Set-Membership Fuzzy Filtering for Nonlinear Discrete-Time Systems

Yang, Fang; Li, Yongmin

doi:10.1109/tsmcb.2009.2020436

Cited by 61 publications

(2 citation statements)

References 49 publications

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“…Moreover, the set-membership filtering method can guarantee that all possible true states are constrained in the ellipsoid sets, see e.g. X. , Wei et al (2016), Xia et al (2017), Yang and Li (2010), J. . CONTACT As we all know, the Kalman filtering is very effective for estimating the states of the systems with Gaussian noise.…”

Section: Introductionmentioning

confidence: 99%

“…For example, the set-membership state estimation problem has been discussed in for a class of state-saturated systems with mixed time-delays under the WTOD protocol, where a sufficient condition has been derived to guarantee the existence of the desired setmembership filters by solving a set of recursive linear matrix inequalities and an optimization problem has been put forward to acquire the minimized ellipsoid in the sense of matrix trace. In Yang and Li (2010), the T-S fuzzy model has been employed to approximate the nonlinear systems and the set-membership filtering method has been given.…”

Section: Introductionmentioning

confidence: 99%

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Set-membership filtering for a class of nonlinear complex networks with mixed time-delays and incomplete measurements

Liang

Cheng

Chen

et al. 2020

Systems Science & Control Engineering

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In this paper, the set-membership filtering problem is discussed for a class of discrete time-varying nonlinear complex networks with mixed time-delays and incomplete measurements. Both the process noise and the measurement noise are unknown but bounded. The nonlinear function is assumed to satisfy the sector bounded condition and the incomplete measurement is model by a diagonal matrix. The main purpose of this paper is to design a set-membership filtering method in order to provide the certain regions, which include the real states. Accordingly, the filtering errors at each time can be limited to the ellipsoid sets for the considered nonlinear delayed complex networks in the simultaneous presence of unknown but bounded noise, mixed time-delays and incomplete measurements. Moreover, a convex optimization method is presented to obtain the minimum ellipsoid domain. In addition, the filter gain matrix can be obtained by solving a series of linear matrix inequalities. Finally, a simulation example is used to illustrate the effectiveness of the proposed set-membership filtering method.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Set-membership filtering for a class of nonlinear complex networks with mixed time-delays and incomplete measurements

Liang

Cheng

Chen

et al. 2020

Systems Science & Control Engineering

View full text Add to dashboard Cite

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Adaptive control and signal processing literature survey (No. 18)

2010

Adaptive Control & Signal

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that although the list has been broadly categorized, these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Erfu Yang at erfu.yang@eee.strath.ac.uk. ADAPTIVE SYSTEMSBaldi S, Battistelli G, Mosca E, Tesi P. Multi-model unfalsified adaptive switching supervisory control. Automatica 2010; 46(2):249-259. Cao C, Hovakimyan N. Stability margins of L 1 adaptive control architecture. IEEE Transactions on Automatic Control 2010; 55(2):480-487. Chen W, Jiao L. Adaptive tracking for periodically time-varying and nonlinearly parameterized systems using multilayer neural networks. IEEE Transactions on Neural Networks 2010; 21(2):345-351. Han T-T, Ge S, Lee T. Persistent dwell-time switched nonlinear systems: variation paradigm and gauge design. IEEE Transactions on Automatic Control 2010; 55(2):321-337. Li L, Petersen I. A Gramian-based approach to model reduction for uncertain systems. IEEE Transactions on Automatic Control 2010; 55(2):508-514. Li Q-K, Zhao J, Dimirovski G, Liu X-J. State convergence property of perturbed switched linear time-delay systems. IET Control Theory and Applications 2010; 4(2):273-281. Li T-S, Tong S-C, Feng G. A novel robust adaptive-fuzzytracking control for a class of nonlinear multi-input/multioutput systems. IEEE Transactions on Fuzzy Systems 2010; 18(1):150-160. Ma H-J, Yang G-H. Adaptive output control of uncertain nonlinear systems with non-symmetric dead-zone input. Automatica 2010; 46(2):413-420. Niedzwiecki M. Generalized adaptive notch smoothing revisited. IEEE Transactions on Signal Processing 2010; 58(3):1565-1576. Obaidat M, Misra S, Papadimitriou G. Adaptive and learning systems. IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetic 2010; 40(1):2-5. Yang Z-J, Hara S, Kanae S, Wada K, Su C-Y. An adaptive robust nonlinear motion controller combined with disturbance observer. IEEE Transactions on Control Systems Technology 2010; 18(2):454-462. Yildiz Y, Annaswamy A, Kolmanovsky L, Yanakiev D. Adaptive posicast controller for time-delay systems with relative degree n * 2. Automatica 2010; 46(2):279-289. Zhang W-A, Yu L. Stabilization of sampled-data control systems with control inputs missing. IEEE Transactions on Automatic Control 2010; 55(2):447-452. 2. SIGNAL PROCESSING Aboutanios E. Estimation of the frequency and decay factor of a decaying exponential in noise. IEEE Transactions on Signal Processing 2010; 58(2):501-509. Addesso P, Marano S, Matta V. Estimation of target location via likelihood approximation in sensor networks. IEEE Transactions on Signal Processing 2010; 58(3):1358-1368. Bai K, Tepedelenlioglu C. Distributed detection in UWB wireles...

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A new kernel RLS algorithm for systems with bounded noise

Constantin

Bigand

2017

Adaptive Control & Signal

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In this paper, we propose a new nonlinear set-membership recursive least-squares algorithm. The algorithm draws on a linear set-membership filter in conjunction with kernels for nonlinear processing. Set-membership algorithms exploit a priori model information that directly, or indirectly, prescribes dynamic constraints on the solution space. Such information is disregarded by conventional approaches. Kernel methods provide an implicit mapping of the data in a high-dimensional feature space where linear techniques are applied.Computations are done in the initial space by means of kernel functions. In this work, we develop a kernel-based version of a set-membership filter that belongs to a class of optimal bounding ellipsoid algorithms. Optimal bounding ellipsoid algorithms compute ellipsoidal approximations to regions in the parameter space that are consistent with the observed data and the model assumptions.Experiments involving stationary and nonstationary data are presented. Compared with existing kernel adaptive algorithms, the proposed algorithm offers an enhanced performance and sparsity, conjugated with better tracking capabilities.

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Set-Membership Fuzzy Filtering for Nonlinear Discrete-Time Systems

Cited by 61 publications

References 49 publications

Set-membership filtering for a class of nonlinear complex networks with mixed time-delays and incomplete measurements

Set-membership filtering for a class of nonlinear complex networks with mixed time-delays and incomplete measurements

Adaptive control and signal processing literature survey (No. 18)

A new kernel RLS algorithm for systems with bounded noise

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