2007, 53(4):642-666.
[218] Akbari, R., Mohammadi, A., Ziarati, K. A powerful bee swarm optimization algorithm[C]. IEEE Conferences, 2009 , PP: 1-6.
[219] J. Kennedy, R. Eberhart. Particle swarm optimization[C]. IEEE, 1995,4:1942-1948.
[220]张光澄,王文娟,韩会磊,等.非线性最优化计算方法[M].北京:高等教育出版社,2005.7.
[221]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, 2009,12.
[222]贾英民.鲁棒H∞控制[M].北京:科学出版社,2007.4.