T-S模糊系统的若干控制器设计及应用问题研究
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摘要
近年来,作为控制领域热点研究问题之一的非线性系统控制理论得到了长足的发展。在许多实际情况下,非线性系统很难通过已知函数精确地描述。由于模糊系统理论不需要精确的数学模型并且可以有效的利用专家知识,从而成功地应用于许多控制问题中。模糊控制系统在理论研究和工程实践方面得到了长足发展。T-S模糊模型的优点在于用它进行系统分析和控制器设计时,通过对非线性系统进行模糊建模,然后可利用一套系统化的方法来研究非线性系统的稳定性以及控制器设计问题。当系统存在不确定性和时滞时,系统的性能往往会受到严重的影响,甚至使系统不稳定。因此本文利用Lyapunov稳定性理论和线性矩阵不等式(LMI)方法,研究了T-S模糊系统的稳定性分析和控制器设计的相关问题,以及在网络和混沌同步方面的应用。论文的主要研究成果有以下几个方面：
针对T-S模糊系统的H∞控制问题,给出了更简洁更具整体性的系统稳定和H∞控制存在的充分条件。该条件把子系统间的相互作用考虑在一个矩阵中,并以LMI的形式给出,克服了以往在T-S模糊系统与稳定性相关问题的研究中对于子系统之间的相互作用考虑不多的缺陷。最后通过仿真例子验证了所提出方法的有效性。
实际控制系统中,很多时候状态是不完全可测的。基于LMI处理方法,研究了不确定T-S模糊系统的动态输出反馈控制问题。给出了控制器作用下的闭环系统渐近稳定的充分条件。将求解控制器的问题转化为凸的可解的线性矩阵不等式问题。因此,可以直接利用LMI工具箱求解控制器参数。数值例子说明了动态输出反馈控制器的优良性能。
考虑了基于T-S模糊模型描述的不确定系统多目标控制问题。通过构造动态输出反馈控制器,对于已知的性能指标和模糊控制律提出了闭环系统稳定的充分条件,并且把该充分条件表示为一组线性矩阵不等式。使得闭环系统同时满足区域极点配置和H∞干扰抑制要求。数值仿真例子说明了所提出的设计方法是有效的。
针对连续T-S模糊时滞系统的时滞相关稳定性分析与综合问题进行了研究。通过构造更多地考虑系统状态方程中各项间相互关系的Lyapunov-Krasovksii泛函,提出了一种新的基于LMI的时滞相关稳定性判据。该判据与现有判据相比具有更低的保守性,而且放宽了对控制对象时滞的约束条件,适用对象更广泛。通过相应的数值仿真说明了所提出设计方法的有效性。
网络拥塞已经成为制约网络发展和应用的重要问题。主动队列管理是一种基于路由器的拥塞控制机制,由于该机制能够很好地抑制拥塞而得到了广泛研究。网络系统本身存在非线性和不确定性等因素导致网络形成了一个复杂的大系统,因此需要设计鲁棒性更强的主动队列管理算法(AQM),以便获得更好的拥塞控制效果。针对TCP/IP网络拥塞控制问题,利用T-S模糊模型具有很好地逼近非线性系统的特点,设计了基于模糊观测器的T-S模糊控制器。对非线性TCP/IP网络拥塞控制系统进行了T-S模型的建模,通过选取适当的模糊规则和隶属函数来提高拥塞控制系统的性能,并给出了理论性证明。仿真结果表明所设计的控制器对活动的TCP连接数、链路带宽及往返时延的不确定性具有很强的稳定性和鲁棒性。
混沌是一种特殊的复杂的非线性系统,普遍存在于自然界中。由于混沌系统具有内在的随机性和对初值的敏感性等特点,使其被广泛的应用于保密通信、信号处理、图像处理等方面。本文对于混沌系统的完全同步和广义投影同步问题进行了研究。首先,针对一类基于T-S模糊模型描述的混沌系统给出了模糊滑模控制器的设计。该控制器可以达到驱动-响应系统的渐近同步。其次,对于混沌时滞系统广义投影同步问题进行了研究。利用LMI把混沌系统的广义投影同步问题转化为模糊状态观测器设计问题,利用Lyapunov稳定性理论证明了所提方案的可行性和全局稳定性。最后,通过仿真验证了所提方法的有效性。
最后对全文作出总结,并提出了下一步研究的方向。
As an active research field, the nonlinear system theory makes a rapid progress recently, which plays an important role in control fields. However, nonlinear systems can not be described with given functions precisely in many practical cases. Fuzzy system theory is successfully applied to many control problems because it does not need to accurate mathematical models of the system and can cooperate with human experts' knowledge. Recent years have witnessed rapidly growing of fuzzy systems in theory and engineering applications. An advantage of a T-S fuzzy model is that, when it is applied to the system analysis and controller design, one can first represent a nonlinear system as a fuzzy model, and then study the problems of stability and controller design by using a systematic approach.
When uncertainties and time delays always appear in a T-S fuzzy system, they may affect the system performance seriously, and even make the system unstable. Hence by using Lyapunov stability theory and linear matrix inequality (LMI) technique, this dissertation investigates stability analysis and synthesis problems of T-S fuzzy system and the applications to the network and chaotic system. The main contributions are listed as the following:
For T-S fuzzy systems, H∞control problem is considered. A more concise and integral sufficient condition of system stability and and controller existence is proposed, which overcomes such a drawback that the interactions among the fuzzy subsystems are not considered. And it collects the subsystem interactions in a matrix, and is in the terms of linear matrix inequalities (LMI). The simulation example shows the effectiveness of the proposed method.
In real word control problems, the system states may not be completely accessible. Based on the LMI technique, the problem of the dynamic output feedback control for uncertain T-S fuzzy system is concerned. A dynamic output feedback controller is designed so that the resulted closed-loop systems are asymptotically stable. The solution of the controller is transformed into the solvability of convex linear matrix inequalities. Therefore, the parameter of the controller could be solved directly via the LMI toolbox. The perfect performance of the dynamic output feedback controller is demonstrated by numerical examples.
The fuzzy multi-objective control problem is investigated for a class of uncertain nonlinear system based on T-S fuzzy model. By constructing dynamic output feedback controller, sufficient conditions are presented in terms of LMI for given performance index and fuzzy control law, such that the closed-loop system is quadratically stable. The resulting closed-loop system satisfies the domain pole placement requirement and H∞disturbance attenuation. A numerical simulation example is presented to illustrate the effectiveness of the proposed design method.
Delay-dependent stability analysis and synthesis of continuous time delay T-S fuzzy systems are thoroughly studied. By constructing Lyapunov-Krasovksii functions that take more consideration of interrelationship of various factors in system state functions, a new delay-dependent stability criterion via LMI approach is proposed. The delay-dependent condition has lower conservation and relaxes the restriction to time delays of the control systems. The corresponding numerical simulation results demonstrate the effectiveness of the proposed schemes.
The network congestion has become an important problem, which restricts the development and application of networks. Active queue management (AQM) is a kind of congestion control mechanism based on router. Nowadays AQM is extensively studied due to good effect for constraining congestion. TCP network is a complex large system with respect to the nature of nonlinearity and uncertainty, which requires a kind of more robust AQM algorithm in order to obtain better congestion control effect. T-S fuzzy model can suitably representate a nonlinear system, and a fuzzy observer-based T-S fuzzy controller is designed. A T-S fuzzy model is built for the nonlinear TCP/IP network congestion control system. The performance of network congestion control system is improved by choosing appropriate fuzzy rules and membership functions, and the stability of the system is rigorously theoretic proved. Simulation results in different scenarios demonstrate that the proposed controllers have good stability and robustness with respect to the uncertainties of the number of active TCP sessions, link capacity and the round-trip time (RTT).
Chaos is a kind of special complicated nonlinear systems, which is ubiquitous in nature. Because such chaotic systems progress certain features, such as high randomicity and hyper sensitivity to initial conditions, the application of chaos can be especially found in secure communications, signal progressing and image progressing etc. Chaos synchronization has become the key technique in secure communications. The complete synchronization and generalized projective synchronization of chaotic systems are discussed in this dissertation. First, the fuzzy sliding mode controller is given for a class of chaotic systems based on T-S fuzzy system. It can guarantee asymptotical synchronization of both drive and response systems. Then the generalized projective synchronization problem of delay chaotic systems is studied. A new fuzzy state observer is designed in terms of LMI. According to the Lyapunov stability theory, it is verified that the proposed scheme is feasible and globally stable. Finally, the corresponding numerical simulation results demonstrate the effectiveness of the proposed schemes.
Lastly, the summary of the whole dissertation is given and the research directions in future are put forward.
针对T-S模糊系统的H∞控制问题,给出了更简洁更具整体性的系统稳定和H∞控制存在的充分条件。该条件把子系统间的相互作用考虑在一个矩阵中,并以LMI的形式给出,克服了以往在T-S模糊系统与稳定性相关问题的研究中对于子系统之间的相互作用考虑不多的缺陷。最后通过仿真例子验证了所提出方法的有效性。
实际控制系统中,很多时候状态是不完全可测的。基于LMI处理方法,研究了不确定T-S模糊系统的动态输出反馈控制问题。给出了控制器作用下的闭环系统渐近稳定的充分条件。将求解控制器的问题转化为凸的可解的线性矩阵不等式问题。因此,可以直接利用LMI工具箱求解控制器参数。数值例子说明了动态输出反馈控制器的优良性能。
考虑了基于T-S模糊模型描述的不确定系统多目标控制问题。通过构造动态输出反馈控制器,对于已知的性能指标和模糊控制律提出了闭环系统稳定的充分条件,并且把该充分条件表示为一组线性矩阵不等式。使得闭环系统同时满足区域极点配置和H∞干扰抑制要求。数值仿真例子说明了所提出的设计方法是有效的。
针对连续T-S模糊时滞系统的时滞相关稳定性分析与综合问题进行了研究。通过构造更多地考虑系统状态方程中各项间相互关系的Lyapunov-Krasovksii泛函,提出了一种新的基于LMI的时滞相关稳定性判据。该判据与现有判据相比具有更低的保守性,而且放宽了对控制对象时滞的约束条件,适用对象更广泛。通过相应的数值仿真说明了所提出设计方法的有效性。
网络拥塞已经成为制约网络发展和应用的重要问题。主动队列管理是一种基于路由器的拥塞控制机制,由于该机制能够很好地抑制拥塞而得到了广泛研究。网络系统本身存在非线性和不确定性等因素导致网络形成了一个复杂的大系统,因此需要设计鲁棒性更强的主动队列管理算法(AQM),以便获得更好的拥塞控制效果。针对TCP/IP网络拥塞控制问题,利用T-S模糊模型具有很好地逼近非线性系统的特点,设计了基于模糊观测器的T-S模糊控制器。对非线性TCP/IP网络拥塞控制系统进行了T-S模型的建模,通过选取适当的模糊规则和隶属函数来提高拥塞控制系统的性能,并给出了理论性证明。仿真结果表明所设计的控制器对活动的TCP连接数、链路带宽及往返时延的不确定性具有很强的稳定性和鲁棒性。
混沌是一种特殊的复杂的非线性系统,普遍存在于自然界中。由于混沌系统具有内在的随机性和对初值的敏感性等特点,使其被广泛的应用于保密通信、信号处理、图像处理等方面。本文对于混沌系统的完全同步和广义投影同步问题进行了研究。首先,针对一类基于T-S模糊模型描述的混沌系统给出了模糊滑模控制器的设计。该控制器可以达到驱动-响应系统的渐近同步。其次,对于混沌时滞系统广义投影同步问题进行了研究。利用LMI把混沌系统的广义投影同步问题转化为模糊状态观测器设计问题,利用Lyapunov稳定性理论证明了所提方案的可行性和全局稳定性。最后,通过仿真验证了所提方法的有效性。
最后对全文作出总结,并提出了下一步研究的方向。
As an active research field, the nonlinear system theory makes a rapid progress recently, which plays an important role in control fields. However, nonlinear systems can not be described with given functions precisely in many practical cases. Fuzzy system theory is successfully applied to many control problems because it does not need to accurate mathematical models of the system and can cooperate with human experts' knowledge. Recent years have witnessed rapidly growing of fuzzy systems in theory and engineering applications. An advantage of a T-S fuzzy model is that, when it is applied to the system analysis and controller design, one can first represent a nonlinear system as a fuzzy model, and then study the problems of stability and controller design by using a systematic approach.
When uncertainties and time delays always appear in a T-S fuzzy system, they may affect the system performance seriously, and even make the system unstable. Hence by using Lyapunov stability theory and linear matrix inequality (LMI) technique, this dissertation investigates stability analysis and synthesis problems of T-S fuzzy system and the applications to the network and chaotic system. The main contributions are listed as the following:
For T-S fuzzy systems, H∞control problem is considered. A more concise and integral sufficient condition of system stability and and controller existence is proposed, which overcomes such a drawback that the interactions among the fuzzy subsystems are not considered. And it collects the subsystem interactions in a matrix, and is in the terms of linear matrix inequalities (LMI). The simulation example shows the effectiveness of the proposed method.
In real word control problems, the system states may not be completely accessible. Based on the LMI technique, the problem of the dynamic output feedback control for uncertain T-S fuzzy system is concerned. A dynamic output feedback controller is designed so that the resulted closed-loop systems are asymptotically stable. The solution of the controller is transformed into the solvability of convex linear matrix inequalities. Therefore, the parameter of the controller could be solved directly via the LMI toolbox. The perfect performance of the dynamic output feedback controller is demonstrated by numerical examples.
The fuzzy multi-objective control problem is investigated for a class of uncertain nonlinear system based on T-S fuzzy model. By constructing dynamic output feedback controller, sufficient conditions are presented in terms of LMI for given performance index and fuzzy control law, such that the closed-loop system is quadratically stable. The resulting closed-loop system satisfies the domain pole placement requirement and H∞disturbance attenuation. A numerical simulation example is presented to illustrate the effectiveness of the proposed design method.
Delay-dependent stability analysis and synthesis of continuous time delay T-S fuzzy systems are thoroughly studied. By constructing Lyapunov-Krasovksii functions that take more consideration of interrelationship of various factors in system state functions, a new delay-dependent stability criterion via LMI approach is proposed. The delay-dependent condition has lower conservation and relaxes the restriction to time delays of the control systems. The corresponding numerical simulation results demonstrate the effectiveness of the proposed schemes.
The network congestion has become an important problem, which restricts the development and application of networks. Active queue management (AQM) is a kind of congestion control mechanism based on router. Nowadays AQM is extensively studied due to good effect for constraining congestion. TCP network is a complex large system with respect to the nature of nonlinearity and uncertainty, which requires a kind of more robust AQM algorithm in order to obtain better congestion control effect. T-S fuzzy model can suitably representate a nonlinear system, and a fuzzy observer-based T-S fuzzy controller is designed. A T-S fuzzy model is built for the nonlinear TCP/IP network congestion control system. The performance of network congestion control system is improved by choosing appropriate fuzzy rules and membership functions, and the stability of the system is rigorously theoretic proved. Simulation results in different scenarios demonstrate that the proposed controllers have good stability and robustness with respect to the uncertainties of the number of active TCP sessions, link capacity and the round-trip time (RTT).
Chaos is a kind of special complicated nonlinear systems, which is ubiquitous in nature. Because such chaotic systems progress certain features, such as high randomicity and hyper sensitivity to initial conditions, the application of chaos can be especially found in secure communications, signal progressing and image progressing etc. Chaos synchronization has become the key technique in secure communications. The complete synchronization and generalized projective synchronization of chaotic systems are discussed in this dissertation. First, the fuzzy sliding mode controller is given for a class of chaotic systems based on T-S fuzzy system. It can guarantee asymptotical synchronization of both drive and response systems. Then the generalized projective synchronization problem of delay chaotic systems is studied. A new fuzzy state observer is designed in terms of LMI. According to the Lyapunov stability theory, it is verified that the proposed scheme is feasible and globally stable. Finally, the corresponding numerical simulation results demonstrate the effectiveness of the proposed schemes.
Lastly, the summary of the whole dissertation is given and the research directions in future are put forward.
引文
1. 陈际达.线性控制系统[M].长沙：中南工业大学,1990.
2. Kaeear V. A bridge between state-space transfer-function method [J]. Annual Reviews in Contorl,1999,23:177-184.
3. 胡寿松.自动控制原理[M].北京：科学出版社,2001.
4. 刘艳军.非线性系统自适应模糊控制算法的研究[D].大连理工大学博士学位论文,2007.
5. 解学书.最优控制：理论与应用[M].北京：清华大学出版社,1986.
6. 舒迪前.预测控制系统及其应用[M].北京：机械工业出版社,1996.
7. 叶其华,王晨皓,吴捷.基于自组织模糊神经网络电力系统稳定器的设计[J].控制理论与应用,1999,15(5)：687-690.
8. 陈余庆.多移动机器人的协作运动控制研究[D].大连理工大学博士学位论文,2006.
9. 李清泉.自适应控制系统理论、设计与应用[M].北京：科学出版社,1990.
10.巩诚.T-S模糊时滞系统的鲁棒H∞控制及滤波[D].哈尔滨工业大学博士学位论文,2008.
11. Zadeh LA. Fuzzy sets [J]. Information and Control,1965,8:338-353.
12. Zadeh L A. Outline of a new approach to the analysis of complex systems and decision processes [J]. IEEE Transactions on Systems Man and Cybernetics,1973,3(1):28-44.
13. Wang L X. Adaptive fuzzy systems and control:design and stability analysis [M]. Englewood Cliffs, N J:Prentice-Hall,1994.
14.王立新.模糊系统与模糊控制教程[M].北京：清华大学出版社,2003.
15. Mamdani E H, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller [J]. International Journal of Man Machine Studies,1975,7(1):1-5.
16. Mamdani E H. Application of fuzzy algorithms for control of simple dynamic plant [J], IEE Proceedings Control and Science.1974,121(12):1585-1588.
17. Holmblad L P, Ostergaard J J. Control of cement kiln by fuzzy logic [J]. Fuzzy Information and Decision Proeesses, New York:North-Holland,1982.
18. Ysger R R. Ordered Weighted Averaging Aggregation Operator in Muti-criteria Decision Making [J]. IEEE Transactions on Systems Man and Cybernetics,1988,18:183-190.
19. Kiekert W, Mamdani E. Analysis of a fuzzy logic controller [J]. Fuzzy Sets and Systems,1978, 1:29-44.
20. Tong R. Analysis and control of fuzzy systems using the relation matrix [J]. International Journal of Control,1978,27:679-686.
21. Braae M R. Selection of parameters for a fuzzy logic controller [J]. Fuzzy Sets and Systems, 1979,2:185-199.
22邓聚龙.Fuzzy控制的稳定性问题[J].模据数学,1983,3：71-84.
23. Kiszka J, Gupta M, Nikiforuk P. Energetic stability of fuzzy dynamic systems [J]. IEEE Transactions on Systems, Man and Cybernetics,1985,15:783-792.
24.王士同.神经模糊系统及其应用[M].北京：北京航空航天大学出版社,1998.
25. Filev D P, Yager R R. A generalized defuzzifieation method via BAD distributions [J]. International Journal of Intelligent Systems,1991,6(4):687-697.
26.李丽.非线性模糊时滞系统的鲁棒控制研究[D].大连理工大学博士学位论文,2008.
27. Takagi T, Sugeno M. Fuzzy identifieation of systems and its applications to modeling and control [J]. IEEE Transactions on Systems, Man and Cysberneties,1985,15:116-132.
28. Chen C L, Chang M H. Optimal design of fuzzy sliding mode control:A comparative [J]. Fuzzy Sets Systems,1998,93:37-48.
29. Glower S, Munoghan J. Designing fuzzy controllers from a variable structures standpoint [J]. IEEE Transactions on Fuzzy Systems,1997,5(1):138-144.
30. Palm R. Robust control by fuzzy sliding mode [J]. Automatica,1994,30:1429-1437.
31. Chen J Y. Rule regulation of fuzzy sliding mode controller design:Direct adaptive approach [J]. Fuzzy Sets and Systems,2001,120:159-168.
32. Feng G. A survey on analysis and design of model-based fuzzy control systems [J]. IEEE Transactions on Fuzzy Systems,2006,14(5):676-697.
33. Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control [J]. IEEE Transactions on Systems, Man and Cybernetics,1985,15(1):116-132.
34. Sugeno M, Kang G T. Structure identffication of fuzzy model [J]. Fuzzy Sets and Systems, 1988,28(10):15-33.
35. Feng G, Cao S G, Rees N W, et al. Design of fuzzy control systems with guaranteed stability [J]. IEEE Transactions on Systems, Man and Cybernetics,1997,85(1):1-10.
36. Cao S G, Rees N W, Feng G. Analysis and design for a class of complex control systems part II [J]. Automatica,1997,33(6):1017-1028.
37. Tanaka K, Ikeda T, Wang H O. Robust stabilization of a class of uncertain nonlinear systems via fuzzy control:quadratic stabilkizability, H∞ control theory, and linear matrix inequalities [J]. IEEE Transactions on Fuzzy Systems,1996,4(1):1-13.
38. Tanaka K, Ikeda T, Wang H O. Fuzzy regulator and fuzzy observers:relaxed stability conditions and LMI-based design [J]. IEEE Transactions on Fuzzy Systems,1998,6(2): 250-265.
39. Tanaka K, Wang H O. Fuzzy control systems design and analysis:A linear matrix inequality approach [M]. New York:John Wiley & Sons,2001.
40. Matia F, Aihadithi B, Jimenez A. Generalization of stability criterion for Takagi-Sugeno continuous fuzzy model [J]. Fuzzy Sets and Systems,2002,129(3):295-309.
41.巩长忠.基于T-S模糊模型的控制方法及稳定性分析[D].东北大学博士学位论文,2003.
42.王岩,张庆灵,孙增圻.离散模糊系统分析与设计的模糊Lyapunov方法[J].自动化学报,2004,30(2)：255-260.
43. Xiu Z H, Ren G. Stability analysis and systematic design of Takagi-Sugeno fuzzy control systems [J]. Fuzzy Sets and Systems,2005,151(1):119-138.
44.王利魁.离散Takagi-Sugeno模糊控制系统的稳定性研究[D].大连理工大学博士学位论文,2009.
45. Nguang S K, Shi P. H∞ fuzzy output feedback control desugn for nonlinear systems:An LMI approach [J]. IEEE Transactions on Fuzzy Systems,2003,11(3):331-340.
46. Teixeira M C M, Assuncao E, Avellar R G. On relaxed LMI-based design for fuzzy regulators and fuzzy observers [J]. IEEE Transactions on Fuzzy Systems,2003,11:613-623.
47. Zuo Z Q, Wang Y J. Robust stability criteria of uncertain fuzzy systems with time-varying delays [C]. IEEE International Conference on Systems, Man and Cybernetics,2005, 1303-1307.
48. Ban X J, Gao X Z, Huang X L, et al. Stability analysis of simplest Takagi-Sugeno fuzzy control system using circle criterion [J]. Information Sciences,2007,177(20):4387-4409.
49. Ding Y S, Ying H, Shao S H. Structure and stability analysis of a Takagi-Sugeno fuzzy PI controller with application to tissue hypermermia therapy [J]. Soft Computing.1999,2(4): 183-190.
50.张吉礼,欧进萍,于达仁.基于相平面轨迹特征的规则自调整模糊控制方法[J].控制理论与应用,2003,20(4)：607-611.
51. Abdelnour Q, Cheung J Y, Chnag C. Application of deseribing functions in the transient response analysis of a three-term fuzzy control [J]. IEEE Tnarsaetions on Sysetms, Man and Cybenreties,1993,23 (2):603-606.
52. Lo J C, Lin Y T. State feedback via circle criterion for systems subject to input saturations [C]. IEEE International Conference on Networking, Sensing and Control, Taiwan,2004,2:920-925.
53. Glower J S, Munighna J. Desinging fuzzy controllers from a variable structure standpoint [J]. IEEE Transactions on Fuzzy Systems,1997,5(1):138-144.
54. Htani Y O, Yoshirnura T. Fuzzy control of manipulator using the concept of sliding mode [J]. International Jounral of Sysetms Seience,1996,27(2):179-186.
55. Kim E, Lee H. New approaches to relaxed quadratic stability condition of fuzzy control systems [J]. IEEE Transactions on Fuzzy Systems,2000,8(5):523-534.
56. Liu X D, Zhang Q L.Approaches to quadratic stability conditions an H∞ control designs for T-S fuzzy systems [J]. IEEE Transactions on Fuzzy,2003,11(6):830-839.
57. Fang C H, Liu Y S, Kau S W, et al. A new LMI-based approach to relaxed quadratic stabilization of T-S fuzzy control systems [J]. IEEE Transactions on Fuzzy Systems,2006, 14(3):386-397.
58. Cao S G, Ress N W, Feng G. Analysis and design of fuzzy control systems using dynamic fuzzy-state space models [J]. IEEE Transactions on Fuzzy Systems,1999,7(2):192-200.
59. Johansson M, Rantzer A, Arzen K E. Piecewise quadratic stability of fuzzy systems [J]. IEEE Transactions on Fuzzy Systems,1999,7(6):713-722.
60. Tanaka K, Hori T, Wang H O. A fuzzy Lyapunov approach to fuzzy control system design [C]. Proceedings of the American Control Conference, Arlington, V A,2001,6:4790-4795.
61. Choi D, Park P. H∞ state feedback controller design for discrete time fuzzy systems using fuzzy weighting dependent Lyapunov functions [J]. IEEE Transactions on Fuzzy Systems, 2003,11(2):271-278.
62. Hong S K, Langari R. An LMI-based Fuzzy Control System Design with T-S Framework [J]. Information Sciences,2000,123:163-179.
63. Xie L. Output feedback H∞ control of systems with parameter uncertainty [J]. Internal Journal Control,1996,63(4):741-750.
64. Chen B S, Tseng C S, Uang H J. Mixed H2/H∞ fuzzy output feedback control design for nonlinear dynamic systems:An LMI approach [J]. IEEE Transactions on Fuzzy Systems,2000, 8(2):249-265.
65. Zhang N, Feng G. H∞, output feedback control design of fuzzy dynamic via LMI [J]. Acta Automatica Sinica,2001,27(4):495-509.
66. Lin C, Wang Q G, Lee T H. H∞ output tracking control for nonlinear systems via T-S fuzzy model approach [J]. IEEE Transactions on Systems, Man and Cybernetics, part B:Cybernetics, 2006,36(2):450-457.
67. Liu X D, Zhang Q L. New approaches to H∞ controller designs based on fuzzy observers for T-S fuzzy systems via LMI [J]. Automatica,2003,39:1571-1582.
68. Lin C, Wang Q G, Lee T H. Improvement on observer-based H∞ control for T-S fuzzy systems [J]. Automatica,2005,41:1651-1656.
69. Yoneyama J, Masahiro N, Hitoshi K, Akria I. Output stsbilization of Takagi-Sugeno fuzzy systems [J]. Fuzzy Sets and Systems,2000,111(2):142-151.
70. Cao S G, Rees N W, Feng G. H∞ contort of nonlinear discrete time systems based on dynamical fuzzy models [J]. International Journal of System Sciences,2000,31:229-241.
71. Cao S G, Rees N W, Feng G. H∞ control of nonlinear continuous time systems based on dynamical fuzzy models [J]. International Journal of System Science,1996,27:821-830.
72. Cao S G, Rees N W, Feng G. H∞ contorl of uncertain fuzzy continuous time systems [J]. Fuzzy Sets and Systems,2000,115:171-190.
73. Zhou S S, Lam J, Zheng W X. Control design for fuzzy systems based on relaxed nonquadratic stability and H∞ performance conditions [J]. IEEE Transactions on Fuzzy System,2007,15(2): 188-199.
74. Willems J C. Least squares stationary optimal control and algebraic Riccati equation [J]. IEEE Trnasactions on Automatie Control,1971,16(1):621-634.
75. Neestorv Y, Nemiorvskii A. A general approach to polynomial-time algorithms design for convex programming [C]. Technical report, Cent. Econ. & Math. Inst., USSR Acad. Sci., Moscow, USSR,1998.
76. Boyd S, Ghaoui E, Feorn E, Balakrishnna V. Linear matrix inequality in systems and conrtol theory [M]. Philadelphia, PA:SIAM,1994.
77. Gahinet P, Nemirovaki A, Laub A J, Alahmoud C. LMI Control Toolbox [M]. The Math Works, Inc.1995,1-200.
78.俞立.鲁棒控制—线性矩阵不等式处理方法[M].北京：清华大学出版社,2002.
79. Gahinet P, Nemirovski A, Laub A J, et al. LMI Control toolbox [M]. The Math Works Inc, Natick,1995.
80.李泽滔.采用MATLABLMI工具进行控制系统鲁棒分析[J].机械与电子,2000,6：3942.
81. Callender A, Haritree D R, Porter A. Time lag in a control system [J]. Philosophical Transactions of the Royal Society of London,1996,235:415-444.
82. Cao Y Y, Frank P M. Analysis and synthesis of nonlinear time-delay systems via fuzzy control approach [J]. IEEE Trnasaetions on Fuzzy System,2000,8(2):200-211.
83. Cao Y Y, Frank P M. Stability analysis and synthesis of nonlinear time-delay systems via linear Takagi-Sugeno fuzzy models [J]. Fuzzy Sets and Systems,2001,124(2):213-229.
84. Chen B, Liu X P, Lin Chong, Liu K. Robust H∞ control of Takagi-Sugeno fuzzy systems with state and input time delays [J]. Fuzzy Sets and Systems,2009,160(4):403-422.
85. Zhang B Y, Lam J, Xu S Y, Shu Z, Robust stabilization of uncertain T-S fuzzy time-delay systems with exponential estimates [J]. Fuzzy Sets and Systems,2009,160(12):1720-1737.
86. Yu K W, Lien C H. Robust H control of uncertain T-S fuzzy systems with state and input time delays [J]. Chaos Solions & Fractals,2008,37:150-156.
87. Guan X P, Chen C L. Delay-dependent guaranteed cost control for T-S fuzzy systems with time delays [J]. IEEE Transaction on Fuzzy Systems,2004,12:236-249.
88. Wang R J, Lin W, Wang W J. Stabilizability of linear quadratic state feedback for uncertain fuzzy time-delay systems [J]. IEEE Transactions on Systems, Man and Cybernetics,2004, 34(2):1288-1292.
89. Chiang T S, Chiu C S, Liu P. Output regulation for discrete-time nonlinear time-varying delay system:An LMI Approach [C]. IEEE International Conference on fuzzy Systems,2004,3: 1269-1273.
90.韩安太,王树青.基于LMI的一类非线性时滞关联大系统的分散模糊控制[J].控制与决策,2004,19(4)：416-419.
91. Guna X P, Chen C L. Delay-dependent guanarteed cost control T-S fuzzy systems with time delays [J]. IEEE Transactions on Fuzzy Systems,2004,12(2):236-249.
92. Chen B, Liu X P, Tong S C. Delay-dependent stability analysis and control synthesis of fuzzy dynamic systems with time delay [J]. Fuzzy Sets and Systems,2006,157(16):2224-2240.
93. Chen B, Liu X P, Tong S C. New delay-dependent stabilization conditions of T-S fuzzy systems with constant delay [J]. Fuzzy Sets and Systems,2007,158(20):2209-2224.
94. Tian E G, Peng C. Delay-dependent stability analysis and synthesis of uncertain T-S fuzzy systems with time-varying delay [J]. Fuzzy Sets and Systems,2006,157:544-559.
95. Jiang X F, Han Q L. Robust H∞ control for uncertain Takagi-Sugeno fuzzy systems with interval time-varying delay [J]. IEEE Transaction on Fuzzy Systems,2007,15(2):321-331.
96. Wu H N, Li H X. New approach to delay-dependedt stability analysis and stabilization for continuous-time fuzzy systems with time-varying delay [J]. IEEE Transaction on Fuzzy Systems,2007,15(3):482-493.
97. Chen B, Liu X P, Tong S C. Robust fuzzy control of nonlinear systems with input delay [J]. Chaos Solitons and Fractals,2008,37(3):894-901.
98. Lien C H, Yu K W. Robust control for Takagi-Sugeno fuzzy systems with time-varying state and input delays [J]. Chaos Solitons and Fractals,2008,35(5):1003-1008.
99. Zhang X M, Wu M, She J H. Delay-dependent stabilization of linear systems with time-varying state and input delays [J]. Automatica,2005,41(8):1405-1412.
100. Chen W H, Zheng W X. Delay-dependent robust stabilization for uncertain neutral systems with distributed delays [J]. Automatica,2007,43:95-104.
101. Doyle J C, Glover K, Khargonekar P P, Francis B A. State space solution to standard H∞ and H∞ control problem [J]. IEEE Transaction on Automatica Control,1989,34(6):831-846.
102. Isidori A, Astofi A. Disturbence attenuation and H∞ control via measurement feedback in nonlinear systems [J]. IEEE Transaction on Automatica Control,1992,37:1283-1293.
103. Khargoneker P P, Petersen I R, Zhou K M. Robust stabilization of uncertain linear systems: quadratic stability and H∞ control theory [J]. IEEE Transaction on Automatica Control,1990, 29(1):1-10.
104. Chen B S, Tseng C S and Uang H J. Mixed H2/H∞ fuzzy output feedback control design for nonlinear dynamic systems:An LMI approach [J]. IEEE Transaction on Fuzzy Systems,2000, 8(2):249-265.
105. Chen B S, Tseng C S and Uang H J. Robustness design of nonlinear dynamic systems via fuzzy linear control [J]. IEEE Transaction on Fuzzy Systems,1999,7:571-585.
106. Zhang N, Feng G. H∞ Output feedback control design of fuzzy dynamic systems via LMI [J]. Acta Automatica Sinica,2001,27(4):495-509.
107. Lee K R, Jeung E T, Park H B. Robust fuzzy H∞ control for uncertain nonlinear systems via state feedback:an LMI approach [J]. Fuzzy Sets and Systems,2001,120:123-134.
108. Liu C H. An LMI-based stable T-S fuzzy model with paprametric uncertainties using mulitiple Lyapunov Function approach [C]. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems,2004,514-519.
109.刘晓东,张庆灵,王岩.基于LMI的T-S模糊系统的H∞控制[J].控制与决策,2002,17(6)：923-927.
110.王立新.自适应模糊系统与控制[M].北京：国防工业出版社,1995,1-200.
111.郑大钟.线性系统理论[M].北京：清华大学出版社,1999.
112. Tanaka K, Sugeno M. Stability analysis and design of fuzzy control systems [J]. Fuzzy Sets and Systems,1992,45:135-156.
113. Tanaka K, Ikede T, Wang H O. Robust stabilization of a class of uncertain nonlinear systems via fuzzy control:quadratic stabilizability,H ∞control theory, and linear matrix inequalities [J]. IEEE Transaction on Fuzzy Systems,1996,4(1):1-13.
114. Kim E, Lee H. New approaches to relaxed quadratic stability condition of fuzzy control systems [J]. IEEE Transaction on Fuzzy Systems,2002,8(5):523-533.
115. Liu X D, Zhang Q L. New approach H∞ controller designs based on fuzzy observer for T-S fuzzy systems via LMI [J]. Atuomatica,2003,39(5):1571-1582.
116. Fang C H, Liu Y S, Kau S W, Hong L, Lee C H. A new LMI-based approach to relaxed quadratic stabilization of T-S fuzzy control systems [J]. IEEE Transaction on Fuzzy Systems. 2006,14(3):386-397.
117. Chung H Y, Wu S M, Yu F M, Chang W J. Evolutionary design of static output feedback controller for Takagi-Sugeno fuzzy systems [J]. Control Theory and Applications,2007,1(4): 1096-1103.
118. Kau S W, Lee H J, Yang C M, et al. Robust H∞ fuzzy static output feedback control of T-S fuzzy systems with parametric uncertainties [J]. Fuzzy Sets and System,2007,158:135-156.
119.张艳,张庆灵,杨冬梅.基于LMI方法的T-S模糊系统动态输出反馈耗散控制器设计[J].控制与决策,2007,22(7)：760-764.
120. Saicedo J, Martinez M. BIBO stabilisation of Takagi-Sugeno fuzzy systems under persistent perturbations using fuzzy output-feedback controllers [J]. Control Theory and Applications, 2008,2(6):513-523.
121. Zhang K, Jiang B, Staroswiecki M. Dynamic output feedback-fault tolerant controller design for Takagi-Sugeno fuzzy systems with actuator faults [J]. IEEE Transaction on Fuzzy Systems, 2010,18(1):194-201.
122. Baser U, Kizilsac B. Dynamic output feedback H∞ control problem for linear neutral systems [J]. IEEE Transations on Automatic Control,2007,52(6):1113-1118.
123.刘国义,张庆灵,翟丁.T-S模糊系统H2/H∞混合控制器设计的LMI方法[J].控制与决策,2007,22(9)：1032-1034.
124. Tanaka K, Nishimura M, Wang H. Multi-objective fuzzy control of high rise/high speed elevators using LMIs [C]. Proceedings of American Control Conference Philadelphia, PA, 1998,6:3450-3454.
125. He L, Duan G. Multi-objective control synthesis for a class of uncertain fuzzy systems [C]. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, 2005,14:2563-2567.
126. Han Z X, Feng G, Walcott B L, Ma J. Dynamic output feedback controller H∞ design for fuzzy systems [J]. IEEE Transactions on Systems, Man and Cybernetics,2000,30(1):204-210.
127. Nguang S K, Shi P. Robust H∞ output feedback control design for fuzzy dynamic systems with quadratic D-stability constraints:an LMI approach [J]. Internal Journal Information Science, 2006,176:2161-2191.
128. Jafar R P, Vahid J M. A robust multi-objective DPDC for uncertain T-S fuzzy systems [J]. Fuzzy Sets and Systems,2008,159:2749-2762.
129. Tian EG, Peng C. Delay dependent stability analysis and synthesis of uncertain T-S fuzzy systems with time-varying delay [J]. Fuzzy Sets and Systems,2006,157:544-559.
130. He Y, Wang Q G, Lin C, Wu M. Augmented Lyapunov functional and delay-dependent stability criteria for neutral systems [J]. Internal Journal Robust Nonlinear Control,2005,15: 923-933.
131. Lin C, Wang Q G, Lee T H. A less conservative robust stability test for linear uncertain time-delay systems [J]. IEEE Transaction on Automat Control,2006,51(1):87-90.
132. Yoneyama J. New delay-dependent approach to robust stability and stabilization for Takagi-Sugeno fuzzy time-delay systems [J]. Fuzzy Sets and Systems,2007,158:2225-2337.
133. Peng C, Tian Y C. Delay-dependent robust stability criteria for uncertain systems with interval time-varying delay [J]. Journal Computational and Applied Mathematics,2008,214(2): 480-494.
134. Peng C, Tian Y C. State feedback controller design of networked control systems with interval time-varying delay and nonlinearity [J]. Internal Journal of Robust Nonlinear Control,2008.
135. Chen P, Tian Y C, Tian E. Improved delay-dependent robust stabilization conditions of uncertain T-S fuzzy systems with time-varying delay [J]. Fuzzy Sets and Systems,2008, 159(20):2713-2729.
136.陈学敏,张国山.具有输入与状态时滞的非线性系统的模糊保性能控制[J].辽宁工学院学报,2006,26(1)：34-39.
137.齐望东,薛卫娟,傅麒麟等译.William Stallings著,高速网络与互联网一性能与服务质量(第二版)[M].北京：电子工业出版社,2003.
138. Braden B, Clark D. Recommendations on queue management and congestion avoidance in the Internet [Z]. RFC 2309, April 1998.
139. Feng W, Kang G S, Dilip D K. The BLUE active queue management algorithms [J]. IEEE/ACM Transactions on Networking,2002,10(4):513-528.
140. Low S H, Lapsley D E. Optimization flow control I:basic algorithm and convergence [J]. IEEE/ACM Transactions on Networking,1999,7(6):861-875.
141. Luo H, Shyu M L, Chen S C. An optimal resource utilization scheme with end-to-end congestion control for continuous media stream transmission [J]. Computer Networks,2006, 50 (7):921-937.
142. Hollot C V, Misra V, Towsley D, et al. Analysis and design of controllers for AQM routers supporting TCP flows [J]. IEEE Transactions on Automatic Control,2002,47(6):945-959.
143. Wang C G, Li B, Sohraby K. API:Adaptive proportional-integral algorithm for active queue management under dynamic environments [C], Proceedings of IEEE Workshop on High Performance Switching and Routing (HPSR),2004:51-55.
144. Quet P F, Ozbay H. On the design of AQM supporting TCP flows using robust control theory [J]. IEEE Transactions on Automatic Control,2004,49(6):1031-1036.
145.王宏伟.TCP/IP网络拥塞控制中主动队列管理算法研究[D].东北大学博士学位论文,2008.
146. Ren F Y, Ren Y, Shan X M. Design of a fuzzy controller for active queue management [J]. Computer Communications,2002,25:874-883.
147. Loukas R, Kohler S, Andreas P, et al. Fuzzy RED:congestion control for TCP/IP Diff-Serv [J]. Proceedings of the 2000 Mediterranean Electrotechnical Conference,2000,1:19-22.
148. Qin K. Y, Xie J Y. A variable structure control in active queue management [C]. Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, China, 2004,643-647.
149.修智宏,王伟.T-S模糊系统输出反馈控制器的稳定性分析与设计[J].控制理论与应用,2006,23(4)：508-514.
150. Sugeno M, Kang G T. Structure identification of fuzzy model [J]. Fuzzy Sets and Systems, 1988,28(10):15-33.
151.吴忠强,许世范,岳东.非线性系统的T-S模糊建模与控制[J].系统仿真学报,2002,14(2)：253-256.
152. Jang L J, Xu Y S. A new method of sliding surface design for multivariable structure system [J]. IEEE Trans on Automatic Control,1994,39(2):414-419.
153. Lu Y S, Chen J S. Design of a global sliding-mode controller for a motor drive with bounded control [J]. International Journal of Control,1995,62(5):1001-1019.
154.单梁.混沌系统的若干同步方法研究.博士论文南京[D].南京理工大学博士论文,2006.
155. Zhang H G, Huang W, Wang Z L, Chai T Y. Adaptive synchronization between two different chaotic systems with unknown parameters [J]. Physics Letters A,2006,350(5-6):363-366.
156. Li W L, Chen X Q, Shen Z P. Anti-synchronization of two different chaotic systems [J]. Physica A,2008,387:3747-3750.
157. Breve F A., Zhao L, Quiles M G, Macau E E N. Chaotic phase synchronization and desynchronization in an oscillator network for object selection [J]. Neural Networks,2009, 22(5-6):728-737.
158. Vasegh N, Khellat F. Projective synchronization of chaotic time-delayed systems via sliding mode controller [J]. Chaos, Solitons & Fractals,2009,42(2):1054-1061.
159. Chen Y, Chen X X, Gu S S. Lag synchronization of structurally nonequivalent chaotic systems with time delays [J]. Nonlinear Analysis:Theory, Methods & Applications,2007,66(9): 1929-1937.
160. Zhang R, Xu Z Y, Yang S X, et al. Generalized synchronization via impulsive control [J]. Chaos, Solitons & Fractals,2008,38(1):97-105.
161. Wang F Q, Liu C X. A new criterion for chaos and hyperchaos synchronization using linear feedback control [J]. Physics Letters A,2006,360(2):274-278.
162. Lazzouni S A, Bowong S, Moukam K F M, Cherki B. An adaptive feedback control for chaos synchronization of nonlinear systems with different order [J]. Communications in Nonlinear Science and Numerical Simulation,2007,12(4):568-583.
163. Cao J D, Daniel W C H, Yang Y Q. Projective synchronization of a class of delayed chaotic systems via impulsive control [J]. Physics Letters A,2009,373(35):3128-3133.
164. Wang J, Gao J F, Ma X K. Synchronization control of cross-strict feedback hyperchaotic system based on cross active backstepping design [J]. Physics Letters A,2007,369(5-6): 452-457.
165. Mohammad S T, Mohammad H. Synchronization of chaotic fractional-order systems via active sliding mode controller [J]. Physica A:Statistical Mechanics and its Applications,2008, 387(1):57-70.
166. Liu B, Zhou Y M, Jiang M, Zhang Z K. Synchronizing chaotic systems using control based on tridiagonal structure [J]. Chaos, Solitons & Fractals,2009,39(5):2274-2281.
167. Tang F. An adaptive synchronization strategy based on active control for demodulating message hidden in chaotic signals [J]. Chaos, Solitons & Fractals,2008,37(4):1090-1096.
168. Santoboni G, Pogromsky Y A, Nijmeijer H. An observer for phase synchronization of chaos [J]. Physics Letters A,2001,291(4-5):265-273.
169. Yau H T, Shieh C S. Chaos synchronization using fuzzy logic controller [J]. Nonlinear Analysis:Real World Applications,2008,9(4):1800-1810.
170. Wu W, Chen T P. Global Synchronization Criteria of Linearly Coupled Neural Network Systems With Time-Varying Coupling [J]. IEEE Transactions on Neural Networks,2008, 19(2):319-332.
171. Zhao Y, Wang W. Chaos synchronization in a Josephson junction system via active sliding mode control [J]. Chaos, Solitons & Fractals,2009,41(1):60-66.
172. Kim J H, Hyun C H, Kim E, Park M. Adaptive synchronization of uncertain chaotic systems based on T-S fuzzy model [J]. IEEE Transactions on fuzzy systems,2007,15(3):359-369
173. Lam H K, Lakmal D. Seneviratne chaotic synchronization using sampled data fuzzy controller based on fuzzy model based approach [J]. IEEE Transactions on circuits and systems,2008, 55(3):883-892.
174. Yau H T, Chaos synchronization of two uncertain chaotic nonlinear gyros using fuzzy sliding mode control [J]. Mechanical Systems and Signal Processing,2008,22:408-418.
175. Lam H K, Ling W K, Herbert H C, Steve S H Synchronization of chaotic systems using time-delayed fuzzy state-feedback controller [J]. IEEE Transactions on circuits and systems, 2008,55(3):883-892.
176. Ahn C K. Fuzzy delayed output feedback synchronization for time-delayed chaotic systems [J]. Nonlinear Analysis:Hybrid Systems,2010,4:16-24.
177.王兴元,武相军.基于态观测器的一类混沌系统的反同步[J].物理学报,2007,56(4)：1988-1992.
178.孟娟,王兴元.基于模糊观测器的Chua(?)昆沌系统投影同步[J].物理学报,2009,58(2)：819-823.
2. Kaeear V. A bridge between state-space transfer-function method [J]. Annual Reviews in Contorl,1999,23:177-184.
3. 胡寿松.自动控制原理[M].北京：科学出版社,2001.
4. 刘艳军.非线性系统自适应模糊控制算法的研究[D].大连理工大学博士学位论文,2007.
5. 解学书.最优控制：理论与应用[M].北京：清华大学出版社,1986.
6. 舒迪前.预测控制系统及其应用[M].北京：机械工业出版社,1996.
7. 叶其华,王晨皓,吴捷.基于自组织模糊神经网络电力系统稳定器的设计[J].控制理论与应用,1999,15(5)：687-690.
8. 陈余庆.多移动机器人的协作运动控制研究[D].大连理工大学博士学位论文,2006.
9. 李清泉.自适应控制系统理论、设计与应用[M].北京：科学出版社,1990.
10.巩诚.T-S模糊时滞系统的鲁棒H∞控制及滤波[D].哈尔滨工业大学博士学位论文,2008.
11. Zadeh LA. Fuzzy sets [J]. Information and Control,1965,8:338-353.
12. Zadeh L A. Outline of a new approach to the analysis of complex systems and decision processes [J]. IEEE Transactions on Systems Man and Cybernetics,1973,3(1):28-44.
13. Wang L X. Adaptive fuzzy systems and control:design and stability analysis [M]. Englewood Cliffs, N J:Prentice-Hall,1994.
14.王立新.模糊系统与模糊控制教程[M].北京：清华大学出版社,2003.
15. Mamdani E H, Assilian S. An experiment in linguistic synthesis with a fuzzy logic controller [J]. International Journal of Man Machine Studies,1975,7(1):1-5.
16. Mamdani E H. Application of fuzzy algorithms for control of simple dynamic plant [J], IEE Proceedings Control and Science.1974,121(12):1585-1588.
17. Holmblad L P, Ostergaard J J. Control of cement kiln by fuzzy logic [J]. Fuzzy Information and Decision Proeesses, New York:North-Holland,1982.
18. Ysger R R. Ordered Weighted Averaging Aggregation Operator in Muti-criteria Decision Making [J]. IEEE Transactions on Systems Man and Cybernetics,1988,18:183-190.
19. Kiekert W, Mamdani E. Analysis of a fuzzy logic controller [J]. Fuzzy Sets and Systems,1978, 1:29-44.
20. Tong R. Analysis and control of fuzzy systems using the relation matrix [J]. International Journal of Control,1978,27:679-686.
21. Braae M R. Selection of parameters for a fuzzy logic controller [J]. Fuzzy Sets and Systems, 1979,2:185-199.
22邓聚龙.Fuzzy控制的稳定性问题[J].模据数学,1983,3：71-84.
23. Kiszka J, Gupta M, Nikiforuk P. Energetic stability of fuzzy dynamic systems [J]. IEEE Transactions on Systems, Man and Cybernetics,1985,15:783-792.
24.王士同.神经模糊系统及其应用[M].北京：北京航空航天大学出版社,1998.
25. Filev D P, Yager R R. A generalized defuzzifieation method via BAD distributions [J]. International Journal of Intelligent Systems,1991,6(4):687-697.
26.李丽.非线性模糊时滞系统的鲁棒控制研究[D].大连理工大学博士学位论文,2008.
27. Takagi T, Sugeno M. Fuzzy identifieation of systems and its applications to modeling and control [J]. IEEE Transactions on Systems, Man and Cysberneties,1985,15:116-132.
28. Chen C L, Chang M H. Optimal design of fuzzy sliding mode control:A comparative [J]. Fuzzy Sets Systems,1998,93:37-48.
29. Glower S, Munoghan J. Designing fuzzy controllers from a variable structures standpoint [J]. IEEE Transactions on Fuzzy Systems,1997,5(1):138-144.
30. Palm R. Robust control by fuzzy sliding mode [J]. Automatica,1994,30:1429-1437.
31. Chen J Y. Rule regulation of fuzzy sliding mode controller design:Direct adaptive approach [J]. Fuzzy Sets and Systems,2001,120:159-168.
32. Feng G. A survey on analysis and design of model-based fuzzy control systems [J]. IEEE Transactions on Fuzzy Systems,2006,14(5):676-697.
33. Takagi T, Sugeno M. Fuzzy identification of systems and its applications to modeling and control [J]. IEEE Transactions on Systems, Man and Cybernetics,1985,15(1):116-132.
34. Sugeno M, Kang G T. Structure identffication of fuzzy model [J]. Fuzzy Sets and Systems, 1988,28(10):15-33.
35. Feng G, Cao S G, Rees N W, et al. Design of fuzzy control systems with guaranteed stability [J]. IEEE Transactions on Systems, Man and Cybernetics,1997,85(1):1-10.
36. Cao S G, Rees N W, Feng G. Analysis and design for a class of complex control systems part II [J]. Automatica,1997,33(6):1017-1028.
37. Tanaka K, Ikeda T, Wang H O. Robust stabilization of a class of uncertain nonlinear systems via fuzzy control:quadratic stabilkizability, H∞ control theory, and linear matrix inequalities [J]. IEEE Transactions on Fuzzy Systems,1996,4(1):1-13.
38. Tanaka K, Ikeda T, Wang H O. Fuzzy regulator and fuzzy observers:relaxed stability conditions and LMI-based design [J]. IEEE Transactions on Fuzzy Systems,1998,6(2): 250-265.
39. Tanaka K, Wang H O. Fuzzy control systems design and analysis:A linear matrix inequality approach [M]. New York:John Wiley & Sons,2001.
40. Matia F, Aihadithi B, Jimenez A. Generalization of stability criterion for Takagi-Sugeno continuous fuzzy model [J]. Fuzzy Sets and Systems,2002,129(3):295-309.
41.巩长忠.基于T-S模糊模型的控制方法及稳定性分析[D].东北大学博士学位论文,2003.
42.王岩,张庆灵,孙增圻.离散模糊系统分析与设计的模糊Lyapunov方法[J].自动化学报,2004,30(2)：255-260.
43. Xiu Z H, Ren G. Stability analysis and systematic design of Takagi-Sugeno fuzzy control systems [J]. Fuzzy Sets and Systems,2005,151(1):119-138.
44.王利魁.离散Takagi-Sugeno模糊控制系统的稳定性研究[D].大连理工大学博士学位论文,2009.
45. Nguang S K, Shi P. H∞ fuzzy output feedback control desugn for nonlinear systems:An LMI approach [J]. IEEE Transactions on Fuzzy Systems,2003,11(3):331-340.
46. Teixeira M C M, Assuncao E, Avellar R G. On relaxed LMI-based design for fuzzy regulators and fuzzy observers [J]. IEEE Transactions on Fuzzy Systems,2003,11:613-623.
47. Zuo Z Q, Wang Y J. Robust stability criteria of uncertain fuzzy systems with time-varying delays [C]. IEEE International Conference on Systems, Man and Cybernetics,2005, 1303-1307.
48. Ban X J, Gao X Z, Huang X L, et al. Stability analysis of simplest Takagi-Sugeno fuzzy control system using circle criterion [J]. Information Sciences,2007,177(20):4387-4409.
49. Ding Y S, Ying H, Shao S H. Structure and stability analysis of a Takagi-Sugeno fuzzy PI controller with application to tissue hypermermia therapy [J]. Soft Computing.1999,2(4): 183-190.
50.张吉礼,欧进萍,于达仁.基于相平面轨迹特征的规则自调整模糊控制方法[J].控制理论与应用,2003,20(4)：607-611.
51. Abdelnour Q, Cheung J Y, Chnag C. Application of deseribing functions in the transient response analysis of a three-term fuzzy control [J]. IEEE Tnarsaetions on Sysetms, Man and Cybenreties,1993,23 (2):603-606.
52. Lo J C, Lin Y T. State feedback via circle criterion for systems subject to input saturations [C]. IEEE International Conference on Networking, Sensing and Control, Taiwan,2004,2:920-925.
53. Glower J S, Munighna J. Desinging fuzzy controllers from a variable structure standpoint [J]. IEEE Transactions on Fuzzy Systems,1997,5(1):138-144.
54. Htani Y O, Yoshirnura T. Fuzzy control of manipulator using the concept of sliding mode [J]. International Jounral of Sysetms Seience,1996,27(2):179-186.
55. Kim E, Lee H. New approaches to relaxed quadratic stability condition of fuzzy control systems [J]. IEEE Transactions on Fuzzy Systems,2000,8(5):523-534.
56. Liu X D, Zhang Q L.Approaches to quadratic stability conditions an H∞ control designs for T-S fuzzy systems [J]. IEEE Transactions on Fuzzy,2003,11(6):830-839.
57. Fang C H, Liu Y S, Kau S W, et al. A new LMI-based approach to relaxed quadratic stabilization of T-S fuzzy control systems [J]. IEEE Transactions on Fuzzy Systems,2006, 14(3):386-397.
58. Cao S G, Ress N W, Feng G. Analysis and design of fuzzy control systems using dynamic fuzzy-state space models [J]. IEEE Transactions on Fuzzy Systems,1999,7(2):192-200.
59. Johansson M, Rantzer A, Arzen K E. Piecewise quadratic stability of fuzzy systems [J]. IEEE Transactions on Fuzzy Systems,1999,7(6):713-722.
60. Tanaka K, Hori T, Wang H O. A fuzzy Lyapunov approach to fuzzy control system design [C]. Proceedings of the American Control Conference, Arlington, V A,2001,6:4790-4795.
61. Choi D, Park P. H∞ state feedback controller design for discrete time fuzzy systems using fuzzy weighting dependent Lyapunov functions [J]. IEEE Transactions on Fuzzy Systems, 2003,11(2):271-278.
62. Hong S K, Langari R. An LMI-based Fuzzy Control System Design with T-S Framework [J]. Information Sciences,2000,123:163-179.
63. Xie L. Output feedback H∞ control of systems with parameter uncertainty [J]. Internal Journal Control,1996,63(4):741-750.
64. Chen B S, Tseng C S, Uang H J. Mixed H2/H∞ fuzzy output feedback control design for nonlinear dynamic systems:An LMI approach [J]. IEEE Transactions on Fuzzy Systems,2000, 8(2):249-265.
65. Zhang N, Feng G. H∞, output feedback control design of fuzzy dynamic via LMI [J]. Acta Automatica Sinica,2001,27(4):495-509.
66. Lin C, Wang Q G, Lee T H. H∞ output tracking control for nonlinear systems via T-S fuzzy model approach [J]. IEEE Transactions on Systems, Man and Cybernetics, part B:Cybernetics, 2006,36(2):450-457.
67. Liu X D, Zhang Q L. New approaches to H∞ controller designs based on fuzzy observers for T-S fuzzy systems via LMI [J]. Automatica,2003,39:1571-1582.
68. Lin C, Wang Q G, Lee T H. Improvement on observer-based H∞ control for T-S fuzzy systems [J]. Automatica,2005,41:1651-1656.
69. Yoneyama J, Masahiro N, Hitoshi K, Akria I. Output stsbilization of Takagi-Sugeno fuzzy systems [J]. Fuzzy Sets and Systems,2000,111(2):142-151.
70. Cao S G, Rees N W, Feng G. H∞ contort of nonlinear discrete time systems based on dynamical fuzzy models [J]. International Journal of System Sciences,2000,31:229-241.
71. Cao S G, Rees N W, Feng G. H∞ control of nonlinear continuous time systems based on dynamical fuzzy models [J]. International Journal of System Science,1996,27:821-830.
72. Cao S G, Rees N W, Feng G. H∞ contorl of uncertain fuzzy continuous time systems [J]. Fuzzy Sets and Systems,2000,115:171-190.
73. Zhou S S, Lam J, Zheng W X. Control design for fuzzy systems based on relaxed nonquadratic stability and H∞ performance conditions [J]. IEEE Transactions on Fuzzy System,2007,15(2): 188-199.
74. Willems J C. Least squares stationary optimal control and algebraic Riccati equation [J]. IEEE Trnasactions on Automatie Control,1971,16(1):621-634.
75. Neestorv Y, Nemiorvskii A. A general approach to polynomial-time algorithms design for convex programming [C]. Technical report, Cent. Econ. & Math. Inst., USSR Acad. Sci., Moscow, USSR,1998.
76. Boyd S, Ghaoui E, Feorn E, Balakrishnna V. Linear matrix inequality in systems and conrtol theory [M]. Philadelphia, PA:SIAM,1994.
77. Gahinet P, Nemirovaki A, Laub A J, Alahmoud C. LMI Control Toolbox [M]. The Math Works, Inc.1995,1-200.
78.俞立.鲁棒控制—线性矩阵不等式处理方法[M].北京：清华大学出版社,2002.
79. Gahinet P, Nemirovski A, Laub A J, et al. LMI Control toolbox [M]. The Math Works Inc, Natick,1995.
80.李泽滔.采用MATLABLMI工具进行控制系统鲁棒分析[J].机械与电子,2000,6：3942.
81. Callender A, Haritree D R, Porter A. Time lag in a control system [J]. Philosophical Transactions of the Royal Society of London,1996,235:415-444.
82. Cao Y Y, Frank P M. Analysis and synthesis of nonlinear time-delay systems via fuzzy control approach [J]. IEEE Trnasaetions on Fuzzy System,2000,8(2):200-211.
83. Cao Y Y, Frank P M. Stability analysis and synthesis of nonlinear time-delay systems via linear Takagi-Sugeno fuzzy models [J]. Fuzzy Sets and Systems,2001,124(2):213-229.
84. Chen B, Liu X P, Lin Chong, Liu K. Robust H∞ control of Takagi-Sugeno fuzzy systems with state and input time delays [J]. Fuzzy Sets and Systems,2009,160(4):403-422.
85. Zhang B Y, Lam J, Xu S Y, Shu Z, Robust stabilization of uncertain T-S fuzzy time-delay systems with exponential estimates [J]. Fuzzy Sets and Systems,2009,160(12):1720-1737.
86. Yu K W, Lien C H. Robust H control of uncertain T-S fuzzy systems with state and input time delays [J]. Chaos Solions & Fractals,2008,37:150-156.
87. Guan X P, Chen C L. Delay-dependent guaranteed cost control for T-S fuzzy systems with time delays [J]. IEEE Transaction on Fuzzy Systems,2004,12:236-249.
88. Wang R J, Lin W, Wang W J. Stabilizability of linear quadratic state feedback for uncertain fuzzy time-delay systems [J]. IEEE Transactions on Systems, Man and Cybernetics,2004, 34(2):1288-1292.
89. Chiang T S, Chiu C S, Liu P. Output regulation for discrete-time nonlinear time-varying delay system:An LMI Approach [C]. IEEE International Conference on fuzzy Systems,2004,3: 1269-1273.
90.韩安太,王树青.基于LMI的一类非线性时滞关联大系统的分散模糊控制[J].控制与决策,2004,19(4)：416-419.
91. Guna X P, Chen C L. Delay-dependent guanarteed cost control T-S fuzzy systems with time delays [J]. IEEE Transactions on Fuzzy Systems,2004,12(2):236-249.
92. Chen B, Liu X P, Tong S C. Delay-dependent stability analysis and control synthesis of fuzzy dynamic systems with time delay [J]. Fuzzy Sets and Systems,2006,157(16):2224-2240.
93. Chen B, Liu X P, Tong S C. New delay-dependent stabilization conditions of T-S fuzzy systems with constant delay [J]. Fuzzy Sets and Systems,2007,158(20):2209-2224.
94. Tian E G, Peng C. Delay-dependent stability analysis and synthesis of uncertain T-S fuzzy systems with time-varying delay [J]. Fuzzy Sets and Systems,2006,157:544-559.
95. Jiang X F, Han Q L. Robust H∞ control for uncertain Takagi-Sugeno fuzzy systems with interval time-varying delay [J]. IEEE Transaction on Fuzzy Systems,2007,15(2):321-331.
96. Wu H N, Li H X. New approach to delay-dependedt stability analysis and stabilization for continuous-time fuzzy systems with time-varying delay [J]. IEEE Transaction on Fuzzy Systems,2007,15(3):482-493.
97. Chen B, Liu X P, Tong S C. Robust fuzzy control of nonlinear systems with input delay [J]. Chaos Solitons and Fractals,2008,37(3):894-901.
98. Lien C H, Yu K W. Robust control for Takagi-Sugeno fuzzy systems with time-varying state and input delays [J]. Chaos Solitons and Fractals,2008,35(5):1003-1008.
99. Zhang X M, Wu M, She J H. Delay-dependent stabilization of linear systems with time-varying state and input delays [J]. Automatica,2005,41(8):1405-1412.
100. Chen W H, Zheng W X. Delay-dependent robust stabilization for uncertain neutral systems with distributed delays [J]. Automatica,2007,43:95-104.
101. Doyle J C, Glover K, Khargonekar P P, Francis B A. State space solution to standard H∞ and H∞ control problem [J]. IEEE Transaction on Automatica Control,1989,34(6):831-846.
102. Isidori A, Astofi A. Disturbence attenuation and H∞ control via measurement feedback in nonlinear systems [J]. IEEE Transaction on Automatica Control,1992,37:1283-1293.
103. Khargoneker P P, Petersen I R, Zhou K M. Robust stabilization of uncertain linear systems: quadratic stability and H∞ control theory [J]. IEEE Transaction on Automatica Control,1990, 29(1):1-10.
104. Chen B S, Tseng C S and Uang H J. Mixed H2/H∞ fuzzy output feedback control design for nonlinear dynamic systems:An LMI approach [J]. IEEE Transaction on Fuzzy Systems,2000, 8(2):249-265.
105. Chen B S, Tseng C S and Uang H J. Robustness design of nonlinear dynamic systems via fuzzy linear control [J]. IEEE Transaction on Fuzzy Systems,1999,7:571-585.
106. Zhang N, Feng G. H∞ Output feedback control design of fuzzy dynamic systems via LMI [J]. Acta Automatica Sinica,2001,27(4):495-509.
107. Lee K R, Jeung E T, Park H B. Robust fuzzy H∞ control for uncertain nonlinear systems via state feedback:an LMI approach [J]. Fuzzy Sets and Systems,2001,120:123-134.
108. Liu C H. An LMI-based stable T-S fuzzy model with paprametric uncertainties using mulitiple Lyapunov Function approach [C]. Proceedings of IEEE Conference on Cybernetics and Intelligent Systems,2004,514-519.
109.刘晓东,张庆灵,王岩.基于LMI的T-S模糊系统的H∞控制[J].控制与决策,2002,17(6)：923-927.
110.王立新.自适应模糊系统与控制[M].北京：国防工业出版社,1995,1-200.
111.郑大钟.线性系统理论[M].北京：清华大学出版社,1999.
112. Tanaka K, Sugeno M. Stability analysis and design of fuzzy control systems [J]. Fuzzy Sets and Systems,1992,45:135-156.
113. Tanaka K, Ikede T, Wang H O. Robust stabilization of a class of uncertain nonlinear systems via fuzzy control:quadratic stabilizability,H ∞control theory, and linear matrix inequalities [J]. IEEE Transaction on Fuzzy Systems,1996,4(1):1-13.
114. Kim E, Lee H. New approaches to relaxed quadratic stability condition of fuzzy control systems [J]. IEEE Transaction on Fuzzy Systems,2002,8(5):523-533.
115. Liu X D, Zhang Q L. New approach H∞ controller designs based on fuzzy observer for T-S fuzzy systems via LMI [J]. Atuomatica,2003,39(5):1571-1582.
116. Fang C H, Liu Y S, Kau S W, Hong L, Lee C H. A new LMI-based approach to relaxed quadratic stabilization of T-S fuzzy control systems [J]. IEEE Transaction on Fuzzy Systems. 2006,14(3):386-397.
117. Chung H Y, Wu S M, Yu F M, Chang W J. Evolutionary design of static output feedback controller for Takagi-Sugeno fuzzy systems [J]. Control Theory and Applications,2007,1(4): 1096-1103.
118. Kau S W, Lee H J, Yang C M, et al. Robust H∞ fuzzy static output feedback control of T-S fuzzy systems with parametric uncertainties [J]. Fuzzy Sets and System,2007,158:135-156.
119.张艳,张庆灵,杨冬梅.基于LMI方法的T-S模糊系统动态输出反馈耗散控制器设计[J].控制与决策,2007,22(7)：760-764.
120. Saicedo J, Martinez M. BIBO stabilisation of Takagi-Sugeno fuzzy systems under persistent perturbations using fuzzy output-feedback controllers [J]. Control Theory and Applications, 2008,2(6):513-523.
121. Zhang K, Jiang B, Staroswiecki M. Dynamic output feedback-fault tolerant controller design for Takagi-Sugeno fuzzy systems with actuator faults [J]. IEEE Transaction on Fuzzy Systems, 2010,18(1):194-201.
122. Baser U, Kizilsac B. Dynamic output feedback H∞ control problem for linear neutral systems [J]. IEEE Transations on Automatic Control,2007,52(6):1113-1118.
123.刘国义,张庆灵,翟丁.T-S模糊系统H2/H∞混合控制器设计的LMI方法[J].控制与决策,2007,22(9)：1032-1034.
124. Tanaka K, Nishimura M, Wang H. Multi-objective fuzzy control of high rise/high speed elevators using LMIs [C]. Proceedings of American Control Conference Philadelphia, PA, 1998,6:3450-3454.
125. He L, Duan G. Multi-objective control synthesis for a class of uncertain fuzzy systems [C]. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, 2005,14:2563-2567.
126. Han Z X, Feng G, Walcott B L, Ma J. Dynamic output feedback controller H∞ design for fuzzy systems [J]. IEEE Transactions on Systems, Man and Cybernetics,2000,30(1):204-210.
127. Nguang S K, Shi P. Robust H∞ output feedback control design for fuzzy dynamic systems with quadratic D-stability constraints:an LMI approach [J]. Internal Journal Information Science, 2006,176:2161-2191.
128. Jafar R P, Vahid J M. A robust multi-objective DPDC for uncertain T-S fuzzy systems [J]. Fuzzy Sets and Systems,2008,159:2749-2762.
129. Tian EG, Peng C. Delay dependent stability analysis and synthesis of uncertain T-S fuzzy systems with time-varying delay [J]. Fuzzy Sets and Systems,2006,157:544-559.
130. He Y, Wang Q G, Lin C, Wu M. Augmented Lyapunov functional and delay-dependent stability criteria for neutral systems [J]. Internal Journal Robust Nonlinear Control,2005,15: 923-933.
131. Lin C, Wang Q G, Lee T H. A less conservative robust stability test for linear uncertain time-delay systems [J]. IEEE Transaction on Automat Control,2006,51(1):87-90.
132. Yoneyama J. New delay-dependent approach to robust stability and stabilization for Takagi-Sugeno fuzzy time-delay systems [J]. Fuzzy Sets and Systems,2007,158:2225-2337.
133. Peng C, Tian Y C. Delay-dependent robust stability criteria for uncertain systems with interval time-varying delay [J]. Journal Computational and Applied Mathematics,2008,214(2): 480-494.
134. Peng C, Tian Y C. State feedback controller design of networked control systems with interval time-varying delay and nonlinearity [J]. Internal Journal of Robust Nonlinear Control,2008.
135. Chen P, Tian Y C, Tian E. Improved delay-dependent robust stabilization conditions of uncertain T-S fuzzy systems with time-varying delay [J]. Fuzzy Sets and Systems,2008, 159(20):2713-2729.
136.陈学敏,张国山.具有输入与状态时滞的非线性系统的模糊保性能控制[J].辽宁工学院学报,2006,26(1)：34-39.
137.齐望东,薛卫娟,傅麒麟等译.William Stallings著,高速网络与互联网一性能与服务质量(第二版)[M].北京：电子工业出版社,2003.
138. Braden B, Clark D. Recommendations on queue management and congestion avoidance in the Internet [Z]. RFC 2309, April 1998.
139. Feng W, Kang G S, Dilip D K. The BLUE active queue management algorithms [J]. IEEE/ACM Transactions on Networking,2002,10(4):513-528.
140. Low S H, Lapsley D E. Optimization flow control I:basic algorithm and convergence [J]. IEEE/ACM Transactions on Networking,1999,7(6):861-875.
141. Luo H, Shyu M L, Chen S C. An optimal resource utilization scheme with end-to-end congestion control for continuous media stream transmission [J]. Computer Networks,2006, 50 (7):921-937.
142. Hollot C V, Misra V, Towsley D, et al. Analysis and design of controllers for AQM routers supporting TCP flows [J]. IEEE Transactions on Automatic Control,2002,47(6):945-959.
143. Wang C G, Li B, Sohraby K. API:Adaptive proportional-integral algorithm for active queue management under dynamic environments [C], Proceedings of IEEE Workshop on High Performance Switching and Routing (HPSR),2004:51-55.
144. Quet P F, Ozbay H. On the design of AQM supporting TCP flows using robust control theory [J]. IEEE Transactions on Automatic Control,2004,49(6):1031-1036.
145.王宏伟.TCP/IP网络拥塞控制中主动队列管理算法研究[D].东北大学博士学位论文,2008.
146. Ren F Y, Ren Y, Shan X M. Design of a fuzzy controller for active queue management [J]. Computer Communications,2002,25:874-883.
147. Loukas R, Kohler S, Andreas P, et al. Fuzzy RED:congestion control for TCP/IP Diff-Serv [J]. Proceedings of the 2000 Mediterranean Electrotechnical Conference,2000,1:19-22.
148. Qin K. Y, Xie J Y. A variable structure control in active queue management [C]. Proceedings of the Third International Conference on Machine Learning and Cybernetics, Shanghai, China, 2004,643-647.
149.修智宏,王伟.T-S模糊系统输出反馈控制器的稳定性分析与设计[J].控制理论与应用,2006,23(4)：508-514.
150. Sugeno M, Kang G T. Structure identification of fuzzy model [J]. Fuzzy Sets and Systems, 1988,28(10):15-33.
151.吴忠强,许世范,岳东.非线性系统的T-S模糊建模与控制[J].系统仿真学报,2002,14(2)：253-256.
152. Jang L J, Xu Y S. A new method of sliding surface design for multivariable structure system [J]. IEEE Trans on Automatic Control,1994,39(2):414-419.
153. Lu Y S, Chen J S. Design of a global sliding-mode controller for a motor drive with bounded control [J]. International Journal of Control,1995,62(5):1001-1019.
154.单梁.混沌系统的若干同步方法研究.博士论文南京[D].南京理工大学博士论文,2006.
155. Zhang H G, Huang W, Wang Z L, Chai T Y. Adaptive synchronization between two different chaotic systems with unknown parameters [J]. Physics Letters A,2006,350(5-6):363-366.
156. Li W L, Chen X Q, Shen Z P. Anti-synchronization of two different chaotic systems [J]. Physica A,2008,387:3747-3750.
157. Breve F A., Zhao L, Quiles M G, Macau E E N. Chaotic phase synchronization and desynchronization in an oscillator network for object selection [J]. Neural Networks,2009, 22(5-6):728-737.
158. Vasegh N, Khellat F. Projective synchronization of chaotic time-delayed systems via sliding mode controller [J]. Chaos, Solitons & Fractals,2009,42(2):1054-1061.
159. Chen Y, Chen X X, Gu S S. Lag synchronization of structurally nonequivalent chaotic systems with time delays [J]. Nonlinear Analysis:Theory, Methods & Applications,2007,66(9): 1929-1937.
160. Zhang R, Xu Z Y, Yang S X, et al. Generalized synchronization via impulsive control [J]. Chaos, Solitons & Fractals,2008,38(1):97-105.
161. Wang F Q, Liu C X. A new criterion for chaos and hyperchaos synchronization using linear feedback control [J]. Physics Letters A,2006,360(2):274-278.
162. Lazzouni S A, Bowong S, Moukam K F M, Cherki B. An adaptive feedback control for chaos synchronization of nonlinear systems with different order [J]. Communications in Nonlinear Science and Numerical Simulation,2007,12(4):568-583.
163. Cao J D, Daniel W C H, Yang Y Q. Projective synchronization of a class of delayed chaotic systems via impulsive control [J]. Physics Letters A,2009,373(35):3128-3133.
164. Wang J, Gao J F, Ma X K. Synchronization control of cross-strict feedback hyperchaotic system based on cross active backstepping design [J]. Physics Letters A,2007,369(5-6): 452-457.
165. Mohammad S T, Mohammad H. Synchronization of chaotic fractional-order systems via active sliding mode controller [J]. Physica A:Statistical Mechanics and its Applications,2008, 387(1):57-70.
166. Liu B, Zhou Y M, Jiang M, Zhang Z K. Synchronizing chaotic systems using control based on tridiagonal structure [J]. Chaos, Solitons & Fractals,2009,39(5):2274-2281.
167. Tang F. An adaptive synchronization strategy based on active control for demodulating message hidden in chaotic signals [J]. Chaos, Solitons & Fractals,2008,37(4):1090-1096.
168. Santoboni G, Pogromsky Y A, Nijmeijer H. An observer for phase synchronization of chaos [J]. Physics Letters A,2001,291(4-5):265-273.
169. Yau H T, Shieh C S. Chaos synchronization using fuzzy logic controller [J]. Nonlinear Analysis:Real World Applications,2008,9(4):1800-1810.
170. Wu W, Chen T P. Global Synchronization Criteria of Linearly Coupled Neural Network Systems With Time-Varying Coupling [J]. IEEE Transactions on Neural Networks,2008, 19(2):319-332.
171. Zhao Y, Wang W. Chaos synchronization in a Josephson junction system via active sliding mode control [J]. Chaos, Solitons & Fractals,2009,41(1):60-66.
172. Kim J H, Hyun C H, Kim E, Park M. Adaptive synchronization of uncertain chaotic systems based on T-S fuzzy model [J]. IEEE Transactions on fuzzy systems,2007,15(3):359-369
173. Lam H K, Lakmal D. Seneviratne chaotic synchronization using sampled data fuzzy controller based on fuzzy model based approach [J]. IEEE Transactions on circuits and systems,2008, 55(3):883-892.
174. Yau H T, Chaos synchronization of two uncertain chaotic nonlinear gyros using fuzzy sliding mode control [J]. Mechanical Systems and Signal Processing,2008,22:408-418.
175. Lam H K, Ling W K, Herbert H C, Steve S H Synchronization of chaotic systems using time-delayed fuzzy state-feedback controller [J]. IEEE Transactions on circuits and systems, 2008,55(3):883-892.
176. Ahn C K. Fuzzy delayed output feedback synchronization for time-delayed chaotic systems [J]. Nonlinear Analysis:Hybrid Systems,2010,4:16-24.
177.王兴元,武相军.基于态观测器的一类混沌系统的反同步[J].物理学报,2007,56(4)：1988-1992.
178.孟娟,王兴元.基于模糊观测器的Chua(?)昆沌系统投影同步[J].物理学报,2009,58(2)：819-823.
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