Probability-guaranteed set-membership filtering for systems with incomplete measurements

Wei, Guoliang; Liu, Shuai; Song, Yan; Liu, Yurong

doi:10.1016/j.automatica.2015.06.037

Cited by 104 publications

(66 citation statements)

References 15 publications

(14 reference statements)

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“…Finally, a simulation example has been presented to verify the effectiveness of the proposed design approach. Further research topics include sampled-data control problem under the general noisy sampling interval by taking into account the incomplete information investigated in [3,4,12,15]. …”

Section: Discussionmentioning

confidence: 99%

Stabilization for sampled-data systems under noisy sampling interval

2016

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In engineering practice, the sampling interval for a sampled-data system often fluctuates around a nominal/ideal value based on certain probability distributions that can be specified a priori through statistical tests. In this paper, a fundamental stabilization problem is investigated for a class of sampled-data systems under noisy sampling interval. The stochastic sampleddata control system under consideration is first converted into a discrete-time system whose system matrix is represented as an equivalent yet tractable form via the matrix exponential computation. Then, by introducing a Vandermonde matrix, the mathematical expectation of the quadratic form of the system matrix is computed. By recurring to the Kronecker product operation, the sampled-data stabilization controller is designed such that the closed-loop system is stochastically stable in the presence of noisy sampling interval. Subsequently, a special case is considered where the sampling interval obeys the continuous uniform distribution and the corresponding stabilization controller is designed. Finally, a numerical simulation example is provided to demonstrate the effectiveness of the proposed design approach.

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Section: Discussionmentioning

confidence: 99%

Stabilization for sampled-data systems under noisy sampling interval

2016

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“…Linear matrix inequality method time-invariant complex dynamical systems feasible [25], [40]- [44] Difference linear matrix inequality method time-varying complex dynamical systems feasible [21], [27], [45], [46] Innovation analysis approach linear time-invariant/time-varying systems optimal [30]- [32], [39], [47], [48] Hamilton-Jacobi-Isaacs inequality approach general nonlinear time-invariant systems feasible [35] Backward Riccati difference equation method nonlinear time-varying systems sub-optimal [37], [49], [50] Forward Riccati difference equation method nonlinear time-varying systems sub-optimal [7], [16], [51]- [54] [49], [56], [57], the N-order Rice fading channel has been modeled by sequences of independent and identically distributed Gaussian random variables with known means and variances, where the multi-path induced fading stemming mainly from multi-path propagation has been considered when dealing with the control and estimation problems for networked systems and the impact from the fading measurements onto the control/estimation performance has been examined.…”

Section: Applications Solutions Referencesmentioning

confidence: 99%

“…Therefore, the design of the filtering algorithms has received increasing research attention. According to different performance indices (minimized variance constraint, set-valued constraints, guaranteed H ∞ performance requirements and so on), a great number of filtering algorithms have been developed for networked systems, such as Kalman filtering [13], [14], extended Kalman filtering [15]- [18], set-valued filtering [19], [20], setmembership filtering [21], H 2 filtering [22]- [24], H ∞ filtering [25], [26], and consensus filtering [27], [28]. On the other hand, the design of linear optimal estimators (including filter, predictor and smoother) for networked systems has gained a great deal of research attention as conducted in [29]- [32].…”

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confidence: 99%

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

et al. 2016

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In this paper, some recent advances on the estimation, filtering and fusion for networked systems are reviewed. Firstly, the network-induced phenomena under consideration are briefly recalled including missing/fading measurements, signal quantization, sensor saturations, communication delays, and randomly occurring incomplete information. Secondly, the developments of the estimation, filtering and fusion for networked systems from four aspects (linear networked systems, nonlinear networked systems, complex networks and sensor networks) are reviewed comprehensively. Subsequently, some recent results on the estimation, filtering and fusion for systems with the network-induced phenomena are reviewed in great detail. In particular, some latest results on the multiobjective filtering problems for time-varying nonlinear networked systems are summarized. Finally, conclusions are given and several possible research directions concerning the estimation, filtering, and fusion for networked systems are highlighted.

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“…In networked control systems (NCSs) [1], [2], in addition to the well-studied communication delays [3], [4], packet dropouts [5]- [8] and signal quantization [9]- [11], the channel fading phenomenon is often unavoidable due mainly to the multi-path propagation, shadowing effects from obstacles, as well as the path loss. Up to now, the stability and state estimation problems for the networked systems with fading measurements have drawn some initial research attention [12]- [17].…”

Section: Introductionmentioning

confidence: 99%

Fault detection filter design for networked multi‐rate systems with fading measurements and randomly occurring faults

Yong

Wang

Zou

et al. 2016

IET Control Theory & Applications

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In this paper, the fault detection problem is investigated for a class of networked multi-rate systems (NMSs) with network-induced fading channels and randomly occurring faults. The stochastic characteristics of the fading measurements are governed by mutually independent random channel coefficients over the known interval [0,1]. By applying the lifting technique, the system model for the observer-based fault detection is established. With the aid of the stochastic analysis approach, sufficient conditions are established under which the stochastic stability of the error dynamics for the state estimation is guaranteed and the prescribed H ∞ performance constraint on the error dynamics for the fault estimation is achieved. Based on the established conditions, the addressed fault detection problem of NMSs is recast as a convex optimization one that can be solved via the semi-definite program method, and the explicit expression of the desired fault detection filter is derived by means of the feasibility of certain matrix inequalities. The main results are specialized to the networked single-rate systems that are a special case of the NMSs. Finally, two simulation examples are utilized to illustrate the effectiveness of the proposed fault detection method.

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Probability-guaranteed set-membership filtering for systems with incomplete measurements

Cited by 104 publications

References 15 publications

Stabilization for sampled-data systems under noisy sampling interval

Stabilization for sampled-data systems under noisy sampling interval

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

Fault detection filter design for networked multi‐rate systems with fading measurements and randomly occurring faults

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