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NN-based Output Tracking for More General Stochastic Nonlinear Systems with Unknown Control Coefficients
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作者 Na Duan Hui-Fang Min 《International Journal of Automation and computing》 EI CSCD 2017年第3期350-359,共10页
This paper considers the output tracking problem for more general classes of stochastic nonlinear systems with unknown control coefficients and driven by noise of unknown covariance. By utilizing the radial basis func... This paper considers the output tracking problem for more general classes of stochastic nonlinear systems with unknown control coefficients and driven by noise of unknown covariance. By utilizing the radial basis function neural network approximation method and backstepping technique, we successfully construct a controller to guarantee the solution process to be bounded in probability.The tracking error signal is 4th-moment semi-globally uniformly ultimately bounded(SGUUB) and can be regulated into a small neighborhood of the origin in probability. A simulation example is given to demonstrate the effectiveness of the control scheme. 展开更多
关键词 Stochastic nonlinear systems unknown control coefficients output tracking neural networks backstepping
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Robust fault detection for a class of nonlinear network control system with communication delay 被引量:5
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作者 Ai Qiangyu Liu Chunsheng Jiang Bin 《Journal of Systems Engineering and Electronics》 SCIE EI CSCD 2009年第5期1024-1030,共7页
To investigate a class of nonlinear network control system, a robust fault diagnosis method is presented based on the robust state observer. To access the objective that the designed robust filter is maximally toleran... To investigate a class of nonlinear network control system, a robust fault diagnosis method is presented based on the robust state observer. To access the objective that the designed robust filter is maximally tolerant to disturbances and sensitive to fault, the robustness and stability properties of the fault diagnosis scheme are established rigorously. Using the residual vector, a fault tolerant controller is established in order to guarantee the stability of the closed-loop system, and the controller law can be obtained by solving a set of linear matrix inequalities. Then, some relevant sufficient conditions for the existence of a solution are given by applying Lyapunov stability theory. Finally, a simulation example is performed to show the effectiveness of the proposed approach. 展开更多
关键词 nonlinear network control systems robust fault detection OBSERVER linear matrix inequality.
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Stochastic Channel Allocation for Nonlinear Systems with Markovian Packet Dropout 被引量:1
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作者 LONG Yushen LIU Shuai +1 位作者 XIE Lihua CHEN Jie 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2018年第1期22-37,共16页
This paper addresses a channel scheduling problem for group of dynamically decoupled nonlinear subsystems with actuators connected through digital communication channels and controlled by a centralized controller. Due... This paper addresses a channel scheduling problem for group of dynamically decoupled nonlinear subsystems with actuators connected through digital communication channels and controlled by a centralized controller. Due to the limited communication capacity, only one channel can be activated and hence there is only one pair of sensor and actuator can communicate with the controller at each time instant. In addition, the communication channels are not reliable so Markovian packed dropout is introduced. A predictive control framework is adopted for controller/scheduler co-design to alleviate the performance loss caused by the limited communication capacity. Instead of sending a single control value, the controller sends a sequence of predicted control values to a selected actuator so that there are control input candidates which can be fed to the subsystem when the actuator does not communicate with the controller. A stochastic algorithm is proposed to schedule the usage of the communication medium and sufficient conditions on stochastic stability are given under some mild assumptions. 展开更多
关键词 Markovian packet dropout model predictive control networked control systems nonlinear systems
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