时延网络化控制系统的控制与稳定性研究
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摘要
随着计算机技术与网络通信技术的高速发展,网络化控制系统的研究得到了广泛的关注。它具有信息资源共享,接线减少和易于扩展和维护等优点。同时网络的引入给控制系统带来了网络诱导时延和数据包丢失两大主要问题,因而原有的控制理论不能直接应用于网络化控制系统中。本文对非线性时延网络化控制系统、基于模型的时延网络化控制系统及基于变采样理论的时延网络化控制系统进行了建模、控制器设计及稳定性等方面的研究。
本文将模糊控制理论与线性网络化随机控制理论相结合,解决了非线性网络化控制系统的建模、控制器设计及稳定性分析问题。运用T-S模糊理论,将一个整体非线性系统精确地表示为多个局部线性模型的模糊逼近。在设计线性子系统的控制器时,首先在已知网络时延分布概率特性的条件下,求取状态转移矩阵;然后采用动态规划和线性随机最优理论设计出线性子系统的控制器并进行了稳定性分析。仿真结果表明用本文提出的控制方案来设计非线性网络化控制系统控制器是可行而且有效的。
结合Delta域离散理论,本文对仿射非线性网络化控制系统进行了研究。首先运用泰勒级数展开法,将仿射非线性被控对象表示为多个局部线性模型的模糊逼近;然后将Delta算子引入到网络化控制系统中,运用动态规划理论和随机最优理论设计出Delta域随机最优网络控制器和Delta域随机最优跟踪器,并进行了稳定性分析。仿真结果表明所设计出的Delta域无限时间随机最优控制器是有效的。
为了减少网络对控制系统的影响,本文采用了仅在传感器到控制器之间存在网络的网络化控制系统结构,研究了基于模型的网络化控制系统的确定性控制器的设计和稳定性分析问题。对于基于模型的网络化控制系统的结构,本文进行了两处改进:第一,将观测器放在控制器节点侧,用数字观测器来有代替传统的观测器,大大地节约了成本;第二,在传感器信号到达控制器前增加了信号处理单元,增大了系统所能容忍的最大采样周期值。基于模型的网络化控制系统的设计思想是:先根据被控对象的连续模型及所期望的极点配置值,在不考虑网络影响的情况下,求出连续控制器的增益矩阵;然后用此控制器来控制系统,在考虑网络影响的情况下,求出保证系统稳定所能容忍的采样周期值。本文在基于模型的无时延和时延网络化控制系统新结构模型的基础上,分别提出了保证系统稳定的充要条件和保证Lyapunov稳定的充分条件,给出了保证系统稳定的采样周期的变化区间的求取方法。
采用事件-时间驱动方式,本文研究了基于变采样理论的网络化控制系统的控制器设计和稳定性分析问题。首先,研究了连续系统所容许的最大采样周期的选取原则;然后在变采样离散化模型的基础上,提出了保证系统稳定的充要条件,进而给出了求取控制增益的步骤;接着研究了带观测器的变采样离散化模型,提出了系统控制器和观测器的设计方法;最后基于变采样理论对时延网络化控制系统进行了控制器的设计,并在基于Lon技术的网络化实验平台上进行了实验,实验结果表明所设计的控制器能保证网络化控制系统是稳定的。
The networked control system is paid more attention to by scholars with the rapid development of computer technology and network communication technology. It has many advantages such as sharing information resources, reducing wire and extending and maintaining easily. On the other hand, the two main problems, the network induced time delay and data packet dropout, are brought into the control system. Therefore, it is impossible that traditional control theories are applied to networked control systems. The problems of modeling and controlling for the nonlinear networked control system, the model-based networked control system and the variable-sampling networked control system are studied in the paper.
In the paper, the problems of modeling, controller designing and stability analyzing for the nonlinear networked control system are settled by combining the fuzzy control theory with the linear networked stochastic control theory. A nonlinear system is exactly described as several local linear fuzzy models by using T-S fuzzy theory. When designing controller for the linear subsystem, firstly, the state transition matrix is derived on condition that distributing probability of the network-induced delay is known; Afterwards, the stochastic optimal controller for the linear subsystem is designed and stability is analyzed adopting dynamic programming and linear stochastic optimal theory. The simulation results show that the control scheme for the nonlinear networked control system presented in the paper is feasible and effective.
The affine nonlinear network control system is further studied using the Delta domain discrete theory. Firstly, the nonlinear plant is described as several local linear fuzzy models by using the method of Taylor series expansion. Afterwards, Delta operator is introduced into the networked control system, the stochastic optimal controller and the tractor for the linear subsystem are designed in Delta domain, and stability is analyzed by using dynamic programming and linear stochastic optimal theory. The simulation results show that infinite-time stochastic optimal controller in Delta domain designed in the paper is effective.
In order to lessen the influence of network on control systems, the structure for the NCSs is network connecting only between the sensor and the controller. The certain controller for the model-based networked control systems is designed and stability is analyzed. Two innovations for the structure of model-based networked control systems are done in the paper. Firstly, the observer is laid on the side of controller node, and digital observer instead of traditional observer can save the cost effectively. Secondly, the signal processing unit is added before the signal from the sensor reaches the controller, thus the tolerance value of the maximal sample period of the system is greatly increased. The model-based networked control system design is designed as follows. Firstly, the gain matrix of continuous controller is obtained according to continuous model of the plant and the expected pole values without considering the influence of the network. Then, by using the controller to control the system, considering the influence of network, the tolerated maximal sampling period is obtained. The necessary and sufficient conditions for ensuring the system’s stability and sufficient conditions for ensuring Lyapunov stability are both put forward for the model-based networked control system based on new structure with time delay and without time delay, and the change zone of the sampling period for ensuring the system’s stability is given.
By adopting event-time drive method, the controller design and stability analysis for the networked control system based on variable-sampling theory is explored in the paper. Above of all, the selecting principle for the continuous system’s tolerance maximum sampling period is studyed. According to variable-sampling discrete model, the necessary and sufficient condition for ensuring the system’s stability is putted forward, and the steps for control gain is given. Then, the variable-sampling discrete model with observer is studied, and design method for the controller and the observer are given. Finally, the controller for the time delay networked control system is designed based on variable-sampling theory, and the experiment is conducted on the networked platform based on the Lon technology. The results of the experiment prove that the designed controller can ensure the networked control system’s stability.
本文将模糊控制理论与线性网络化随机控制理论相结合,解决了非线性网络化控制系统的建模、控制器设计及稳定性分析问题。运用T-S模糊理论,将一个整体非线性系统精确地表示为多个局部线性模型的模糊逼近。在设计线性子系统的控制器时,首先在已知网络时延分布概率特性的条件下,求取状态转移矩阵;然后采用动态规划和线性随机最优理论设计出线性子系统的控制器并进行了稳定性分析。仿真结果表明用本文提出的控制方案来设计非线性网络化控制系统控制器是可行而且有效的。
结合Delta域离散理论,本文对仿射非线性网络化控制系统进行了研究。首先运用泰勒级数展开法,将仿射非线性被控对象表示为多个局部线性模型的模糊逼近;然后将Delta算子引入到网络化控制系统中,运用动态规划理论和随机最优理论设计出Delta域随机最优网络控制器和Delta域随机最优跟踪器,并进行了稳定性分析。仿真结果表明所设计出的Delta域无限时间随机最优控制器是有效的。
为了减少网络对控制系统的影响,本文采用了仅在传感器到控制器之间存在网络的网络化控制系统结构,研究了基于模型的网络化控制系统的确定性控制器的设计和稳定性分析问题。对于基于模型的网络化控制系统的结构,本文进行了两处改进:第一,将观测器放在控制器节点侧,用数字观测器来有代替传统的观测器,大大地节约了成本;第二,在传感器信号到达控制器前增加了信号处理单元,增大了系统所能容忍的最大采样周期值。基于模型的网络化控制系统的设计思想是:先根据被控对象的连续模型及所期望的极点配置值,在不考虑网络影响的情况下,求出连续控制器的增益矩阵;然后用此控制器来控制系统,在考虑网络影响的情况下,求出保证系统稳定所能容忍的采样周期值。本文在基于模型的无时延和时延网络化控制系统新结构模型的基础上,分别提出了保证系统稳定的充要条件和保证Lyapunov稳定的充分条件,给出了保证系统稳定的采样周期的变化区间的求取方法。
采用事件-时间驱动方式,本文研究了基于变采样理论的网络化控制系统的控制器设计和稳定性分析问题。首先,研究了连续系统所容许的最大采样周期的选取原则;然后在变采样离散化模型的基础上,提出了保证系统稳定的充要条件,进而给出了求取控制增益的步骤;接着研究了带观测器的变采样离散化模型,提出了系统控制器和观测器的设计方法;最后基于变采样理论对时延网络化控制系统进行了控制器的设计,并在基于Lon技术的网络化实验平台上进行了实验,实验结果表明所设计的控制器能保证网络化控制系统是稳定的。
The networked control system is paid more attention to by scholars with the rapid development of computer technology and network communication technology. It has many advantages such as sharing information resources, reducing wire and extending and maintaining easily. On the other hand, the two main problems, the network induced time delay and data packet dropout, are brought into the control system. Therefore, it is impossible that traditional control theories are applied to networked control systems. The problems of modeling and controlling for the nonlinear networked control system, the model-based networked control system and the variable-sampling networked control system are studied in the paper.
In the paper, the problems of modeling, controller designing and stability analyzing for the nonlinear networked control system are settled by combining the fuzzy control theory with the linear networked stochastic control theory. A nonlinear system is exactly described as several local linear fuzzy models by using T-S fuzzy theory. When designing controller for the linear subsystem, firstly, the state transition matrix is derived on condition that distributing probability of the network-induced delay is known; Afterwards, the stochastic optimal controller for the linear subsystem is designed and stability is analyzed adopting dynamic programming and linear stochastic optimal theory. The simulation results show that the control scheme for the nonlinear networked control system presented in the paper is feasible and effective.
The affine nonlinear network control system is further studied using the Delta domain discrete theory. Firstly, the nonlinear plant is described as several local linear fuzzy models by using the method of Taylor series expansion. Afterwards, Delta operator is introduced into the networked control system, the stochastic optimal controller and the tractor for the linear subsystem are designed in Delta domain, and stability is analyzed by using dynamic programming and linear stochastic optimal theory. The simulation results show that infinite-time stochastic optimal controller in Delta domain designed in the paper is effective.
In order to lessen the influence of network on control systems, the structure for the NCSs is network connecting only between the sensor and the controller. The certain controller for the model-based networked control systems is designed and stability is analyzed. Two innovations for the structure of model-based networked control systems are done in the paper. Firstly, the observer is laid on the side of controller node, and digital observer instead of traditional observer can save the cost effectively. Secondly, the signal processing unit is added before the signal from the sensor reaches the controller, thus the tolerance value of the maximal sample period of the system is greatly increased. The model-based networked control system design is designed as follows. Firstly, the gain matrix of continuous controller is obtained according to continuous model of the plant and the expected pole values without considering the influence of the network. Then, by using the controller to control the system, considering the influence of network, the tolerated maximal sampling period is obtained. The necessary and sufficient conditions for ensuring the system’s stability and sufficient conditions for ensuring Lyapunov stability are both put forward for the model-based networked control system based on new structure with time delay and without time delay, and the change zone of the sampling period for ensuring the system’s stability is given.
By adopting event-time drive method, the controller design and stability analysis for the networked control system based on variable-sampling theory is explored in the paper. Above of all, the selecting principle for the continuous system’s tolerance maximum sampling period is studyed. According to variable-sampling discrete model, the necessary and sufficient condition for ensuring the system’s stability is putted forward, and the steps for control gain is given. Then, the variable-sampling discrete model with observer is studied, and design method for the controller and the observer are given. Finally, the controller for the time delay networked control system is designed based on variable-sampling theory, and the experiment is conducted on the networked platform based on the Lon technology. The results of the experiment prove that the designed controller can ensure the networked control system’s stability.
引文
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69 L. A. Montestruque, P. J. Antsaklis. On the Model-based Control of Networked System. Automatica, 2003,39(10): 1837-1843
70 L. A. Montestruque, P. J. Antsaklis. Stability of Model-based Networked Control Systems with Time-varying Transmission Times. IEEE Transaction. On Automatic Control, 2004,49(9): 1526-1572
71 Luis A. Montestruque, Panos J.Antsaklis. Quantization In Model Based Networked Control Systems. IFAC, 2005
72汤贤铭,俞金寿.基于模型的输出反馈网络控制系统反馈调度研究.控制与决策, 2009, 24(1): 141-144
73孙海燕,侯朝桢,赵奇.基于模型的网络控制系统稳定性.控制与决策, 2005,20(4): 412-416
74 S. Mastellone, C. T. Abdallah, P. Dorato. Model-based Networked Control for Nonlinear Systems with Stochastic Packet Dropout. American Control Conference, Portland, OR, USA. 2005
75 P. V. Zhivoglyadov, R. H. Middleton. Networked Control Design for Linear Systems. Automatica, 2003,39(4): 743-750
76黄杰,吴平东,修震,马树元,陈之龙.基于因特网的运动控制系统中变采样过程的研究.控制与决策, 2005,20(7): 755-763
77薛燕,刘克.变采样网络控制系统的稳定性分析.清华大学学报, 2007,47(S2): 1919-1923
78薛燕,刘克.基于预测值控制的变采样网络控制系统.控制理论与应用, 2009,26(6): 657-660
79 Gao Huijun, Wu Junli, Shi Peng. Robust Sampled-data H∞Control with Stochastic Sampling. Automatica, 2009,45(7): 1729-1736
80 Liu Fuchun, Yao Yu, He Fenghua, Chen Songlin. Stability Analysis of Networked Control Systems with Time-varying Sampling Periods. Journal of Control Theory and Applications, 2008,6(1): 22-25
81 Li Xuan, Wu Xiaobei. Guaranteed Cost Fault-tolerant Controller Design of Networked Control Systems under Variable-period Sampling. Information Technology Journal, 2009,8(4): 537-543
82 Wang Yan, Sun Zengqi. Stochastic Stabilization of Networked Control Systems with Time-varying Sampling Periods. 11th IEEE International Conference on Communication Technology Proceedings, ICCT 2008: 541-545
83 G. C. Walsh, O. Beldiman, L. G. Bushnell. Asymptotic Behavior of Nonlinear Networked Control Systems. IEEE Transactions on Automatic Control, 2001,46(7): 1093-1097
84 D. Ne?i?, A. R. Teel. Input-to-state Stability Properties of Networked Control Systems. IEEE Transactions on Automatic Control, 2004,49(10): 1650-1658
85 D. Nesic, A. R. Teel. Input-output Stability Properties of Networked Control Systems. IEEE Transactions on Automatic control, 2004,49(10): 1659-1667
86 Yang Jun, Wang Xiangdong. Stability of a Class of Networked Control Systems. Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, 2004: 1400-1405
87 N. Xie, G. Y. Tang. Delay-dependent Nonfragile Guaranteed Cost Control for Nonlinear Time-delay Systems. Nonlinear Analysis Theory, Methods and Applications, 2006,64(9): 2084-2097
88 Gao Jinfeng, Su Hongye, Ji Xiaofu, Chu Jian. Robust Stabilization for a Class of Nonlinear Networked Control Systems. Journal of Control Theory and Applications, 2008,6(3): 300-304
89王艳,陈庆伟,吴晓蓓,胡维礼.一类非线性网络控制系统的控制器设计.兵工学报, 2006,27(4): 731-736
90马丹,赵军.一类仿射非线性网络控制系统的稳定性分析.控制与决策, 2006,21(9): 1001-1010
91 D. Ne?i?, A. R. Teel. On Lp Stability of Networked Control Systems. Proceeding of 42nd IEEE Conference on Decision and Control, Hawaii, 2003: 1188-1193
92 S. Mastellone, C. T. Abdallah, P. Dorato. Stability and Finie-Time Stability Analysis of Discrete-Time Nonlinear Networked Control Systems. American Control Conference, Porland, OR, USA, 2005
93 S. Battilo. Control over a Communication Channel with Random Noise and Delays. Automatica, 2008,41(2): 348-360
94 M. Yu, L. Wang, T. Chu. Sampled-data Stabilization of Networked Control Systems with Nonlinearity. IEE Proc. Control Theory Application, 2005,152(6): 609-614
95 Yang DeDong, Zhang Huaguang. Robust H∞Networked Control for Uncertain Fuzzy Systems with Time-delay. Acta Automatica Sinica, 2007,33(7): 726-730
96 H. G. Zhang, D. D. Yang, T. Y. Chai. Guaranteed Cost Networked Control for T-S Fuzzy Systems with Time Delays. IEEE Transactions on System Man Cybern. Part C, 2007,37(2): 160-172
97 Jia Xinchun, Zhang Dawei, Zheng Lihong, Zheng Nanning. Modeling and Stabilization for a Class of Nonlinear Networked Control Systems: A T-S Fuzzy Approach. Progress in Natural Science, 2008,18: 1031-1037
98 Jiang Xiefu, Han Qinglong. On Designing Fuzzy Controllers for a Class of Nonlinear NetworkedControl Systems. IEEE Transactions on Fuzzy Systems, 2008,16(4): 1050-1060
99 C. Popescu, Y. Wang, H. Zhuang. Stability of Delayed Networked Nonlinear Systems. Proceeding of the 16th Florida Conference on the Recent Advances in Robotics,2003
100朱其新,胡寿松.网络控制系统的能控性和能观性.控制与决策, 2004,19(2): 157-161
101 Cheng Xinming, Gui Weihua, Zhengjia Gan. Stability Analysis and Controller Design for a Class of Delay Systems Via Observer Based on Network. J. Control Theory Application, 2009,7(3): 328-334
102傅磊,戴冠中.网络化控制系统的时延观测器设计及其状态反馈控制.控制理论与应用, 2008,25(4): 764-772
103 Ying Zheng, Huajing Fang, Hua O. Wang. Takagi-Sugeno Fuzzy-Model-Based Fault Detection for Networked Control Systems with Markov Delays. IEEE Transactions on systems, Man, and Cybernetics—Part B:Cybernetics,2006,36(4): 924-929
104唐斌,刘国平,桂卫华.不确定系统的网络化保性能控制.控制理论与应用, 2008,25(1): 105-110
105 K. Tanaka, M. Sugeno. Stability Analysis and Design of Fuzzy Control Systems. Fuzzy Sets and Systems, 1992,45: 135-156
106 S. G. Cao, N. W. Rees, G. Feng. Stability Analysys and Design for a Class of Continuous Time Fuzzy Control Systems. International Journal of Control, 1996,64: 1069-1087
107 S. G. Cao, N. W. Rees, G. Feng. Analysis and Design for a Class of Complex Control System-Part I: Fuzzy Modeling and Identification. Automatica, 1997,33: 1017-1028
108 Ho Jae Lee, Jin Bae Park, Chen Guanrong. Robust Fuzzy Control of Nonlinear Systems with Parametric Uncertainties. IEEE Transactions on Fuzzy Systems, 2001,9(2): 369-379
109 K. Tanaka, M. Sugeno. Fuzzy Stability Criterion of a Class of Nonlinear Systems. Information Science, 1993,70: 3-26
110 R. H. Middleton, G. C. Goodwin. Digital Control and Estimation—a Unified Approach. Englewood Cliffs: Prentice Hall, 1990
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42 D. K. Kim, P. G. Park, J. W. Ko. Output-feedback H∞Control of Systems over Communication Networks Using a Deterministic Switching System Approach. Automatica, 2004,40(7): 1205-1212
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52 Gao Jinfeng, Su Hongye, Chu Jian. Robust Stability Analysis of Networked Control Systems. Journal of Control Theory and Applications, 2009(3): 301-306
53 Gao Jinfeng, Su Hongye, Ji Xiaofu, Chu Jian. Robust Stabilization for a Class of Nonlinear Networked Control Systems. Journal of Control Theory and Applications, 2008,6(3): 300-304
54 G. Liu, D. Rees, S. Chai. Design and Practical Implementation of Networked Predictive Control Systems. Proceedings of the 2005 Conference on Networking, Sensing and Control, 2005: 336-341
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63 Vaccarini Massimo, Longhi Sauro, Katebi M. Reza. Unconstrained Networked Decentralized Model Predictive Control. Journal of Process Control, 2009,19(2): 328-339
64 Wang Rui, Liu guoping, Wang Bo, Wang Wei, Rees David. L2-gain Analysis for Networked Predictive Control Systems Based on Switching Method. International Journal of Control, 2009,82(6): 1148-1156
65 Li Zhijun, Sun Dehui, Shi Yuntao, Wang Lifeng. A Stabilizing Model Predictive Control for Networked Control System with Data Packet Dropout. Journal of Control Theory and Applications, 2009,(3): 281-284
66 Yu Jianyong, Yimin Mei, Shiming Yu. MPC-based Method Dealing with Vacant Sampling and Data Rejection in Networked Control Systems. Proceedings of the Third International Conference on Machine Learning and Cybernetics, shanghai, 2004: 574-578
67 L. A. Montestruque, P. J.Antsaklis. State and Output Feedback Control in Model-based Networked Control Systems. The 41st IEEE Conference on Decision and Control, Las Vegas, 2002: 1620-1625
68马卫国,邵诚.网络控制系统随机稳定性研究.自动化学报, 2007,33(8): 878-882
69 L. A. Montestruque, P. J. Antsaklis. On the Model-based Control of Networked System. Automatica, 2003,39(10): 1837-1843
70 L. A. Montestruque, P. J. Antsaklis. Stability of Model-based Networked Control Systems with Time-varying Transmission Times. IEEE Transaction. On Automatic Control, 2004,49(9): 1526-1572
71 Luis A. Montestruque, Panos J.Antsaklis. Quantization In Model Based Networked Control Systems. IFAC, 2005
72汤贤铭,俞金寿.基于模型的输出反馈网络控制系统反馈调度研究.控制与决策, 2009, 24(1): 141-144
73孙海燕,侯朝桢,赵奇.基于模型的网络控制系统稳定性.控制与决策, 2005,20(4): 412-416
74 S. Mastellone, C. T. Abdallah, P. Dorato. Model-based Networked Control for Nonlinear Systems with Stochastic Packet Dropout. American Control Conference, Portland, OR, USA. 2005
75 P. V. Zhivoglyadov, R. H. Middleton. Networked Control Design for Linear Systems. Automatica, 2003,39(4): 743-750
76黄杰,吴平东,修震,马树元,陈之龙.基于因特网的运动控制系统中变采样过程的研究.控制与决策, 2005,20(7): 755-763
77薛燕,刘克.变采样网络控制系统的稳定性分析.清华大学学报, 2007,47(S2): 1919-1923
78薛燕,刘克.基于预测值控制的变采样网络控制系统.控制理论与应用, 2009,26(6): 657-660
79 Gao Huijun, Wu Junli, Shi Peng. Robust Sampled-data H∞Control with Stochastic Sampling. Automatica, 2009,45(7): 1729-1736
80 Liu Fuchun, Yao Yu, He Fenghua, Chen Songlin. Stability Analysis of Networked Control Systems with Time-varying Sampling Periods. Journal of Control Theory and Applications, 2008,6(1): 22-25
81 Li Xuan, Wu Xiaobei. Guaranteed Cost Fault-tolerant Controller Design of Networked Control Systems under Variable-period Sampling. Information Technology Journal, 2009,8(4): 537-543
82 Wang Yan, Sun Zengqi. Stochastic Stabilization of Networked Control Systems with Time-varying Sampling Periods. 11th IEEE International Conference on Communication Technology Proceedings, ICCT 2008: 541-545
83 G. C. Walsh, O. Beldiman, L. G. Bushnell. Asymptotic Behavior of Nonlinear Networked Control Systems. IEEE Transactions on Automatic Control, 2001,46(7): 1093-1097
84 D. Ne?i?, A. R. Teel. Input-to-state Stability Properties of Networked Control Systems. IEEE Transactions on Automatic Control, 2004,49(10): 1650-1658
85 D. Nesic, A. R. Teel. Input-output Stability Properties of Networked Control Systems. IEEE Transactions on Automatic control, 2004,49(10): 1659-1667
86 Yang Jun, Wang Xiangdong. Stability of a Class of Networked Control Systems. Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, 2004: 1400-1405
87 N. Xie, G. Y. Tang. Delay-dependent Nonfragile Guaranteed Cost Control for Nonlinear Time-delay Systems. Nonlinear Analysis Theory, Methods and Applications, 2006,64(9): 2084-2097
88 Gao Jinfeng, Su Hongye, Ji Xiaofu, Chu Jian. Robust Stabilization for a Class of Nonlinear Networked Control Systems. Journal of Control Theory and Applications, 2008,6(3): 300-304
89王艳,陈庆伟,吴晓蓓,胡维礼.一类非线性网络控制系统的控制器设计.兵工学报, 2006,27(4): 731-736
90马丹,赵军.一类仿射非线性网络控制系统的稳定性分析.控制与决策, 2006,21(9): 1001-1010
91 D. Ne?i?, A. R. Teel. On Lp Stability of Networked Control Systems. Proceeding of 42nd IEEE Conference on Decision and Control, Hawaii, 2003: 1188-1193
92 S. Mastellone, C. T. Abdallah, P. Dorato. Stability and Finie-Time Stability Analysis of Discrete-Time Nonlinear Networked Control Systems. American Control Conference, Porland, OR, USA, 2005
93 S. Battilo. Control over a Communication Channel with Random Noise and Delays. Automatica, 2008,41(2): 348-360
94 M. Yu, L. Wang, T. Chu. Sampled-data Stabilization of Networked Control Systems with Nonlinearity. IEE Proc. Control Theory Application, 2005,152(6): 609-614
95 Yang DeDong, Zhang Huaguang. Robust H∞Networked Control for Uncertain Fuzzy Systems with Time-delay. Acta Automatica Sinica, 2007,33(7): 726-730
96 H. G. Zhang, D. D. Yang, T. Y. Chai. Guaranteed Cost Networked Control for T-S Fuzzy Systems with Time Delays. IEEE Transactions on System Man Cybern. Part C, 2007,37(2): 160-172
97 Jia Xinchun, Zhang Dawei, Zheng Lihong, Zheng Nanning. Modeling and Stabilization for a Class of Nonlinear Networked Control Systems: A T-S Fuzzy Approach. Progress in Natural Science, 2008,18: 1031-1037
98 Jiang Xiefu, Han Qinglong. On Designing Fuzzy Controllers for a Class of Nonlinear NetworkedControl Systems. IEEE Transactions on Fuzzy Systems, 2008,16(4): 1050-1060
99 C. Popescu, Y. Wang, H. Zhuang. Stability of Delayed Networked Nonlinear Systems. Proceeding of the 16th Florida Conference on the Recent Advances in Robotics,2003
100朱其新,胡寿松.网络控制系统的能控性和能观性.控制与决策, 2004,19(2): 157-161
101 Cheng Xinming, Gui Weihua, Zhengjia Gan. Stability Analysis and Controller Design for a Class of Delay Systems Via Observer Based on Network. J. Control Theory Application, 2009,7(3): 328-334
102傅磊,戴冠中.网络化控制系统的时延观测器设计及其状态反馈控制.控制理论与应用, 2008,25(4): 764-772
103 Ying Zheng, Huajing Fang, Hua O. Wang. Takagi-Sugeno Fuzzy-Model-Based Fault Detection for Networked Control Systems with Markov Delays. IEEE Transactions on systems, Man, and Cybernetics—Part B:Cybernetics,2006,36(4): 924-929
104唐斌,刘国平,桂卫华.不确定系统的网络化保性能控制.控制理论与应用, 2008,25(1): 105-110
105 K. Tanaka, M. Sugeno. Stability Analysis and Design of Fuzzy Control Systems. Fuzzy Sets and Systems, 1992,45: 135-156
106 S. G. Cao, N. W. Rees, G. Feng. Stability Analysys and Design for a Class of Continuous Time Fuzzy Control Systems. International Journal of Control, 1996,64: 1069-1087
107 S. G. Cao, N. W. Rees, G. Feng. Analysis and Design for a Class of Complex Control System-Part I: Fuzzy Modeling and Identification. Automatica, 1997,33: 1017-1028
108 Ho Jae Lee, Jin Bae Park, Chen Guanrong. Robust Fuzzy Control of Nonlinear Systems with Parametric Uncertainties. IEEE Transactions on Fuzzy Systems, 2001,9(2): 369-379
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