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Peng Shi - One of the best experts on this subject based on the ideXlab platform.
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fault detection filtering for nonhomogeneous markovian jump systems via a fuzzy approach
IEEE Transactions on Fuzzy Systems, 2018Co-Authors: Peng Shi, Chengchew LimAbstract:This paper investigates the problem of the fault detection filter design for nonhomogeneous Markovian jump systems by a Takagi–Sugeno fuzzy approach. Attention is focused on the construction of a fault detection filter to ensure the estimation Error Dynamic stochastically stable, and the prescribed performance requirement can be satisfied. The designed fuzzy model-based fault detection filter can guarantee the sensitivity of the residual signal to faults and the robustness of the external disturbances. By using the cone complementarity linearization algorithm, the existence conditions for the design of fault detection filters are provided. Meanwhile, the Error between the residual signal and the fault signal is made as small as possible. Finally, a practical application is given to illustrate the effectiveness of the proposed technique.
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dissipativity based filtering for fuzzy switched systems with stochastic perturbation
IEEE Transactions on Automatic Control, 2016Co-Authors: Peng ShiAbstract:In this technical note, the problem of the dissipativity-based filtering problem is considered for a class of T-S fuzzy switched systems with stochastic perturbation. Firstly, a sufficient condition of strict dissipativity performance is given to guarantee the mean-square exponential stability for the concerned T-S fuzzy switched system. Then, our attention is focused on the design of a filter to the T-S fuzzy switched system with Brownian motion. By combining the average dwell time technique with the piecewise Lyapunov function technique, the desired fuzzy filters are designed that guarantee the filter Error Dynamic system to be mean-square exponential stable with a strictly dissipative performance, and the corresponding solvability condition for the fuzzy filter is also presented based on the linearization procedure approach. Finally, an example is provided to illustrate the effectiveness of the proposed dissipativity-based filter technique.
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robust filtering for nonlinear nonhomogeneous markov jump systems by fuzzy approximation approach
IEEE Transactions on Systems Man and Cybernetics, 2015Co-Authors: Yanyan Yin, Fei Liu, Peng Shi, Kok Lay Teo, Chengchew LimAbstract:This paper addresses the problem of robust fuzzy $L_{2}-L_{\infty }$ filtering for a class of uncertain nonlinear discrete-time Markov jump systems (MJSs) with nonhomogeneous jump processes. The Takagi-Sugeno fuzzy model is employed to represent such nonlinear nonhomogeneous MJS with norm-bounded parameter uncertainties. In order to decrease conservation, a polytope Lyapunov function which evolves as a convex function is employed, and then, under the designed mode-dependent and variation-dependent fuzzy filter which includes the membership functions, a sufficient condition is presented to ensure that the filtering Error Dynamic system is stochastically stable and that it has a prescribed $L_{2}-L_{\infty }$ performance index. Two simulated examples are given to demonstrate the effectiveness and advantages of the proposed techniques.
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sensor networks with random link failures distributed filtering for t s fuzzy systems
IEEE Transactions on Industrial Informatics, 2013Co-Authors: Peng ShiAbstract:The paper is concerned with the problem of distributed fuzzy filter design for a class of sensor networks described by discrete-time T-S fuzzy systems with time-varying delays and multiple probabilistic packet losses. In sensor network, each individual sensor can receive not only its own measurement but also its neighboring sensors' measurements according to the interconnection topology to estimate the system states. Our attention is focused on the design of distributed fuzzy filters to guarantee the filtering Error Dynamic system to be mean-square asymptotically stable with an average \mathscr H∞ performance. Sufficient conditions for the obtained filtering Error Dynamic system are proposed by applying an comparison model and the scaled small gain theorem. Based on the measurements and estimates of the system states and its neighbors for each sensor, the solution of the parameters of the distributed fuzzy filters is characterized in terms of the feasibility of a convex optimization problem. Finally, an illustrative example is provided to illustrate the effectiveness of the proposed approaches in sensor networks.
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observer design for switched recurrent neural networks an average dwell time approach
IEEE Transactions on Neural Networks, 2011Co-Authors: Jie Lian, Zhi Feng, Peng ShiAbstract:This paper is concerned with the problem of observer design for switched recurrent neural networks with time-varying delay. The attention is focused on designing the full-order observers that guarantee the global exponential stability of the Error Dynamic system. Based on the average dwell time approach and the free-weighting matrix technique, delay-dependent sufficient conditions are developed for the solvability of such problem and formulated as linear matrix inequalities. The Error-state decay estimate is also given. Then, the stability analysis problem for the switched recurrent neural networks can be covered as a special case of our results. Finally, four illustrative examples are provided to demonstrate the effectiveness and the superiority of the proposed methods.
Lina Yao - One of the best experts on this subject based on the ideXlab platform.
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fault diagnosis and fault tolerant tracking control for the non gaussian singular time delayed stochastic distribution system with pdf approximation Error
Neurocomputing, 2016Co-Authors: Lina Yao, Long FengAbstract:In this paper, fault diagnosis and fault tolerant tracking control algorithms are proposed for the non-Gaussian singular time-delayed stochastic distribution control system. The square root B-spline model is used to approximate the output probability density function (PDF), and the PDF approximation Error is taken into consideration. A fault diagnosis approach based on the adaptive observer is constructed to diagnose the size of fault in the singular stochastic distribution control (SDC) system. When fault occurs, in order to track the expected PDF, a distribution tracking Error Dynamic system is established. The purpose of fault tolerant tracking control is transformed into making the distribution tracking Error at each time instant satisfy a certain upper bound beyond a limited time. Then the fault tolerant controller is designed using the fault diagnosis information and other measurable information. A simulation example is included to illustrate the effectiveness of the proposed algorithms and encouraging results have been obtained.
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fault diagnosis and fault tolerant control for non gaussian non linear stochastic systems using a rational square root approximation model
Iet Control Theory and Applications, 2013Co-Authors: Lina Yao, Jifeng Qin, Aiping Wang, Hong WangAbstract:The purpose of the fault detection and diagnosis of stochastic distribution control systems is to use the measured input and the system output probability density functions (PDFs) to obtain the fault information of the system. In this paper, the rational square-root B-spline model is used to represent the Dynamics between the output PDF and the input. This is then followed by the novel design of a non-linear neural network observer-based fault diagnosis (FD) algorithm so as to diagnose the fault in the Dynamic part of such systems. Convergency analysis is performed for the Error Dynamic system raised from the fault detection and diagnosis phase using the Lyapunov stability theorem. Finally, based on the FD information, a new fault-tolerant control based on proportional integral tracking control scheme is designed to make the post-fault PDF still track the given distribution. A simulated example is given to illustrate the efficiency of the proposed algorithms.
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robust fault diagnosis for non gaussian stochastic systems based on the rational square root approximation model
Science in China Series F: Information Sciences, 2008Co-Authors: Lina Yao, Hong WangAbstract:The task of robust fault detection and diagnosis of stochastic distribution control (SDC) systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system. Using the rational square-root B-spline model to represent the Dynamics between the output PDF and the input, in this paper, a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the Dynamic part of such systems with model uncertainties. When certain conditions are satisfied, the weight vector of the rational square-root B-spline model proves to be bounded. Convergency analysis is performed for the Error Dynamic system raised from robust fault detection and fault diagnosis phase. Computer simulations are given to demonstrate the effectiveness of the proposed algorithm.
Hong Wang - One of the best experts on this subject based on the ideXlab platform.
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fault diagnosis and fault tolerant control for non gaussian non linear stochastic systems using a rational square root approximation model
Iet Control Theory and Applications, 2013Co-Authors: Lina Yao, Jifeng Qin, Aiping Wang, Hong WangAbstract:The purpose of the fault detection and diagnosis of stochastic distribution control systems is to use the measured input and the system output probability density functions (PDFs) to obtain the fault information of the system. In this paper, the rational square-root B-spline model is used to represent the Dynamics between the output PDF and the input. This is then followed by the novel design of a non-linear neural network observer-based fault diagnosis (FD) algorithm so as to diagnose the fault in the Dynamic part of such systems. Convergency analysis is performed for the Error Dynamic system raised from the fault detection and diagnosis phase using the Lyapunov stability theorem. Finally, based on the FD information, a new fault-tolerant control based on proportional integral tracking control scheme is designed to make the post-fault PDF still track the given distribution. A simulated example is given to illustrate the efficiency of the proposed algorithms.
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robust fault diagnosis for non gaussian stochastic systems based on the rational square root approximation model
Science in China Series F: Information Sciences, 2008Co-Authors: Lina Yao, Hong WangAbstract:The task of robust fault detection and diagnosis of stochastic distribution control (SDC) systems with uncertainties is to use the measured input and the system output PDFs to still obtain possible faults information of the system. Using the rational square-root B-spline model to represent the Dynamics between the output PDF and the input, in this paper, a robust nonlinear adaptive observer-based fault diagnosis algorithm is presented to diagnose the fault in the Dynamic part of such systems with model uncertainties. When certain conditions are satisfied, the weight vector of the rational square-root B-spline model proves to be bounded. Convergency analysis is performed for the Error Dynamic system raised from robust fault detection and fault diagnosis phase. Computer simulations are given to demonstrate the effectiveness of the proposed algorithm.
Zhaohua Yang - One of the best experts on this subject based on the ideXlab platform.
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a non saturated sliding mode control of shaft deflection for magnetically suspended momentum wheel with coupled disturbance and saturated amplifier
Acta Astronautica, 2017Co-Authors: Xinwei Wang, Yuanjin Yu, Zhaohua YangAbstract:Abstract The magnetically suspended momentum wheel (MSMW) expands its fresh functions through deflecting the rotary shaft. An improved nonsingular terminal sliding-mode control (NTSMC) method is proposed to achieve high precision tracking of shaft deflection for the MSMW under coupled disturbance and saturated amplifier. A novel structure designed for this MSMW is introduced initially. Its magnetic torque model and coupled disturbance are analyzed, and a tracking Error Dynamic model is established. Then a NTSMC method is applied to shaft tracking control. As the saturation of amplifier influences tracking performances, an improved NTSMC is designed to deal with saturation problem. Finally, several simulations are performed to validate the effectiveness of the proposed method. The results indicate the proposed method improves the tracking precision and velocity compared with the conventional integral sliding-mode method, and solves the saturation problem compared with existing NTSMC method.
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active vibration control of magnetically suspended wheel using active shaft deflection
IEEE Transactions on Industrial Electronics, 2017Co-Authors: Yuanjin Yu, Zhaohua YangAbstract:A novel method is proposed to actively attenuate the synchronous vibration of a magnetically suspended wheel (MSW) on the basis of shaft deflection. Considering the nonparallelism of the rotary axis and the inertial axis of the rotor, a precise Dynamic model of the MSW is established as a linear model with synchronous disturbances. To reduce the synchronous vibration torques transferred to the base, the rotor shaft is actively deflected. A reference deflection angle is scheduled according to the synchronous disturbances, and a new tracking Error Dynamic model is established. Then, a composite control method is designed by combining a state feedback method and a disturbance observer. The stabilities of the disturbance observer and the closed-loop system are proven by Lyapunov's stability theorem. The parameters of the disturbance observer and the state feedback controller can be obtained by solving several linear matrix inequalities. The feasibility of vibration reduction due to the proposed method is analyzed. Finally, numerical simulations and experiments are performed. The results indicate that the proposed method significantly reduces the synchronous vibration torques of the MSW.
Chengchew Lim - One of the best experts on this subject based on the ideXlab platform.
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fault detection filtering for nonhomogeneous markovian jump systems via a fuzzy approach
IEEE Transactions on Fuzzy Systems, 2018Co-Authors: Peng Shi, Chengchew LimAbstract:This paper investigates the problem of the fault detection filter design for nonhomogeneous Markovian jump systems by a Takagi–Sugeno fuzzy approach. Attention is focused on the construction of a fault detection filter to ensure the estimation Error Dynamic stochastically stable, and the prescribed performance requirement can be satisfied. The designed fuzzy model-based fault detection filter can guarantee the sensitivity of the residual signal to faults and the robustness of the external disturbances. By using the cone complementarity linearization algorithm, the existence conditions for the design of fault detection filters are provided. Meanwhile, the Error between the residual signal and the fault signal is made as small as possible. Finally, a practical application is given to illustrate the effectiveness of the proposed technique.
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robust filtering for nonlinear nonhomogeneous markov jump systems by fuzzy approximation approach
IEEE Transactions on Systems Man and Cybernetics, 2015Co-Authors: Yanyan Yin, Fei Liu, Peng Shi, Kok Lay Teo, Chengchew LimAbstract:This paper addresses the problem of robust fuzzy $L_{2}-L_{\infty }$ filtering for a class of uncertain nonlinear discrete-time Markov jump systems (MJSs) with nonhomogeneous jump processes. The Takagi-Sugeno fuzzy model is employed to represent such nonlinear nonhomogeneous MJS with norm-bounded parameter uncertainties. In order to decrease conservation, a polytope Lyapunov function which evolves as a convex function is employed, and then, under the designed mode-dependent and variation-dependent fuzzy filter which includes the membership functions, a sufficient condition is presented to ensure that the filtering Error Dynamic system is stochastically stable and that it has a prescribed $L_{2}-L_{\infty }$ performance index. Two simulated examples are given to demonstrate the effectiveness and advantages of the proposed techniques.