State Estimation Over a Lossy Network in Spatially Distributed Cyber-Physical Systems

Deshmukh, Siddharth; Natarajan, Balasubramaniam; Pahwa, Anil

doi:10.1109/tsp.2014.2330810

Cited by 70 publications

(25 citation statements)

References 19 publications

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“…where H is the sensing matrix, and w k is the noise with R k covariance matrix. This sensing information transfers to the energy management system where packet loss occurs as follows [5], [28]:…”

Section: Iot Based System Modelmentioning

confidence: 99%

Estimating and Controlling the Renewable Microgrid States Using IoT Infrastructure

Rana

Chen

2019

Asian Journal of Control

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This paper considers the optimal filter design issue for microgrid state estimation and stabilisation under packet loss conditions. The considered internet of things (IoT) technologies collect system state information from sensors and send control messages to actuators where they are connected over a lossy communication network. From signal processing point of view, this paper develops an optimal state estimation algorithm using the mean squared error principle. Then a feedback controller is designed under the condition of packet dropouts in the communication network. Finally, the convergence of the developed algorithm is numerically shown. The performance of the proposed schemes are demonstrated using the renewable microgrid. KEYWORDSController under lossy channel, dynamic state estimation, internet of things, microgrid, packet dropouts the IoT devices are used for accommodating home appliances information, and communicating between green homes and networked control system in cloud server via the internet. These type of Kalman filter algorithms are extensively applied in different applications such as

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Section: Iot Based System Modelmentioning

confidence: 99%

Estimating and Controlling the Renewable Microgrid States Using IoT Infrastructure

Rana

Chen

2019

Asian Journal of Control

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“…On the other hand, the sensor networks equipped with distributed autonomous sensors have proven to be persistent research focuses which have gained an increasing attention in a variety of areas, and a great number of estimation schemes have been given in the literature [133]. It should be pointed out that the network-induced phenomena are inevitable in the sensor measurement outputs due to the noisy environment and limited communication capacity.…”

Section: Complex Network and Sensor Networkmentioning

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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“…For example, estimating the real voltage from sensor information must be completed before taking certain actions to regulate the voltage into some desired range in a power grid [16]. It is noted that the accuracy of state estimation has an important impact on computing control commands for safe and efficient operation of a CPS [17]- [19]. Therefore, it is of theoretical significance and practical relevance to investigate the problem of information fusion estimation for the CPSs [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Distributed Dimensionality Reduction Fusion Estimation for Cyber-Physical Systems Under DoS Attacks

Chen

Zhang

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

147

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This paper studies the distributed dimensionality reduction fusion estimation problem with communication delays for a class of cyber-physical systems (CPSs). The raw measurements are preprocessed in each sink node to obtain the local optimal estimate (LOE) of a CPS, and the compressed LOE under dimensionality reduction encounters with communication delays during the transmission. Under this case, a mathematical model with compensation strategy is proposed to characterize the dimensionality reduction and communication delays. This model also has the property to reduce the information loss caused by the dimensionality reduction and delays. Based on this model, a recursive distributed Kalman fusion estimator (DKFE) is derived by optimal weighted fusion criterion in the linear minimum variance sense. A stability condition for the DKFE, which can be easily verified by the exiting software, is derived. In addition, this condition can guarantee that estimation error covariance matrix of the DKFE converges to the unique steady-state matrix for any initial values, and thus the steady-state DKFE (SDKFE) is given. Notice that the computational complexity of the SDKFE is much lower than that of the DKFE. Moreover, a probability selection criterion for determining the dimensionality reduction strategy is also presented to guarantee the stability of the DKFE. Two illustrative examples are given to show the advantage and effectiveness of the proposed methods. WVWHS 7U^; W`J J J J J WVWHS 7U^ ; W`J J J J J J Fig. 4. The estimation performances of the CSE (i.e., Tr{Ξ 11 (t)}) with different selection probabilities γ 11 .

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State Estimation Over a Lossy Network in Spatially Distributed Cyber-Physical Systems

Cited by 70 publications

References 19 publications

Estimating and Controlling the Renewable Microgrid States Using IoT Infrastructure

Estimating and Controlling the Renewable Microgrid States Using IoT Infrastructure

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

Distributed Dimensionality Reduction Fusion Estimation for Cyber-Physical Systems Under DoS Attacks

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