基于Terminal滑模的空天飞行器再入鲁棒自适应控制
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摘要
空天飞行器(ASV)是新一代的航空航天器,它将填补临近空间(Near Space)航空航天活动的空白,并且具有极其重要的军事价值。ASV再入大气层的飞行环境变化剧烈、干扰大,因而其姿态控制系统的设计是一项非常前沿的研究课题。本文围绕这一问题,对ASV再入飞行的高精度、强稳定、快速自适应鲁棒控制问题展开了较为深入的研究。
首先,在实验室已有成果的基础上,建立了ASV的再入模型,其控制系统包含反作用发动机(RCS)和控制舵面,以满足不同飞行阶段的控制要求。然后对其开环性能进行了分析,结果表明该模型具有复杂的非线性、耦合性以及快速时变性等特点,具有一定的代表性,可以为后续的研究工作提供参考。
其次,基于Terminal滑模控制方法研究了ASV再入飞行控制系统的设计问题。在对综合干扰进行一定假设的条件下,得到了具有有限时间收敛特性的Terminal滑模闭环控制系统,可以保证系统跟踪误差在有限时间内收敛到零,从而提高了系统的响应速度。为了消除前述假设,提出另一种基于神经网络的自适应Terminal滑模控制方案,可以在线消除系统的综合干扰,并对闭环稳定性进行了严格的证明。两种方案都在ASV高超声速再入飞行条件下进行了仿真验证。
随后,为了更有效消除综合干扰的影响,且易于工程应用,提出了基于模糊干扰观测器的自适应Terminal滑模控制方法,并基于Lyapunov稳定性理论给出了严格的稳定性证明。进一步,通过改进模糊干扰观测器的自适应律,设计了快速模糊干扰观测器,大大加快了其逼近速度,并将其应用到ASV再入飞控系统的设计中。最后的仿真结果进一步显示了该方法的优越性。
在第五章中,重点研究了闭环系统的有限时间收敛特性和自适应律运算效率的问题。为了明确得到基于快速模糊干扰观测器的闭环系统的收敛时间,保留了模糊系统的逼近误差。此时,闭环系统误差不再收敛到零,而是收敛到可以任意小的某些区域。本章研究的另一个问题是如何减轻机载计算机的运算量。通过提出一种新型参数自适应律,将模糊系统在线调整的参数减少到两个,大大提高了运算效率。
第六章首先分析了Terminal滑模面设计参数对滑模区域及达到时间的影响,然后设计了基于T-S模糊模型的时变自适应Terminal滑模控制系统。该方法采用T-S模糊系统在线逼近综合干扰,且时变滑模面有利于增大滑模区域,提高系统的鲁棒性,缩短到达时间。
最后,研究了ASV状态观测器的设计问题。在考虑系统量测噪声的情况下,对系统进行两次扩张,设计了基于超扭曲算法(Super-twisting)的新型扩张状态观测器,保证观测误差在有限时间内收敛到零。接着利用可以测量的状态,结合精确微分器,对ASV未知状态进行了估计,该方法简单,精度高,易于工程应用。
Aerospace vehicle (ASV) is the reusable flying vehicle of next generation. The vehicle fills the blanks of spaceraft’s activities in near space and has very important martial values. For the varieties of the aerosphere and the large disturbances, the design of attitude control system of ASV is a very difficult problem during the re-entry. In this dissertation, we focus on the high-precision, strong-stabilization, fast adaptive robust control of ASV re-entry.
First of all, a simulation model of an ASV re-entry mode is presented based on the contributions of our lab. This model includes two kinds of actuators, the RCS and the control surface deflections, to satisfy different control tasks in the flight. Open-loop dynamics and stability characteristics demonstrate that this model is complex-nonlinear, coupling, fast time-varying, so it is able to be used in the following research works.
Secondly, a discussion is devoted to the design of the control systems of ASV re-entry based on the terminal sliding mode control. Under the assumption to the totall disturbances, a terminal sliding mode control scheme with finite-time convergence is proposed. This scheme can promise that the tracking errors converge to zeros in finite time, and can improve the response speed of the closed-loop system rapidly. Then, in order to release the assumption above, an adaptive terminal sliding mode control based on neural networks is designed. The controller can compensate the totall disturbances online. After strict analysis to the stabilities, the simulation results are given to demonstrate the good performances of the controllers under the hypersonic conditions.
In the following, an adaptive terminal sliding mode control method that based on the fuzzy disturbance observer (FDO) is designed, in order to eliminate the influences of the totall disturbances and be applicated easily. The stabilities of this method are proved strictly based on Lyapunov’s direct method. Furthmore, by modifying the adaptive law of the FDO, a fast fuzzy disturbance observer (FFDO) is presented to improve the convergence speed of FDO. Then the FFDO is used in the design of the flight control system of ASV re-entry. The simulation results demonstrate the superiority of proposed methods.
In Chapter 5, the reseach emphases are the finite-time convergence of closed-loop system and the efficiencies of the adaptive laws. The approximation errors of fuzzy systems are holded to get the explicit expression of the convergence time. Then the errors of closed-loop converge to certain arbitary small regions, not to zeroes. Another problem studied in this chapter is how to alleviate the computation burden of the computers on-vehicle. A novel adaptive law for the fuzzy systems, only two parameters are adjusted on-line, is proposed to enhance the efficiencies of the computation.
In Chapter 6, after the analysis to the influences of the design parameters to the sliding mode regions and the reaching time, an adaptive time-varying terminal sliding mode control method based on T-S fuzzy model is designed. In this scheme, on one side the T-S fuzzy systems are adopted to approach the totall disturbances on-line, on the other side the time-varying sliding surfaces are benefit to enhance the sliding mode regions, improve the robusticity of closed-loop systems and abbreviate the reaching time.
Finally, the design of observers is studied. After extended the system two times, a novel state-extended observer based on super-twisting is presented. It provides the observer errors can converge to zeroes in finite time under the consideration of the measured noises. Then the unkown states of ASV are estimated using the measured states and the exact differentiation. This method is simple, high-precision, and easy to be applicated.
首先,在实验室已有成果的基础上,建立了ASV的再入模型,其控制系统包含反作用发动机(RCS)和控制舵面,以满足不同飞行阶段的控制要求。然后对其开环性能进行了分析,结果表明该模型具有复杂的非线性、耦合性以及快速时变性等特点,具有一定的代表性,可以为后续的研究工作提供参考。
其次,基于Terminal滑模控制方法研究了ASV再入飞行控制系统的设计问题。在对综合干扰进行一定假设的条件下,得到了具有有限时间收敛特性的Terminal滑模闭环控制系统,可以保证系统跟踪误差在有限时间内收敛到零,从而提高了系统的响应速度。为了消除前述假设,提出另一种基于神经网络的自适应Terminal滑模控制方案,可以在线消除系统的综合干扰,并对闭环稳定性进行了严格的证明。两种方案都在ASV高超声速再入飞行条件下进行了仿真验证。
随后,为了更有效消除综合干扰的影响,且易于工程应用,提出了基于模糊干扰观测器的自适应Terminal滑模控制方法,并基于Lyapunov稳定性理论给出了严格的稳定性证明。进一步,通过改进模糊干扰观测器的自适应律,设计了快速模糊干扰观测器,大大加快了其逼近速度,并将其应用到ASV再入飞控系统的设计中。最后的仿真结果进一步显示了该方法的优越性。
在第五章中,重点研究了闭环系统的有限时间收敛特性和自适应律运算效率的问题。为了明确得到基于快速模糊干扰观测器的闭环系统的收敛时间,保留了模糊系统的逼近误差。此时,闭环系统误差不再收敛到零,而是收敛到可以任意小的某些区域。本章研究的另一个问题是如何减轻机载计算机的运算量。通过提出一种新型参数自适应律,将模糊系统在线调整的参数减少到两个,大大提高了运算效率。
第六章首先分析了Terminal滑模面设计参数对滑模区域及达到时间的影响,然后设计了基于T-S模糊模型的时变自适应Terminal滑模控制系统。该方法采用T-S模糊系统在线逼近综合干扰,且时变滑模面有利于增大滑模区域,提高系统的鲁棒性,缩短到达时间。
最后,研究了ASV状态观测器的设计问题。在考虑系统量测噪声的情况下,对系统进行两次扩张,设计了基于超扭曲算法(Super-twisting)的新型扩张状态观测器,保证观测误差在有限时间内收敛到零。接着利用可以测量的状态,结合精确微分器,对ASV未知状态进行了估计,该方法简单,精度高,易于工程应用。
Aerospace vehicle (ASV) is the reusable flying vehicle of next generation. The vehicle fills the blanks of spaceraft’s activities in near space and has very important martial values. For the varieties of the aerosphere and the large disturbances, the design of attitude control system of ASV is a very difficult problem during the re-entry. In this dissertation, we focus on the high-precision, strong-stabilization, fast adaptive robust control of ASV re-entry.
First of all, a simulation model of an ASV re-entry mode is presented based on the contributions of our lab. This model includes two kinds of actuators, the RCS and the control surface deflections, to satisfy different control tasks in the flight. Open-loop dynamics and stability characteristics demonstrate that this model is complex-nonlinear, coupling, fast time-varying, so it is able to be used in the following research works.
Secondly, a discussion is devoted to the design of the control systems of ASV re-entry based on the terminal sliding mode control. Under the assumption to the totall disturbances, a terminal sliding mode control scheme with finite-time convergence is proposed. This scheme can promise that the tracking errors converge to zeros in finite time, and can improve the response speed of the closed-loop system rapidly. Then, in order to release the assumption above, an adaptive terminal sliding mode control based on neural networks is designed. The controller can compensate the totall disturbances online. After strict analysis to the stabilities, the simulation results are given to demonstrate the good performances of the controllers under the hypersonic conditions.
In the following, an adaptive terminal sliding mode control method that based on the fuzzy disturbance observer (FDO) is designed, in order to eliminate the influences of the totall disturbances and be applicated easily. The stabilities of this method are proved strictly based on Lyapunov’s direct method. Furthmore, by modifying the adaptive law of the FDO, a fast fuzzy disturbance observer (FFDO) is presented to improve the convergence speed of FDO. Then the FFDO is used in the design of the flight control system of ASV re-entry. The simulation results demonstrate the superiority of proposed methods.
In Chapter 5, the reseach emphases are the finite-time convergence of closed-loop system and the efficiencies of the adaptive laws. The approximation errors of fuzzy systems are holded to get the explicit expression of the convergence time. Then the errors of closed-loop converge to certain arbitary small regions, not to zeroes. Another problem studied in this chapter is how to alleviate the computation burden of the computers on-vehicle. A novel adaptive law for the fuzzy systems, only two parameters are adjusted on-line, is proposed to enhance the efficiencies of the computation.
In Chapter 6, after the analysis to the influences of the design parameters to the sliding mode regions and the reaching time, an adaptive time-varying terminal sliding mode control method based on T-S fuzzy model is designed. In this scheme, on one side the T-S fuzzy systems are adopted to approach the totall disturbances on-line, on the other side the time-varying sliding surfaces are benefit to enhance the sliding mode regions, improve the robusticity of closed-loop systems and abbreviate the reaching time.
Finally, the design of observers is studied. After extended the system two times, a novel state-extended observer based on super-twisting is presented. It provides the observer errors can converge to zeroes in finite time under the consideration of the measured noises. Then the unkown states of ASV are estimated using the measured states and the exact differentiation. This method is simple, high-precision, and easy to be applicated.
引文
[1]李大光,太空作战的指导思想[J].国际太空,2004(3): 27-30
[2]《世界航天器运载器大全》编委会著,世界航天器运载器大全[M],第1版,北京:宇航出版社,1996年2月
[3] Bowcutt KG, A perspective on the future of aerospace vehicle design [A], In: 12th AIAA International Space Planes and Hypersonic Systems and Technologies [C], Norfolk: AIAA, 2003-6957
[4]王振国,罗世彬,吴建军编著,可重复使用运载器研究进展[M],第1版,长沙:国防工业大学出版社,2004
[5]朱森元,单级入轨运输器的发射技术研究[J],导弹与航天运载技术, 2001, 5: 1-6
[6] Chudoba B, Huang X, Coleman G, Future space tourism transportation design requirements [A], In: AIAA/CIRA 13th International Space Planes and Hypersonic Systems and Technologies[C], Capua: AIAA, 2005-3445
[7] S.-F. Wu, Rodrigo R. Costa, Nonlinear dynamic modeling and simulation of an atmospheric re-entry spacecraft [J], Aerosp. Sci. Technol., 2001, 5:365-381
[8] S.-F. Wu, Fuzzy logic based attitude control of the spacecraft X-38 along a nominal re-entry trajectory [J], Control Engineering Practice, 2001(9):699-707
[9] S. Juliana, Flight control of atmospheric re-entry vehicle with non-linear dynamic inversion [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit[C], Rhode Island: AIAA, 2004-5330
[10] Marrison C and Stengel RF, Design of robust control systems for a hypersonic aircraft [J], Journal of Guidance, Control and Dynamics, 1998, 21(1):58-63
[11] Wang Q and Stengel RF, Robust nonlinear control of a hypersonic aircraft [J], Journal of Guidance, Control and Dynamics, 2000, 23(4): 577-585
[12] Y Shtessel and C Hall, Sliding mode control of the X-33 with an engine failure [A], In: 36th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit[C], Huntsville: AIAA, 2000-3883
[13] Holger G, Adaptive neural control of the deployment procedure for tether-assisted re-entry [J], Aerospace Science and Technology, 2004(8): 73-81
[14]冯纯伯,费树岷,非线性控制系统分析与设计[M],第二版,北京:电子工业出版社,1997
[15] J S Brinker, K A Wise, Stability and flying qualities robustness of a dynamic inversion aircraft control law [J], AIAA Journal of Guidance, Control and Dynamics, 1996, 19(6): 1270-1277
[16] Anthony J Calise, Naira Hovakimyan, Moshe Idan, Adaptive output feedback control of nonlinear systems using neural networks [J], Automatica, 2001, 37:1201-1211
[17] Meyer G, Su R and Hunt L R, Application of nonlinear transformations to automatic flight control [J], Automatica, 1984, 20(1): 103-107
[18] Snell, Nonlinear Dynamic-inversion flight control of super-maneuverable aircraft [D], University of Minnesota, 1991
[19] McFarland M B, and Calise A J, Neural-adaptive nonlinear autopilot design for an agile anti-air missile [A], In: AIAA Guidance, Navigation and Control Conference [C], San Diego: AIAA, 1996: 1-9
[20] McFarland M B, and Calise A J, Multilayer neural networks and adaptive nonlinear control of agile anti-air missiles [A] In: AIAA Guidance, Navigation and Control Conference [C], Reston: AIAA, 1997: 401-410
[21] Johnson E N, Limited authority adaptive flight control [D], Atlanta, Georgia Institute of Technology, 2000
[22] Johnson E N, Calise A J, El-Shirbiny H A, Feedback linearization with neural network augmentation applied to X-33 attitude control [A] In: AIAA Guidance, Navigation and Control Conference[C], Denver: AIAA, 2000: 1-11
[23] Johnson E N, and Calise A J, Limited authority adaptive flight control for reusable launch vehicles [J], AIAA Journal of Guidance, Control, and Dynamics, 2003, 26(6):906-913.
[24] Johnson M D, Calise A J, and Johnson E N, Further evaluation of an adaptive method for launch vehicle flight control [A], In: AIAA Guidance, Navigation and Control Conference [C], Providence: AIAA, 2004-5016
[25]朱荣刚,姜长生,邹庆元,新一代歼击机超机动飞行的动态逆控制[J],航空学报,2003, 24 (3): 534-539
[26]武国庆,姜长生,张锐,基于径向基神经网络的不确定非线性系统的鲁棒自适应控制[J],航空学报,2002, 23(6): 530-533
[27] Shamma J S, Athans M, Analysis of Gain scheduled control for Nonlinear Plants [J]. IEEE Transactions on Automatic Control, 1990, 35(8): 898-907
[28] Rugh W J, Analytical framework for gain scheduling [J]. IEEE control systemmagazine, 1991, 11(1):79-84.
[29] Rugh W J,and Shamma J S, Research on gain scheduling[J], Automatica, 2000, 36(10): 1401-1425
[30] Tan S H, Hang C C, Chai J S, Gain scheduling: from conventional to neural-fuzzy [J], Automatica, 1997, 33(3):411-419.
[31] Palm R, Rehfuess U, Fuzzy controllers as gain scheduling approximators [J], Fuzzy sets and systems, 1997, 85(2): 233-246
[32] Apkarian P and Gahinet P, A convex characterization of gain-scheduled H∞controllers, IEEE Transaction on Automatic Control, 1995, 40(5): 853-864
[33] Clement B, Duc G.and Mauffrey S, Aerospace launch vehicle control: a gain scheduling approach, Control Engineering Practice, 2005, 13(3): 333-347
[34] Palm R. and Stutz C., Open loop dynamic trajectory generator for a fuzzy gain scheduler, Engineering Appl. Of Artificial Intelligence, 2003, 16(3): 213-225
[35] Wang J L and Sundararajan N, A nonlinear flight controller design for aircraft, Control Eng;Practice, 1995, 3(6): 813-825
[36] Snell, Nonlinear Dynamic-Inversion Flight Control of Supermaneuverable Aircraft [D], University of Minnesota, 1991
[37] Agustin R. M, Mangoubi R S, Hain R.M, Robust failure detection for reentry vehicle attitude control systems [A], In: AIAA Guidance, Navigation and Control Conference and Exhibit[C], Reston: AIAA, 1998: 1-14
[38] Lu P. Regulation about time-varying trajectories: precision entry guidance illustrated [A], In: AIAA Guidance, Navigation and Control Conference and Exhibit[C], Reston: AIAA, 1999:704-713
[39] Shtesel Y B, Zhu J J, Daniels D, Reusable launch vehicle attitude control using time-varying sliding modes [A], In: AIAA Guidance, Navigation and Control Conference and Exhibit[C], Reston: AIAA, 2002: 1409-1418
[40] Mickle M C and Zhu J J, Bank-to-turn roll-yaw-pitch autopilot design using dynamic nonlinear inversion and PD-eigenvalue assignment [A], In: Proceedings of the American Control Conference[C], Chicago: IEEE, 2000:1359-1364
[41] Mickle M C and Zhu J J, Skid-To-Turn Control of the APKWS Missile Using Trajectory Linearization Technique [A], In: Proceedings of the American Control Conference[C], Arlington: IEEE, 2001: 3346-3351
[42]张春雨,空天飞行器建模及其自适应轨迹线性化控制研究[D],南京:南京航空航天大学,2003
[43] Liu Y, Huang R, and Zhu JJ, Nonlinear system control using neural networks based on trajectory linearization[A], In: Proceedings of the 2004 IEEE International Conference on Control Applications[C], Taipei: IEEE, 2004: 806-811
[44] Mickle MC and Huang R and Zhu JJ, Unstable, Non-minimum Phase, Nonlinear Tracking by Trajectory Linearization Control [A], In: Proceedings of the 2004 IEEE International Conference on Control Applications[C], Taipei: IEEE, 2004: 812-818
[45] Utkin V I, Variable structure systems with sliding modes [J], IEEE Transactions Automatic Control, 1977, 22(2):212-222
[46] Yu X H, Xu J X, Advances in variable structure system[M], World Scientific Publishing, Singapore, 2000
[47] Yeh F K,Chien H H, Fu L C, Design of optimal midcourse guidance sliding mode control for missiles with TVC[J], Aerospace and Electronic Systems, IEEE Transaction on, 2003,39(3):824-837
[48] Chen Y P, Chang J L, Chu S R, PC-based sliding mode control applied to parallel-type double inverted pendulum system [J], Mechatronics, 1999, 9:553-564
[49] Perruquetti W, Barbot J P, Sliding mode control in engineering, Marcel Dekker Inc., New York, 2002
[50] Utkin A, Guldner J, Shi J X, Sliding mode control in electromechanical system[M], Taylor & Francis, 1999
[51] Chen M S, Hwang Y R, Tomizuka M, A state-dependent boundary layer design for sliding mode control [J], IEEE Transactions on Automatic Control, 20002, 47(10): 1677-1681
[52] Kang B P, Ju J L, Sliding mode controller with filtered signal for robot manipulators using virtual plant controller [J], Mechatronics, 1997, 7(3): 277-286
[53] Ha Q P, Nguyen Q H, Rye D C, Durrant-Whyte, Fuzzy sliding-mode controllers with applications [J], IEEE Transactions on Industrial Electronics, 2001, 48(1):38-41
[54]高为炳,变结构控制的理论及设计方法[M],北京:科学出版社,1996
[55] K B Park, Terminal sliding mode control of second-order nonlinear uncertain systems [J], Int. J. Robust Nonlinear Control, 1999, 9:769-780
[56]庄开宇,变结构控制理论若干问题研究及其应用[D],杭州:浙江大学, 2002
[57]张科,周凤歧,不确定性多变量系统的全程滑态变结构控制方案设计[J ],控制理论与应用, 1999, 16(2): 221-224
[58] Choi S B, Moving switching surface for robust control of second order variable structure system[J], Int. J. Contr., 1993, 38 (1): 229-245
[59] Slotine J J, Tracking control of nonlinear system using sliding surface with application to robust manipulators[J], Int. J. Contr., 1983, 38(3):465-492
[60] Man Z H, Paplinski A P, Wu H R, A robust MIMO terminal sliding mode control scheme for rigid robot manipulators [J], IEEE Transaction on Automatic Control, 1994, 39:2464-2469
[61] Shuanghe Yu, Xinghuo Yu, Robust global Terminal sliding mode control of SISO linear uncertain systems [A], Proceeding of the 35th Conference on Decision and Control[C], Sydney, Australia, Dec, 2000
[62] Xinghuo Yu, Zhihong Man, Fast Terminal sliding mode control design for nonlinear dynamical systems [J], IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, 2004, 49(2): 261-265
[63] C.W. Tao, J.S. Taur, M. L. Chan, Adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties [J], IEEE Trans. Systems, Man and Cybernetics Part B, 2004, 34 (1):255-262
[64] ShuangheYu, Xinghuo Yu, Non-singular terminal sliding mode of rigid robotic manipulators[J], Automatica, 2002, 38:2159-2167
[65] Y Feng, X H Yu, Z H Man, Continuous finite-time control for robotic manipulators with terminal sliding mode [J], Automatica, 2005, 41:1957-1964
[66] S C Lin, Y Y Chen, RBF network based sliding mode control[J], IEEE International Conference on Systems, Man and Cybernetics, 1994, 1957-1961
[67] D Munoz, D Sbarbaro, An adaptive sliding mode controller for discrete nonlinear systems[J], IEEE Trans. on Industrial Electronics, 2000, 47(3):574-581
[68] J M Carrasco, Sliding mode control of a DC/DC PWM converter with PFC implemented by neural networks[J], IEEE Trans. on Circuits and Systems A: Fundamental Theory and Applications, 1997, 44(8):743-749
[69] S W Kim, J J Lee, Design of a fuzzy controller with fuzzy sliding surface[J], Fuzzy Sets and Systems, 1995, 71(3):359-367
[70] B Yoo, W Ham, Adaptive fuzzy sliding mode control of nonlinear system[J], IEEE Trans. on Fuzzy Systems, 1998, 6(2):315-321
[71] Y S Lu, J S Chen, A self- organizing fuzzy sliding mode controller design for a class of nonlinear servo systems[J], IEEE Trans. on Industrial Electronics, 1994,41(5):492-502
[72] J Y Chen, Expert SMC-based fuzzy control with genetic algorithms[J], Journal of Franklin institute, 1996, 336:589-610
[73] F J Lin, S L Chiu, K K Shyu, Novel sliding mode controller for synchronous motor drive[J], IEEE Trans. on Aerospace and Electronics Systems, 1998, 34(2):532-542
[74] G Wheeler, C H Su, Y Stepanenko, A sliding mode controller with improved adaptation laws for the upper bounds on the norm of uncertainties synchronous motor drive[J], Automatica, 1998, 34(12):1657-1661
[75] Zhou D, Mu CD, Xu WL, Adaptive silding mode guidance of a homing missile[J], Journal of Guidance, Control and Dynamics, 1999, 22(4): 589-592
[76] McGeoch DJ, McGookin EW, Houston SS, MIMO Sliding Mode Attitude Command Flight Control System For A Helicopter [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], San Francisco:AIAA, 2005: 1-15
[77] Shtessel Y, Tournes C, and Krupp D, Reusable launch vehicle control in sliding modes [A], In: AIAA Guidance, Navigation, and Control Conference[C], New Orleans:AIAA, 1997:335-345
[78] Shtessel Y, McDuffie J, Jackson, M, Sliding mode control of the X-33 vehicle in launch and re-entry modes [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit[C], Reston: AIAA, 1998:1352-1362
[79] Shtessel Y, Hall C, and Jackson M, Reusable launch vehicle control in multiple time sale sliding modes[A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit[C], Reston: AIAA, 2000:1-11
[80] Hall C E, Shtessel Y B, Sliding mode disturbance observer-based control for a reusable launch vehicle [A], In: AIAA Guidance Navigation and Control Conference and Exhibit[C], Reston: AIAA, 2005:1-26
[81] Bode, H W, Network analysis and feedback amplifier design, New York, Van Nostrand, 1945
[82] Fan MKH, Tits AL, and Doyle JC, Robustness in the presence of mixed parametric uncertainty and unmodeled dynamics[J], IEEE Transactions on Automatic Control, 1991, 36(1): 25 - 38
[83] Tits AL, The smallμtheorem without real-rational assumption [A], In: Proceedings of the American Control Conference [C], Seattle: IEEE, 1995, 4: 2870 - 2872
[84] Ahmad SS, Lew JS, and Keel LH, Robust control of flexible structures againststructural damage [J], IEEE Transactions on Control Systems Technology, 2000, 8(1): 170– 182
[85] Youla D, Jabr H, and Bongiorno J, Modern Wiener-Hopf design of optimal controllers Part II: The multivariable case[J], IEEE Transactions on Automatic Control, 1976, 21(3): 319– 338
[86] Zhou K, Comparison between H 2and H∞controllers [J], IEEE Transactions on Automatic Control, 1992, 37(8): 1261 - 1265
[87] Lin J L, Postlethweite I, Gu D W,μ? K iteration: A new algorithm forμsynthesis[J], Automatica, 1993, 29:219-224
[88] Tamaki K, Ohishi K, Ohnishi K, Microprocessor-based robust control of a DC servo motor [J], IEEE Control Systems Magazine, 1986, 6(5): 30– 36
[89] Shahruz S M, Performance enhancement of a class of nonlinear systems by disturbance observer [J], IEEE/ASME Transactions on Mechatronics, 2000, 5(3): 319-323
[90] Chen X K, Fukuda T, and Young K D, A New Nonlinear Robust Disturbance Observer [J], Systems & Control Letters, 2000, 41(3):189-199
[91] Chen X K, Komada S, and Fukuda T, Design of a Nonlinear Disturbance Observer[J], IEEE Transactions of Industrial Electronics, 2000, 47(2): 429-437
[92] Chen X K, Su C Y, and Fukuda T, A nonlinear disturbance observer for multivariable systems and its application to magnetic bearing systems, IEEE Transactions on Control Systems Technology, 2004, 12(4): 569-577
[93] Chen W H, Harmonic Disturbance Observer for Nonlinear systems[J], Journal of Dynamic Systems, Measurement and Control, 2003, 125: 114-117
[94] Chen W H, Disturbance Observer Based Control for Nonlinear Systems[J], IEEE/ASME Transactions on Mechatronics, 2004, 9(4):706-710
[95] Kim E, A fuzzy disturbance observer and its application to control, IEEE Transactions on Fuzzy Systems [J], 2002, 10(1): 77-84
[96] Kim E, A discrete-time fuzzy disturbance observer and its application to control, IEEE Transactions on Fuzzy Systems [J], 2003, 11(3): 399-410
[97] Kim E and Park C W, Fuzzy disturbance observer approach to robust tracking control of nonlinear sampled systems with the guaranteed suboptimal H∞performances [J], IEEE Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics, 2004, 34(3): 1574-1581
[98] Kim E and Lee S Y, Output feedback tracking control of MIMO systems using a fuzzy disturbance observer and its application to the speed control of a PM synchronous motor [J], IEEE Transactions on fuzzy systems, 2005, 13(6): 725-741
[99] Narendra K S, and Valavani L, Stable adaptive controller design--Direct control [J], IEEE Transactions on Automatic Control, 1978, 23(4): 570 - 583
[100] Narendra K S, Lin Y H, and Valavani LS, Stable adaptive controller design- Part II: proof of stability [J], IEEE Transactions on Automatic Control, 1980, 25(3):440-448
[101] Morse A S, Global stability of parameter adaptive systems, IEEE Transactions on Automatic Control [J], 1980, 25(3): 433-439
[102] Narendra K S, and Lin YH, Stable discrete adaptive control, IEEE Transactions on Automatic Control [J], 1980, 25(3): 456-461
[103] Narendra K S, and Annaswamy A M, Stable adaptive systems [M], 1st Ed, New Jersey: Prentice-Hall, 1989
[104] Narendra K S, and Parthasarathy K, Identification and control of dynamical systems using neural networks [J], IEEE Transactions on Neural Networks, 1990, 1(1): 4-27
[105] Polycarpou M M, and Ioannou P A, Neural networks as on-line approximators of nonlinear systems [A], In: Proceedings of the 31st IEEE Conference on Decision and Control [C], Tucson: IEEE, 1992: 7 - 12
[106] Ahmed-Zaid F, Ioannou P A, Polycarpou M M, Identification and control of aircraft dynamics using radial basis function networks [A], In: Second IEEE Conference on Control Applications [C], Vancouver: IEEE, 1993: 567-572
[107] Sanner R M, and Slotine J J E, Gaussian networks for direct adaptive control [J], IEEE Transactions on Neural Networks, 1992, 3(6): 837-863
[108] Sadegh N, A perceptron network for functional identification and control of nonlinear systems [J], IEEE Transactions on Neural networks, 1993, 4(6): 982-988
[109] Chen F C, Khalil H K, Adaptive control of nonlinear systems using neural networks, International Journal of Control, 1992, 55(6): 1299-1317
[110] Chen F C, Liu C C, Adaptively controlling nonlinear continuous-time systems using multilayer neural networks [J], IEEE Transactions on Automatic Control, 1994, 39(6): 1306-1310
[111] Niu Y, Wang X, Lam J, Sliding-mode control for nonlinear state-delayed systems using neural-network approximation [J], IEE Proceedings on Control Theory andApplications, 2003, 150(3): 233– 239
[112] Wai R J, Duan R Y, Lee J D, Wavelet neural network control for induction motor drive using sliding-mode design technique [J], IEEE Transactions on Industrial Electronics, 2003, 50(4):733 - 748
[113] Wai R J, Chang J M, Implementation of robust wavelet-neural-network sliding-mode control for induction servo motor drive, IEEE Transactions on Industrial Electronics, 2003, 50(6):1317 - 1334
[114] Lewis F L, Liu K, and Yesildirek A, Neural net robot controller with guaranteed tracking performance [J], IEEE Trans. Neural networks, 1995, 6(3):703-715
[115] Kwan CM, and Lewis FL, Robust backstepping control of induction motors using neural networks[J], IEEE Transactions on Neural Networks, 2000, 11(5): 1178–1187
[116] Choi JY, and Farrell JA, Adaptive observer backstepping control using neural networks [J], IEEE Transactions on Neural Networks, 2001, 12(5): 1103 - 1112
[117] Li YH, Sheng Q, Zhuang XY, Robust and adaptive backstepping control for nonlinear systems using RBF neural networks [J], IEEE Transactions on Neural Networks, 2004, 15(3):693– 701
[118] Y Shtessel, J Jim Zhu and Dan Daniels, Reusable launch vehicle attitude control using time-varying sliding modes[A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Monterey: AIAA, 2002-4779
[119] Y Shtessel, James Strott, J Jim Zhu, Time-varying sliding modes control with sliding mode observer for reusable launch vehicle [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Austin: AIAA, 2003-5362
[120] Eric N. Johnson, A. J. Calise, H. A. El-Shirbiny, Feedback linearization with neural network augmentation applied to X-33 attitude control [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Denver: AIAA, 2000-4157
[121] Roy Smith, Asif Ahmed, Robust parametrically varying attitude controller designs for the X-33 vehicle [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Denver: AIAA, 2000-4158
[122] Marwan Bikdash, Ken Sartor, Abdollah Homaifar, Fuzzy guidance of the shuttle orbiter during atmospheric reentry [J], Control Engineering Practice, 1999 (7): 295-303
[123] Baohua Lian, Hyochoong Bang, J. E. Hurtado, Adaptive Backstepping control based autopilot design for reentry vehicle [A], In: AIAA Guidance, Navigation, and ControlConference and Exhibit [C], Providence: AIAA, 2004-5328
[124] Elmar M. Wallner, Klaus H. Well, Attitude control of a reentry vehicle with internal dynamics [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Monterey: AIAA, 2002-4647
[125] S. -F. Wu, C. J. H. Engelen, R. Babuska, Fuzzy logic full-envelope autonomous flight control for an atmospheric re-entry spacecraft [J], Control Engineering Practice, 2003 (11): 11-25
[126] Marco Caporicci, Luciano Basile, ESA activities on atmospheric re-entry systems [A], In: 12th AIAA International Space Planes and Hypersonic Systems and Technologies [C], Norfolk, AIAA: 2003-7017
[127] S. Juliana, Q. P. Chu, J. A. Mulder, T. J. van Baten, Flight control of atmospheric re-entry vehicle with non-linear dynamic inversion [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Providence: AIAA, 2004-5330
[128] E. Mooij, Direct model reference adaptive control of a winged reentry vehicle [A], In: AIAA 9th International Space Planes and Hypersonic Systems and Technologies Conference [C], Norfolk: AIAA, 99-45484
[129] E. Mooij, Model reference adaptive guidance for reentry trajectory tracking[A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Providence: AIAA, 2004-4775
[130] J. Fatemi, E. Mooij, S. P. Gurav, Reentry vehicle design optimization with intergrated trajectory uncertainties [A], In: AIAA 13th International Space Planes and Hypersonic Systems and Technologies Conference [C], AIAA, 2005-3386
[131] E. Mooij, I. Barkana, Stability analysis of an adaptive guidance and control system applied to winged reentry vehicle [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], San Francisco: AIAA, 2005-6290
[132] M. Ohno, Y. Yamaguchi, T. Hata, M. Takahama, Robust flight control law design for an automatic landing flight experiment [J], Control Engineering Practice, 1999, 7(9):1143-1152
[133] Atsumi Ohara, Yasuhiro Yamaguchi, Toshiki Morito, LPV modeling and gain scheduled control of re-entry vehicle in approach and landing phase [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Montreal: AIAA, 2001-4038
[134] R. R. da Costa, Q. P. Chu, J. A. Mulder, Re-entry flight controller design usingnonlinear dynamic inversion [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Montreal: AIAA, 2001-4219
[135] A. E. Finzi, M. Lavagna, A. Di Gregorio, Atmospheric re-entry trajectory tracking and control for an unmanned space vehicle with a Lyapunov approach [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Austin: AIAA, 2003-5441
[136]郭锁风,申功璋,吴成富等编著,先进飞行控制系统[M],第1版,北京:国防工业出版社,2003
[137] Shaohua Tan, Chang-chien hang, Joo-siong chai, Gain scheduling: from conventional to neural-fuzzy[J], Automatica, 1997, 33(3):411-419
[138] Rugh W J, Shamma J S, Research on gain scheduling[J], Automatica, 2000, 36(10):1401-1425.
[139] Rugh W J, Analytical framework for gain scheduling[J], IEEE control system magazine, 1991,11(1):79-84.
[140]Thomas Burk, Attitude Control Performance During Cassini Trajectory Correction Maneuvers [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], San Francisco: AIAA, 2005-6270
[141]朱亮,空天飞行器不确定非线性鲁棒自适应控制[D],南京:南京航空航天大学,2006
[142] Shaughnessy J D, Pinckney S Z, McMinn J D, Hypersonic vehicle simulation model: winged-cone configuration[R], NASA TM-102610,1990
[143]张金槐编著,远程火箭精度分析与评估[M],第1版,长沙:国防科技大学出版社,1995年5月
[144]《飞机设计手册》编辑委员会编,飞机设计手册第一卷[M],第1版,北京:航空工业出版社, 1996:13-278
[145]鲁道夫.布罗克豪斯著,飞行控制[M].第1版,北京:国防工业出版社, 1999年9月
[146]Ramon L Chase, Leon McKinney, A near-term alternative for space access and global range [A], In: 41st AIAA/ASME/SAE/ASEE Joint Propulsion Control Conference and Exhibit [C], Tucson: AIAA, 2005-4191
[147] Steven G L, Leopoldo F. Perez, Steve Fitzgerald, X-38 integrated aero- and aerothermodynamic activities [J], Aerosp. Sci. Technol., 1999 (3): 485-493
[148] Andrew Clark, Chivey Wu, Maj Mirmirani, Sangbum Choi, Development of anairframe–propulsion integrated generic hypersonic vehicle mode[A], In: 44th AIAA Aerospace Sciences Meeting and Exhibit[C], Reno: AIAA, 2006-218
[149]陈谋,不确定非线性综合火力/飞行/推进系统鲁棒控制方法研究[D],南京:南京航空航天大学,2003
[150] Dennis J McNabb, Investigation of atmospheric reentry for the space maneuver vehicle[D], Ohio: Air Force Institute of Technology, 2004
[151] J. Free, S. Glubke, Overview of the TRMM RCS design integration and in–orbit operations[A], In: 35th AIAA/ASME/SAE/ASEE Joint Propulsion Control Conference and Exhibit [C], Los Angeles: AIAA, 99-31252
[152] David A. Throckmorton, Shuttle entry aerothermodynamic flight research: the orbiter experiments programComponent development for micropropulsion systems [A], In: 17th Aerospace Ground Testing Conference [C], Nashville: AIAA, 92-3987
[153] P. M. Danehy, J. A. Wilkes, G. J. Brauckman, Visualization of a capsule entry vehicle reaction control system thruster [A], In: 44th AIAA Aerospace Science Meeting and Exhibit[C], Reno: AIAA, 2006-1532
[154] Bruce G. Powers, Active control technology experience with the space shuttle in the landing regime, NASA Technical Memorandum, NASA-TM-85910
[155] J. Muss, Development of the x-33 gCH4/gO2 RCS thruster[A], In: 35thAIAA/ASME/SAE/ASEE Joint Propulsion Control Conference and Exhibit [C], Los Angeles: AIAA, 99-31055
[156] Nakano M M, Space shuttle on-orbit flight control system[R], AIAA Paper, 82-1576
[156]Louis A, Cassel,Applying jet interaction technology [J]. Journal of Spacecraft and Rockets, 2003, 40(4):523~527
[157]李前国,基于直接力的拦截导弹智能化控制、制导及其视景仿真[D],南京:南京航空航天大学,2003
[158] Richard H S, Control allocation for the next generation of entry vehicle [R], ADA-398505, 2001
[159] N. C. Lowry and R. A. Hall, An improved three-pulse algorithm for axisymmetric RCS control[A], In: AIAA Guidance Navigation and Control Conference and Exhibit [C], Austin: AIAA, 2003-5329
[160] Rui Hirokawa, Koichi Sato, Autopilot design for a missile with reaction-jet using coefficient diagram method [A], In: AIAA Guidance Navigation and Control Conference and Exhibit [C], Montreal: AIAA, 2001-4162
[161] Roark D. Weil, Kevin A. Wise, Blended aero & reaction jet missile autopilot design using VSS techniques [A], In: Proceedings of the 30th Conference on Decision and control[C], 2828-2829
[162] Ajay Thukral. Mario Innocenti, A sliding mode pitch autopilot synethesis for high angle of attack maneuvering [J], IEEE Transactions on Control Systems Technology, 1998, 6(3):359-371
[163] Han Ho Choi, An analysis and design method for uncertain variable structure systems with bounded controllers [J], IEEE Transactions on automatic control, 2004, 49(4):602-608
[164] C. Edwards, S. K. Spurgeon and R. G. Hebden, On the design of sliding mode output feedback controllers [J], INT. J, CONTROL, 2003, 76(9/10):893-905
[165]吴立刚,王常虹,曾庆双,Terminal滑模自适应控制实现一类不确定混沌系统的同步[J ],控制与决策, 2006, 21(2): 229-232
[166]胡剑波,时满宏,庄开宇等,一类非线性系统的Terminal滑模控制[J ],控制理论与应用, 2005, 22(3): 495-502
[167] Park J, Sanberg I W, Universal approximation using radial basis function networks [J],Neural Computation, 1991, 3(2):246-257
[168] Khalil H K, Nonlinear Systems [M], 3rd Ed, Upper Saddle River, New Jersey:Prentice-Hall, 2002
[169] Lewis FL, Liu K, and Yesildirek A, Neural net robot controller with guaranteedtracking performance [J], IEEE Trans. Neural networks, 1995, 6(3):703-715
[170] Lewis FL, Yesildirek A and Liu K, Multilayer neural-net robot controller with guaranteed tracking performance [J], IEEE Trans. Neural networks, 1996, 7(2):388-399
[171] Chin-Pao Hung, Integral variable structure control of nonlinear system using a CMAC neural network learning approach [J], IEEE Trans. On Systems, Man and Cybernetics, Part B, 2004, 34(1):702-709
[172] Fu-Chuang Chen, Chih-Horng Chang, Practical stability issues in CMAC neural network control systems [J], IEEE Trans. Control Systems Technology, 1996, 4(1):86-91
[173] L. X. Wang, Fuzzy systems are universal approximators[A], Proceedings of IEEE Conference on Fuzzy Systems [C], San Diego, 1982: 1163-1170
[174] X. Zeng and M. G. Singh, Approximation theory of fuzzy systems MIMIO case [J], IEEE Transaction on Fuzzy Systems, 1995, 5(3): 219-235
[175] J. E. Slotine and W. Li, Applied nonlinear control [M], Upper Saddle River, NJ: Prentice-Hall, 1991
[176] Antonios Tsourdos, Brian A. White, Adaptive flight control design for nonlinear missile [J], Control Engineering Practice, 2005,13:373-382
[177] Matthew D. Johnson, Anthony J Calise, Eric N. Johnson, Evaluation of an adaptive method for launch vehicle flight control [A], In: 2003 AIAA Guidance Navigation and Control Conference [C],1-19
[178] Shuanghe Yu, Xinghuo Yu, Zhihong Man, A fuzzy neural network approximator with fast terminal sliding mode and its applications [J], Fuzzy Sets and Systems, 2004,148 :469-486
[179] Shuanghe Yu, Xinghuo Yu, Continuous finite-time control for robotic manipulators with terminal sliding mode[J], Automatica, 2005 (41):1957-1964
[180] Shuanghe Yu, Adaptive fuzzy control of nonlinear systems based on terminal sliding mode[J], Lecture Notes on Control and Information Science, 2006 (344):474-479
[181] Vélez-Díaz D, Tang Y, Adaptive Robust Fuzzy Control of Nonlinear Systems [J], IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2004, 34(3): 1596-1601
[182] Lu Y S, Chen J S, Design of a global sliding mode controller for a motor drive with bounded control,[J], Int. J. Contr., 1993, 62(5):1001-1019
[183]池毓东,王岩,邵惠鹤,一种时变滑模平面的设计方法[J],上海交通大学学报,1998, 32(6):70-73
[184]冯勇,安澄全,李涛,采用双滑模平面减小一类非线性系统稳态误差[J],控制与决策,2000, 15(3):361-365
[185] Gu Wen-jin, Zhang Yi-fei, Li Chang-ping, Composite control of linear/adaptive variable structure control [J], Chinese Journal of Aeronautics, 2001, 14(1):49-57
[186] Xing Wang, Yigao Deng, Yafeng Wang, Composite control of linear/adaptive TSM control for mass-controlled saucer-like air vehicle[A], Proceedings of the 6th World Congress on Intelligent Control and Automation , Dalian, China, June 21-23, 2006
[187]黄辉先,史忠科,吴方向,一类非线性系统的时变滑模控制[J ],控制理论与应用, 2000, 17(5): 774-776
[188]冯勇,池毓东,任倩,具有全局鲁棒性的时变滑模平面的设计方法[J ],宇航学报, 1997, 18(3): 59-64
[189] Takagi T and Sugeno M, Fuzzy Identification of Systems and its Applications to Modeling and Control [J], IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 1985, 15:116-163
[190] Joongseon J, Chen YH and Reza L, On the Stability Issues of Linear Takagi-Sugeno Fuzzy Models[J], IEEE Transation on Fuzzy Systems, 1998, 6(3): 402-410
[191] Yang Y S and Zhou C J, Robust Adaptive Control of Uncertain Nonlinear Systems Using Fuzzy Logic Systems [C], Proceedings of the 2005 IEEE International Symposium on Intelligent Control, Limassol, Cyprus, June 27-29, 2005
[192] Wang Y, Guan Z H, Wen X, Adaptive Synchronization for Chen Chaotic System with Fully Unknown Parameters[J] , Chaos, Solitons and Fractals, 2004,19(4):899-903
[193] Chih-Chiang Cheng, Adaptive sliding mode controller design based on T-S fuzzy system models[J],Automatica, 2006(42):1005-1010
[194] Stephen A W, Timothy R M., Measurement uncertainty and feasibility study of a flush airdata system for a hypersonic Flight experiment [R], NASATM-4627, 1994
[195]韩京清,一类不确定对象的扩张状态观测器[J],控制与决策,1995,10(1):85-88
[196]韩京清,自抗扰控制器及其应用[J],控制与决策,1998,13(1):19-23
[197]林飞,用于带有量测噪声系统的新型扩张状态观测器[J],理论与应用,2005(22):995-998
[198]王新华,基于扩张观测器的非线性不确定系统输出跟踪[J],控制与决策,2004,19(10):1113-1116
[199] A Pennallegue, A Mokhtari, L Fridman, Feedback linearization and high order sliding mode observer for a quadrotor UAV [C], Proceedings of the 2006 International Workshop on Variable Structure Systems, Alghero, Italy, June 5-7, 2006
[200]武利强,韩京清,直线型倒立摆的自抗扰控制设计方案[J],制理论与应用, 2004,21(5):665-669
[201] Levant A., Universal Single-Input-Single-Output (SISO) sliding-mode controllers with finite-time convergence [J], IEEE Transactions on Automatic Control, 2001(9):1447-1451
[202] Yi Xiong, Sliding mode observer for nonlinear uncertain systems [J],IEEE Trans. Auto. Control, 2001, 46(12):2012-2017
[203]刘强,于达仁,王仲奇,高超声速飞行器的滑模观测器设计[J],航空学报,2004,25(6):589-594
[204] Levant A., Robust exact differentiation via sliding mode technique [J], Automatica, 1998,34(3): 379-384
[2]《世界航天器运载器大全》编委会著,世界航天器运载器大全[M],第1版,北京:宇航出版社,1996年2月
[3] Bowcutt KG, A perspective on the future of aerospace vehicle design [A], In: 12th AIAA International Space Planes and Hypersonic Systems and Technologies [C], Norfolk: AIAA, 2003-6957
[4]王振国,罗世彬,吴建军编著,可重复使用运载器研究进展[M],第1版,长沙:国防工业大学出版社,2004
[5]朱森元,单级入轨运输器的发射技术研究[J],导弹与航天运载技术, 2001, 5: 1-6
[6] Chudoba B, Huang X, Coleman G, Future space tourism transportation design requirements [A], In: AIAA/CIRA 13th International Space Planes and Hypersonic Systems and Technologies[C], Capua: AIAA, 2005-3445
[7] S.-F. Wu, Rodrigo R. Costa, Nonlinear dynamic modeling and simulation of an atmospheric re-entry spacecraft [J], Aerosp. Sci. Technol., 2001, 5:365-381
[8] S.-F. Wu, Fuzzy logic based attitude control of the spacecraft X-38 along a nominal re-entry trajectory [J], Control Engineering Practice, 2001(9):699-707
[9] S. Juliana, Flight control of atmospheric re-entry vehicle with non-linear dynamic inversion [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit[C], Rhode Island: AIAA, 2004-5330
[10] Marrison C and Stengel RF, Design of robust control systems for a hypersonic aircraft [J], Journal of Guidance, Control and Dynamics, 1998, 21(1):58-63
[11] Wang Q and Stengel RF, Robust nonlinear control of a hypersonic aircraft [J], Journal of Guidance, Control and Dynamics, 2000, 23(4): 577-585
[12] Y Shtessel and C Hall, Sliding mode control of the X-33 with an engine failure [A], In: 36th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit[C], Huntsville: AIAA, 2000-3883
[13] Holger G, Adaptive neural control of the deployment procedure for tether-assisted re-entry [J], Aerospace Science and Technology, 2004(8): 73-81
[14]冯纯伯,费树岷,非线性控制系统分析与设计[M],第二版,北京:电子工业出版社,1997
[15] J S Brinker, K A Wise, Stability and flying qualities robustness of a dynamic inversion aircraft control law [J], AIAA Journal of Guidance, Control and Dynamics, 1996, 19(6): 1270-1277
[16] Anthony J Calise, Naira Hovakimyan, Moshe Idan, Adaptive output feedback control of nonlinear systems using neural networks [J], Automatica, 2001, 37:1201-1211
[17] Meyer G, Su R and Hunt L R, Application of nonlinear transformations to automatic flight control [J], Automatica, 1984, 20(1): 103-107
[18] Snell, Nonlinear Dynamic-inversion flight control of super-maneuverable aircraft [D], University of Minnesota, 1991
[19] McFarland M B, and Calise A J, Neural-adaptive nonlinear autopilot design for an agile anti-air missile [A], In: AIAA Guidance, Navigation and Control Conference [C], San Diego: AIAA, 1996: 1-9
[20] McFarland M B, and Calise A J, Multilayer neural networks and adaptive nonlinear control of agile anti-air missiles [A] In: AIAA Guidance, Navigation and Control Conference [C], Reston: AIAA, 1997: 401-410
[21] Johnson E N, Limited authority adaptive flight control [D], Atlanta, Georgia Institute of Technology, 2000
[22] Johnson E N, Calise A J, El-Shirbiny H A, Feedback linearization with neural network augmentation applied to X-33 attitude control [A] In: AIAA Guidance, Navigation and Control Conference[C], Denver: AIAA, 2000: 1-11
[23] Johnson E N, and Calise A J, Limited authority adaptive flight control for reusable launch vehicles [J], AIAA Journal of Guidance, Control, and Dynamics, 2003, 26(6):906-913.
[24] Johnson M D, Calise A J, and Johnson E N, Further evaluation of an adaptive method for launch vehicle flight control [A], In: AIAA Guidance, Navigation and Control Conference [C], Providence: AIAA, 2004-5016
[25]朱荣刚,姜长生,邹庆元,新一代歼击机超机动飞行的动态逆控制[J],航空学报,2003, 24 (3): 534-539
[26]武国庆,姜长生,张锐,基于径向基神经网络的不确定非线性系统的鲁棒自适应控制[J],航空学报,2002, 23(6): 530-533
[27] Shamma J S, Athans M, Analysis of Gain scheduled control for Nonlinear Plants [J]. IEEE Transactions on Automatic Control, 1990, 35(8): 898-907
[28] Rugh W J, Analytical framework for gain scheduling [J]. IEEE control systemmagazine, 1991, 11(1):79-84.
[29] Rugh W J,and Shamma J S, Research on gain scheduling[J], Automatica, 2000, 36(10): 1401-1425
[30] Tan S H, Hang C C, Chai J S, Gain scheduling: from conventional to neural-fuzzy [J], Automatica, 1997, 33(3):411-419.
[31] Palm R, Rehfuess U, Fuzzy controllers as gain scheduling approximators [J], Fuzzy sets and systems, 1997, 85(2): 233-246
[32] Apkarian P and Gahinet P, A convex characterization of gain-scheduled H∞controllers, IEEE Transaction on Automatic Control, 1995, 40(5): 853-864
[33] Clement B, Duc G.and Mauffrey S, Aerospace launch vehicle control: a gain scheduling approach, Control Engineering Practice, 2005, 13(3): 333-347
[34] Palm R. and Stutz C., Open loop dynamic trajectory generator for a fuzzy gain scheduler, Engineering Appl. Of Artificial Intelligence, 2003, 16(3): 213-225
[35] Wang J L and Sundararajan N, A nonlinear flight controller design for aircraft, Control Eng;Practice, 1995, 3(6): 813-825
[36] Snell, Nonlinear Dynamic-Inversion Flight Control of Supermaneuverable Aircraft [D], University of Minnesota, 1991
[37] Agustin R. M, Mangoubi R S, Hain R.M, Robust failure detection for reentry vehicle attitude control systems [A], In: AIAA Guidance, Navigation and Control Conference and Exhibit[C], Reston: AIAA, 1998: 1-14
[38] Lu P. Regulation about time-varying trajectories: precision entry guidance illustrated [A], In: AIAA Guidance, Navigation and Control Conference and Exhibit[C], Reston: AIAA, 1999:704-713
[39] Shtesel Y B, Zhu J J, Daniels D, Reusable launch vehicle attitude control using time-varying sliding modes [A], In: AIAA Guidance, Navigation and Control Conference and Exhibit[C], Reston: AIAA, 2002: 1409-1418
[40] Mickle M C and Zhu J J, Bank-to-turn roll-yaw-pitch autopilot design using dynamic nonlinear inversion and PD-eigenvalue assignment [A], In: Proceedings of the American Control Conference[C], Chicago: IEEE, 2000:1359-1364
[41] Mickle M C and Zhu J J, Skid-To-Turn Control of the APKWS Missile Using Trajectory Linearization Technique [A], In: Proceedings of the American Control Conference[C], Arlington: IEEE, 2001: 3346-3351
[42]张春雨,空天飞行器建模及其自适应轨迹线性化控制研究[D],南京:南京航空航天大学,2003
[43] Liu Y, Huang R, and Zhu JJ, Nonlinear system control using neural networks based on trajectory linearization[A], In: Proceedings of the 2004 IEEE International Conference on Control Applications[C], Taipei: IEEE, 2004: 806-811
[44] Mickle MC and Huang R and Zhu JJ, Unstable, Non-minimum Phase, Nonlinear Tracking by Trajectory Linearization Control [A], In: Proceedings of the 2004 IEEE International Conference on Control Applications[C], Taipei: IEEE, 2004: 812-818
[45] Utkin V I, Variable structure systems with sliding modes [J], IEEE Transactions Automatic Control, 1977, 22(2):212-222
[46] Yu X H, Xu J X, Advances in variable structure system[M], World Scientific Publishing, Singapore, 2000
[47] Yeh F K,Chien H H, Fu L C, Design of optimal midcourse guidance sliding mode control for missiles with TVC[J], Aerospace and Electronic Systems, IEEE Transaction on, 2003,39(3):824-837
[48] Chen Y P, Chang J L, Chu S R, PC-based sliding mode control applied to parallel-type double inverted pendulum system [J], Mechatronics, 1999, 9:553-564
[49] Perruquetti W, Barbot J P, Sliding mode control in engineering, Marcel Dekker Inc., New York, 2002
[50] Utkin A, Guldner J, Shi J X, Sliding mode control in electromechanical system[M], Taylor & Francis, 1999
[51] Chen M S, Hwang Y R, Tomizuka M, A state-dependent boundary layer design for sliding mode control [J], IEEE Transactions on Automatic Control, 20002, 47(10): 1677-1681
[52] Kang B P, Ju J L, Sliding mode controller with filtered signal for robot manipulators using virtual plant controller [J], Mechatronics, 1997, 7(3): 277-286
[53] Ha Q P, Nguyen Q H, Rye D C, Durrant-Whyte, Fuzzy sliding-mode controllers with applications [J], IEEE Transactions on Industrial Electronics, 2001, 48(1):38-41
[54]高为炳,变结构控制的理论及设计方法[M],北京:科学出版社,1996
[55] K B Park, Terminal sliding mode control of second-order nonlinear uncertain systems [J], Int. J. Robust Nonlinear Control, 1999, 9:769-780
[56]庄开宇,变结构控制理论若干问题研究及其应用[D],杭州:浙江大学, 2002
[57]张科,周凤歧,不确定性多变量系统的全程滑态变结构控制方案设计[J ],控制理论与应用, 1999, 16(2): 221-224
[58] Choi S B, Moving switching surface for robust control of second order variable structure system[J], Int. J. Contr., 1993, 38 (1): 229-245
[59] Slotine J J, Tracking control of nonlinear system using sliding surface with application to robust manipulators[J], Int. J. Contr., 1983, 38(3):465-492
[60] Man Z H, Paplinski A P, Wu H R, A robust MIMO terminal sliding mode control scheme for rigid robot manipulators [J], IEEE Transaction on Automatic Control, 1994, 39:2464-2469
[61] Shuanghe Yu, Xinghuo Yu, Robust global Terminal sliding mode control of SISO linear uncertain systems [A], Proceeding of the 35th Conference on Decision and Control[C], Sydney, Australia, Dec, 2000
[62] Xinghuo Yu, Zhihong Man, Fast Terminal sliding mode control design for nonlinear dynamical systems [J], IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications, 2004, 49(2): 261-265
[63] C.W. Tao, J.S. Taur, M. L. Chan, Adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties [J], IEEE Trans. Systems, Man and Cybernetics Part B, 2004, 34 (1):255-262
[64] ShuangheYu, Xinghuo Yu, Non-singular terminal sliding mode of rigid robotic manipulators[J], Automatica, 2002, 38:2159-2167
[65] Y Feng, X H Yu, Z H Man, Continuous finite-time control for robotic manipulators with terminal sliding mode [J], Automatica, 2005, 41:1957-1964
[66] S C Lin, Y Y Chen, RBF network based sliding mode control[J], IEEE International Conference on Systems, Man and Cybernetics, 1994, 1957-1961
[67] D Munoz, D Sbarbaro, An adaptive sliding mode controller for discrete nonlinear systems[J], IEEE Trans. on Industrial Electronics, 2000, 47(3):574-581
[68] J M Carrasco, Sliding mode control of a DC/DC PWM converter with PFC implemented by neural networks[J], IEEE Trans. on Circuits and Systems A: Fundamental Theory and Applications, 1997, 44(8):743-749
[69] S W Kim, J J Lee, Design of a fuzzy controller with fuzzy sliding surface[J], Fuzzy Sets and Systems, 1995, 71(3):359-367
[70] B Yoo, W Ham, Adaptive fuzzy sliding mode control of nonlinear system[J], IEEE Trans. on Fuzzy Systems, 1998, 6(2):315-321
[71] Y S Lu, J S Chen, A self- organizing fuzzy sliding mode controller design for a class of nonlinear servo systems[J], IEEE Trans. on Industrial Electronics, 1994,41(5):492-502
[72] J Y Chen, Expert SMC-based fuzzy control with genetic algorithms[J], Journal of Franklin institute, 1996, 336:589-610
[73] F J Lin, S L Chiu, K K Shyu, Novel sliding mode controller for synchronous motor drive[J], IEEE Trans. on Aerospace and Electronics Systems, 1998, 34(2):532-542
[74] G Wheeler, C H Su, Y Stepanenko, A sliding mode controller with improved adaptation laws for the upper bounds on the norm of uncertainties synchronous motor drive[J], Automatica, 1998, 34(12):1657-1661
[75] Zhou D, Mu CD, Xu WL, Adaptive silding mode guidance of a homing missile[J], Journal of Guidance, Control and Dynamics, 1999, 22(4): 589-592
[76] McGeoch DJ, McGookin EW, Houston SS, MIMO Sliding Mode Attitude Command Flight Control System For A Helicopter [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], San Francisco:AIAA, 2005: 1-15
[77] Shtessel Y, Tournes C, and Krupp D, Reusable launch vehicle control in sliding modes [A], In: AIAA Guidance, Navigation, and Control Conference[C], New Orleans:AIAA, 1997:335-345
[78] Shtessel Y, McDuffie J, Jackson, M, Sliding mode control of the X-33 vehicle in launch and re-entry modes [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit[C], Reston: AIAA, 1998:1352-1362
[79] Shtessel Y, Hall C, and Jackson M, Reusable launch vehicle control in multiple time sale sliding modes[A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit[C], Reston: AIAA, 2000:1-11
[80] Hall C E, Shtessel Y B, Sliding mode disturbance observer-based control for a reusable launch vehicle [A], In: AIAA Guidance Navigation and Control Conference and Exhibit[C], Reston: AIAA, 2005:1-26
[81] Bode, H W, Network analysis and feedback amplifier design, New York, Van Nostrand, 1945
[82] Fan MKH, Tits AL, and Doyle JC, Robustness in the presence of mixed parametric uncertainty and unmodeled dynamics[J], IEEE Transactions on Automatic Control, 1991, 36(1): 25 - 38
[83] Tits AL, The smallμtheorem without real-rational assumption [A], In: Proceedings of the American Control Conference [C], Seattle: IEEE, 1995, 4: 2870 - 2872
[84] Ahmad SS, Lew JS, and Keel LH, Robust control of flexible structures againststructural damage [J], IEEE Transactions on Control Systems Technology, 2000, 8(1): 170– 182
[85] Youla D, Jabr H, and Bongiorno J, Modern Wiener-Hopf design of optimal controllers Part II: The multivariable case[J], IEEE Transactions on Automatic Control, 1976, 21(3): 319– 338
[86] Zhou K, Comparison between H 2and H∞controllers [J], IEEE Transactions on Automatic Control, 1992, 37(8): 1261 - 1265
[87] Lin J L, Postlethweite I, Gu D W,μ? K iteration: A new algorithm forμsynthesis[J], Automatica, 1993, 29:219-224
[88] Tamaki K, Ohishi K, Ohnishi K, Microprocessor-based robust control of a DC servo motor [J], IEEE Control Systems Magazine, 1986, 6(5): 30– 36
[89] Shahruz S M, Performance enhancement of a class of nonlinear systems by disturbance observer [J], IEEE/ASME Transactions on Mechatronics, 2000, 5(3): 319-323
[90] Chen X K, Fukuda T, and Young K D, A New Nonlinear Robust Disturbance Observer [J], Systems & Control Letters, 2000, 41(3):189-199
[91] Chen X K, Komada S, and Fukuda T, Design of a Nonlinear Disturbance Observer[J], IEEE Transactions of Industrial Electronics, 2000, 47(2): 429-437
[92] Chen X K, Su C Y, and Fukuda T, A nonlinear disturbance observer for multivariable systems and its application to magnetic bearing systems, IEEE Transactions on Control Systems Technology, 2004, 12(4): 569-577
[93] Chen W H, Harmonic Disturbance Observer for Nonlinear systems[J], Journal of Dynamic Systems, Measurement and Control, 2003, 125: 114-117
[94] Chen W H, Disturbance Observer Based Control for Nonlinear Systems[J], IEEE/ASME Transactions on Mechatronics, 2004, 9(4):706-710
[95] Kim E, A fuzzy disturbance observer and its application to control, IEEE Transactions on Fuzzy Systems [J], 2002, 10(1): 77-84
[96] Kim E, A discrete-time fuzzy disturbance observer and its application to control, IEEE Transactions on Fuzzy Systems [J], 2003, 11(3): 399-410
[97] Kim E and Park C W, Fuzzy disturbance observer approach to robust tracking control of nonlinear sampled systems with the guaranteed suboptimal H∞performances [J], IEEE Transactions on Systems, Man, and Cybernetics- Part B: Cybernetics, 2004, 34(3): 1574-1581
[98] Kim E and Lee S Y, Output feedback tracking control of MIMO systems using a fuzzy disturbance observer and its application to the speed control of a PM synchronous motor [J], IEEE Transactions on fuzzy systems, 2005, 13(6): 725-741
[99] Narendra K S, and Valavani L, Stable adaptive controller design--Direct control [J], IEEE Transactions on Automatic Control, 1978, 23(4): 570 - 583
[100] Narendra K S, Lin Y H, and Valavani LS, Stable adaptive controller design- Part II: proof of stability [J], IEEE Transactions on Automatic Control, 1980, 25(3):440-448
[101] Morse A S, Global stability of parameter adaptive systems, IEEE Transactions on Automatic Control [J], 1980, 25(3): 433-439
[102] Narendra K S, and Lin YH, Stable discrete adaptive control, IEEE Transactions on Automatic Control [J], 1980, 25(3): 456-461
[103] Narendra K S, and Annaswamy A M, Stable adaptive systems [M], 1st Ed, New Jersey: Prentice-Hall, 1989
[104] Narendra K S, and Parthasarathy K, Identification and control of dynamical systems using neural networks [J], IEEE Transactions on Neural Networks, 1990, 1(1): 4-27
[105] Polycarpou M M, and Ioannou P A, Neural networks as on-line approximators of nonlinear systems [A], In: Proceedings of the 31st IEEE Conference on Decision and Control [C], Tucson: IEEE, 1992: 7 - 12
[106] Ahmed-Zaid F, Ioannou P A, Polycarpou M M, Identification and control of aircraft dynamics using radial basis function networks [A], In: Second IEEE Conference on Control Applications [C], Vancouver: IEEE, 1993: 567-572
[107] Sanner R M, and Slotine J J E, Gaussian networks for direct adaptive control [J], IEEE Transactions on Neural Networks, 1992, 3(6): 837-863
[108] Sadegh N, A perceptron network for functional identification and control of nonlinear systems [J], IEEE Transactions on Neural networks, 1993, 4(6): 982-988
[109] Chen F C, Khalil H K, Adaptive control of nonlinear systems using neural networks, International Journal of Control, 1992, 55(6): 1299-1317
[110] Chen F C, Liu C C, Adaptively controlling nonlinear continuous-time systems using multilayer neural networks [J], IEEE Transactions on Automatic Control, 1994, 39(6): 1306-1310
[111] Niu Y, Wang X, Lam J, Sliding-mode control for nonlinear state-delayed systems using neural-network approximation [J], IEE Proceedings on Control Theory andApplications, 2003, 150(3): 233– 239
[112] Wai R J, Duan R Y, Lee J D, Wavelet neural network control for induction motor drive using sliding-mode design technique [J], IEEE Transactions on Industrial Electronics, 2003, 50(4):733 - 748
[113] Wai R J, Chang J M, Implementation of robust wavelet-neural-network sliding-mode control for induction servo motor drive, IEEE Transactions on Industrial Electronics, 2003, 50(6):1317 - 1334
[114] Lewis F L, Liu K, and Yesildirek A, Neural net robot controller with guaranteed tracking performance [J], IEEE Trans. Neural networks, 1995, 6(3):703-715
[115] Kwan CM, and Lewis FL, Robust backstepping control of induction motors using neural networks[J], IEEE Transactions on Neural Networks, 2000, 11(5): 1178–1187
[116] Choi JY, and Farrell JA, Adaptive observer backstepping control using neural networks [J], IEEE Transactions on Neural Networks, 2001, 12(5): 1103 - 1112
[117] Li YH, Sheng Q, Zhuang XY, Robust and adaptive backstepping control for nonlinear systems using RBF neural networks [J], IEEE Transactions on Neural Networks, 2004, 15(3):693– 701
[118] Y Shtessel, J Jim Zhu and Dan Daniels, Reusable launch vehicle attitude control using time-varying sliding modes[A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Monterey: AIAA, 2002-4779
[119] Y Shtessel, James Strott, J Jim Zhu, Time-varying sliding modes control with sliding mode observer for reusable launch vehicle [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Austin: AIAA, 2003-5362
[120] Eric N. Johnson, A. J. Calise, H. A. El-Shirbiny, Feedback linearization with neural network augmentation applied to X-33 attitude control [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Denver: AIAA, 2000-4157
[121] Roy Smith, Asif Ahmed, Robust parametrically varying attitude controller designs for the X-33 vehicle [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Denver: AIAA, 2000-4158
[122] Marwan Bikdash, Ken Sartor, Abdollah Homaifar, Fuzzy guidance of the shuttle orbiter during atmospheric reentry [J], Control Engineering Practice, 1999 (7): 295-303
[123] Baohua Lian, Hyochoong Bang, J. E. Hurtado, Adaptive Backstepping control based autopilot design for reentry vehicle [A], In: AIAA Guidance, Navigation, and ControlConference and Exhibit [C], Providence: AIAA, 2004-5328
[124] Elmar M. Wallner, Klaus H. Well, Attitude control of a reentry vehicle with internal dynamics [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Monterey: AIAA, 2002-4647
[125] S. -F. Wu, C. J. H. Engelen, R. Babuska, Fuzzy logic full-envelope autonomous flight control for an atmospheric re-entry spacecraft [J], Control Engineering Practice, 2003 (11): 11-25
[126] Marco Caporicci, Luciano Basile, ESA activities on atmospheric re-entry systems [A], In: 12th AIAA International Space Planes and Hypersonic Systems and Technologies [C], Norfolk, AIAA: 2003-7017
[127] S. Juliana, Q. P. Chu, J. A. Mulder, T. J. van Baten, Flight control of atmospheric re-entry vehicle with non-linear dynamic inversion [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Providence: AIAA, 2004-5330
[128] E. Mooij, Direct model reference adaptive control of a winged reentry vehicle [A], In: AIAA 9th International Space Planes and Hypersonic Systems and Technologies Conference [C], Norfolk: AIAA, 99-45484
[129] E. Mooij, Model reference adaptive guidance for reentry trajectory tracking[A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Providence: AIAA, 2004-4775
[130] J. Fatemi, E. Mooij, S. P. Gurav, Reentry vehicle design optimization with intergrated trajectory uncertainties [A], In: AIAA 13th International Space Planes and Hypersonic Systems and Technologies Conference [C], AIAA, 2005-3386
[131] E. Mooij, I. Barkana, Stability analysis of an adaptive guidance and control system applied to winged reentry vehicle [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], San Francisco: AIAA, 2005-6290
[132] M. Ohno, Y. Yamaguchi, T. Hata, M. Takahama, Robust flight control law design for an automatic landing flight experiment [J], Control Engineering Practice, 1999, 7(9):1143-1152
[133] Atsumi Ohara, Yasuhiro Yamaguchi, Toshiki Morito, LPV modeling and gain scheduled control of re-entry vehicle in approach and landing phase [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Montreal: AIAA, 2001-4038
[134] R. R. da Costa, Q. P. Chu, J. A. Mulder, Re-entry flight controller design usingnonlinear dynamic inversion [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Montreal: AIAA, 2001-4219
[135] A. E. Finzi, M. Lavagna, A. Di Gregorio, Atmospheric re-entry trajectory tracking and control for an unmanned space vehicle with a Lyapunov approach [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], Austin: AIAA, 2003-5441
[136]郭锁风,申功璋,吴成富等编著,先进飞行控制系统[M],第1版,北京:国防工业出版社,2003
[137] Shaohua Tan, Chang-chien hang, Joo-siong chai, Gain scheduling: from conventional to neural-fuzzy[J], Automatica, 1997, 33(3):411-419
[138] Rugh W J, Shamma J S, Research on gain scheduling[J], Automatica, 2000, 36(10):1401-1425.
[139] Rugh W J, Analytical framework for gain scheduling[J], IEEE control system magazine, 1991,11(1):79-84.
[140]Thomas Burk, Attitude Control Performance During Cassini Trajectory Correction Maneuvers [A], In: AIAA Guidance, Navigation, and Control Conference and Exhibit [C], San Francisco: AIAA, 2005-6270
[141]朱亮,空天飞行器不确定非线性鲁棒自适应控制[D],南京:南京航空航天大学,2006
[142] Shaughnessy J D, Pinckney S Z, McMinn J D, Hypersonic vehicle simulation model: winged-cone configuration[R], NASA TM-102610,1990
[143]张金槐编著,远程火箭精度分析与评估[M],第1版,长沙:国防科技大学出版社,1995年5月
[144]《飞机设计手册》编辑委员会编,飞机设计手册第一卷[M],第1版,北京:航空工业出版社, 1996:13-278
[145]鲁道夫.布罗克豪斯著,飞行控制[M].第1版,北京:国防工业出版社, 1999年9月
[146]Ramon L Chase, Leon McKinney, A near-term alternative for space access and global range [A], In: 41st AIAA/ASME/SAE/ASEE Joint Propulsion Control Conference and Exhibit [C], Tucson: AIAA, 2005-4191
[147] Steven G L, Leopoldo F. Perez, Steve Fitzgerald, X-38 integrated aero- and aerothermodynamic activities [J], Aerosp. Sci. Technol., 1999 (3): 485-493
[148] Andrew Clark, Chivey Wu, Maj Mirmirani, Sangbum Choi, Development of anairframe–propulsion integrated generic hypersonic vehicle mode[A], In: 44th AIAA Aerospace Sciences Meeting and Exhibit[C], Reno: AIAA, 2006-218
[149]陈谋,不确定非线性综合火力/飞行/推进系统鲁棒控制方法研究[D],南京:南京航空航天大学,2003
[150] Dennis J McNabb, Investigation of atmospheric reentry for the space maneuver vehicle[D], Ohio: Air Force Institute of Technology, 2004
[151] J. Free, S. Glubke, Overview of the TRMM RCS design integration and in–orbit operations[A], In: 35th AIAA/ASME/SAE/ASEE Joint Propulsion Control Conference and Exhibit [C], Los Angeles: AIAA, 99-31252
[152] David A. Throckmorton, Shuttle entry aerothermodynamic flight research: the orbiter experiments programComponent development for micropropulsion systems [A], In: 17th Aerospace Ground Testing Conference [C], Nashville: AIAA, 92-3987
[153] P. M. Danehy, J. A. Wilkes, G. J. Brauckman, Visualization of a capsule entry vehicle reaction control system thruster [A], In: 44th AIAA Aerospace Science Meeting and Exhibit[C], Reno: AIAA, 2006-1532
[154] Bruce G. Powers, Active control technology experience with the space shuttle in the landing regime, NASA Technical Memorandum, NASA-TM-85910
[155] J. Muss, Development of the x-33 gCH4/gO2 RCS thruster[A], In: 35thAIAA/ASME/SAE/ASEE Joint Propulsion Control Conference and Exhibit [C], Los Angeles: AIAA, 99-31055
[156] Nakano M M, Space shuttle on-orbit flight control system[R], AIAA Paper, 82-1576
[156]Louis A, Cassel,Applying jet interaction technology [J]. Journal of Spacecraft and Rockets, 2003, 40(4):523~527
[157]李前国,基于直接力的拦截导弹智能化控制、制导及其视景仿真[D],南京:南京航空航天大学,2003
[158] Richard H S, Control allocation for the next generation of entry vehicle [R], ADA-398505, 2001
[159] N. C. Lowry and R. A. Hall, An improved three-pulse algorithm for axisymmetric RCS control[A], In: AIAA Guidance Navigation and Control Conference and Exhibit [C], Austin: AIAA, 2003-5329
[160] Rui Hirokawa, Koichi Sato, Autopilot design for a missile with reaction-jet using coefficient diagram method [A], In: AIAA Guidance Navigation and Control Conference and Exhibit [C], Montreal: AIAA, 2001-4162
[161] Roark D. Weil, Kevin A. Wise, Blended aero & reaction jet missile autopilot design using VSS techniques [A], In: Proceedings of the 30th Conference on Decision and control[C], 2828-2829
[162] Ajay Thukral. Mario Innocenti, A sliding mode pitch autopilot synethesis for high angle of attack maneuvering [J], IEEE Transactions on Control Systems Technology, 1998, 6(3):359-371
[163] Han Ho Choi, An analysis and design method for uncertain variable structure systems with bounded controllers [J], IEEE Transactions on automatic control, 2004, 49(4):602-608
[164] C. Edwards, S. K. Spurgeon and R. G. Hebden, On the design of sliding mode output feedback controllers [J], INT. J, CONTROL, 2003, 76(9/10):893-905
[165]吴立刚,王常虹,曾庆双,Terminal滑模自适应控制实现一类不确定混沌系统的同步[J ],控制与决策, 2006, 21(2): 229-232
[166]胡剑波,时满宏,庄开宇等,一类非线性系统的Terminal滑模控制[J ],控制理论与应用, 2005, 22(3): 495-502
[167] Park J, Sanberg I W, Universal approximation using radial basis function networks [J],Neural Computation, 1991, 3(2):246-257
[168] Khalil H K, Nonlinear Systems [M], 3rd Ed, Upper Saddle River, New Jersey:Prentice-Hall, 2002
[169] Lewis FL, Liu K, and Yesildirek A, Neural net robot controller with guaranteedtracking performance [J], IEEE Trans. Neural networks, 1995, 6(3):703-715
[170] Lewis FL, Yesildirek A and Liu K, Multilayer neural-net robot controller with guaranteed tracking performance [J], IEEE Trans. Neural networks, 1996, 7(2):388-399
[171] Chin-Pao Hung, Integral variable structure control of nonlinear system using a CMAC neural network learning approach [J], IEEE Trans. On Systems, Man and Cybernetics, Part B, 2004, 34(1):702-709
[172] Fu-Chuang Chen, Chih-Horng Chang, Practical stability issues in CMAC neural network control systems [J], IEEE Trans. Control Systems Technology, 1996, 4(1):86-91
[173] L. X. Wang, Fuzzy systems are universal approximators[A], Proceedings of IEEE Conference on Fuzzy Systems [C], San Diego, 1982: 1163-1170
[174] X. Zeng and M. G. Singh, Approximation theory of fuzzy systems MIMIO case [J], IEEE Transaction on Fuzzy Systems, 1995, 5(3): 219-235
[175] J. E. Slotine and W. Li, Applied nonlinear control [M], Upper Saddle River, NJ: Prentice-Hall, 1991
[176] Antonios Tsourdos, Brian A. White, Adaptive flight control design for nonlinear missile [J], Control Engineering Practice, 2005,13:373-382
[177] Matthew D. Johnson, Anthony J Calise, Eric N. Johnson, Evaluation of an adaptive method for launch vehicle flight control [A], In: 2003 AIAA Guidance Navigation and Control Conference [C],1-19
[178] Shuanghe Yu, Xinghuo Yu, Zhihong Man, A fuzzy neural network approximator with fast terminal sliding mode and its applications [J], Fuzzy Sets and Systems, 2004,148 :469-486
[179] Shuanghe Yu, Xinghuo Yu, Continuous finite-time control for robotic manipulators with terminal sliding mode[J], Automatica, 2005 (41):1957-1964
[180] Shuanghe Yu, Adaptive fuzzy control of nonlinear systems based on terminal sliding mode[J], Lecture Notes on Control and Information Science, 2006 (344):474-479
[181] Vélez-Díaz D, Tang Y, Adaptive Robust Fuzzy Control of Nonlinear Systems [J], IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2004, 34(3): 1596-1601
[182] Lu Y S, Chen J S, Design of a global sliding mode controller for a motor drive with bounded control,[J], Int. J. Contr., 1993, 62(5):1001-1019
[183]池毓东,王岩,邵惠鹤,一种时变滑模平面的设计方法[J],上海交通大学学报,1998, 32(6):70-73
[184]冯勇,安澄全,李涛,采用双滑模平面减小一类非线性系统稳态误差[J],控制与决策,2000, 15(3):361-365
[185] Gu Wen-jin, Zhang Yi-fei, Li Chang-ping, Composite control of linear/adaptive variable structure control [J], Chinese Journal of Aeronautics, 2001, 14(1):49-57
[186] Xing Wang, Yigao Deng, Yafeng Wang, Composite control of linear/adaptive TSM control for mass-controlled saucer-like air vehicle[A], Proceedings of the 6th World Congress on Intelligent Control and Automation , Dalian, China, June 21-23, 2006
[187]黄辉先,史忠科,吴方向,一类非线性系统的时变滑模控制[J ],控制理论与应用, 2000, 17(5): 774-776
[188]冯勇,池毓东,任倩,具有全局鲁棒性的时变滑模平面的设计方法[J ],宇航学报, 1997, 18(3): 59-64
[189] Takagi T and Sugeno M, Fuzzy Identification of Systems and its Applications to Modeling and Control [J], IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 1985, 15:116-163
[190] Joongseon J, Chen YH and Reza L, On the Stability Issues of Linear Takagi-Sugeno Fuzzy Models[J], IEEE Transation on Fuzzy Systems, 1998, 6(3): 402-410
[191] Yang Y S and Zhou C J, Robust Adaptive Control of Uncertain Nonlinear Systems Using Fuzzy Logic Systems [C], Proceedings of the 2005 IEEE International Symposium on Intelligent Control, Limassol, Cyprus, June 27-29, 2005
[192] Wang Y, Guan Z H, Wen X, Adaptive Synchronization for Chen Chaotic System with Fully Unknown Parameters[J] , Chaos, Solitons and Fractals, 2004,19(4):899-903
[193] Chih-Chiang Cheng, Adaptive sliding mode controller design based on T-S fuzzy system models[J],Automatica, 2006(42):1005-1010
[194] Stephen A W, Timothy R M., Measurement uncertainty and feasibility study of a flush airdata system for a hypersonic Flight experiment [R], NASATM-4627, 1994
[195]韩京清,一类不确定对象的扩张状态观测器[J],控制与决策,1995,10(1):85-88
[196]韩京清,自抗扰控制器及其应用[J],控制与决策,1998,13(1):19-23
[197]林飞,用于带有量测噪声系统的新型扩张状态观测器[J],理论与应用,2005(22):995-998
[198]王新华,基于扩张观测器的非线性不确定系统输出跟踪[J],控制与决策,2004,19(10):1113-1116
[199] A Pennallegue, A Mokhtari, L Fridman, Feedback linearization and high order sliding mode observer for a quadrotor UAV [C], Proceedings of the 2006 International Workshop on Variable Structure Systems, Alghero, Italy, June 5-7, 2006
[200]武利强,韩京清,直线型倒立摆的自抗扰控制设计方案[J],制理论与应用, 2004,21(5):665-669
[201] Levant A., Universal Single-Input-Single-Output (SISO) sliding-mode controllers with finite-time convergence [J], IEEE Transactions on Automatic Control, 2001(9):1447-1451
[202] Yi Xiong, Sliding mode observer for nonlinear uncertain systems [J],IEEE Trans. Auto. Control, 2001, 46(12):2012-2017
[203]刘强,于达仁,王仲奇,高超声速飞行器的滑模观测器设计[J],航空学报,2004,25(6):589-594
[204] Levant A., Robust exact differentiation via sliding mode technique [J], Automatica, 1998,34(3): 379-384