Robust Kalman Filtering Under Model Perturbations

Zorzi, Mattia

doi:10.1109/tac.2016.2601879

Cited by 102 publications

(70 citation statements)

References 22 publications

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“…Our future work will focus on extending this study to extensive potential applications ina wholepower system. In addition,more realistic modeling techniques such asmodel uncertainties [36] and unknown inputs [37]will be incorporated to improve the practicality of our approach.Another interesting topic is to use the proposed algorithmforsolvingother estimation problems in engineering, such as state of charge estimation of battery storage [38,39] and state estimation in combined heat and power networks [40].…”

Section: Discussionmentioning

confidence: 99%

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

et al. 2019

IEEE Access

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Kalman-type filtering techniques including cubature Kalman filter (CKF) does not work well in non-Gaussian environments, especially in the presence of outliers. To solve this problem, Huber's M-estimation based robust CKF (RCKF) is proposed for synchronous machines by combining the Huber's M-estimation theorywith the classical CKF,which is capable of coping with the deterioration in performance and discretization of tracking curves when measurement noise statistics deviatefrom the prior noise statistics. The proposed RCKF algorithm has good adaptability to unknown measurement noise statistics characteristics including non-Gaussian measurement noise and outliers. The simulation results on the WSCC 3-machine 9-bus system and New England 16-machine 68-bus system verify the effectiveness of the proposed method and its advantage over the classical CKF.INDEX TERMSDynamic state estimation, cubature Kalman filter, synchronous machines,M-estimation theory,unknown noise statistics, non-Gaussian noise, outliers, PMU data.

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Section: Discussionmentioning

confidence: 99%

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

et al. 2019

IEEE Access

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“…Hence the idea is to consider the usual MPC equipped with a robust Kalman filter whose predictions take into account the fact that the employed model is just an approximation of an accurate but complex (known or unknown) model of the actual process. More specifically in this paper we explore the use of the robust Kalman filter proposed in [17], see also [10,11,16,35]. According to this approach, all possible incremental models belong to a ball which is formed by placing a bound on the Kullback-Leibler divergence (or possibly other divergences, [31,32]) between the actual and the nominal incremental model.…”

Section: Introductionmentioning

confidence: 99%

On the coupling of model predictive control and robust Kalman filtering

Zenere

Zorzi

2018

IET Control Theory & Applications

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Model Predictive Control (MPC) represents nowadays one of the main methods employed for process control in industry. Its strong suits comprise a simple algorithm based on a straightforward formulation and the flexibility to deal with constraints. On the other hand it can be questioned its robustness regarding model uncertainties and external noises. Thus, a lot of efforts have been spent in the past years into the search of methods to address these shortcomings. In this paper we propose a robust MPC controller which stems from the idea of adding robustness in the prediction phase of the algorithm while leaving the core of MPC untouched. More precisely, we consider a robust Kalman filter that has been recently introduced and we further extend its usability to feedback control systems. Overall the proposed control algorithm allows to maintain all of the advantages of MPC with an additional improvement in performance and without any drawbacks in terms of computational complexity. To test the actual reliability of the algorithm we apply it to control a servomechanism system characterized by nonlinear dynamics.

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“…The basic idea of fault detection is to construct a residual based on the system model and then evaluate it to determine whether there is a fault in the system. The model uncertainty and time delay are inevitable in practice, considerable research efforts have been devoted to studying robust filtering and fault detection problems …”

Section: Introductionmentioning

confidence: 99%

“…The model uncertainty and time delay 5,6 are inevitable in practice, considerable research efforts have been devoted to studying robust filtering and fault detection problems. [7][8][9][10][11][12][13][14] Meanwhile, the event-triggered mechanism has received enhanced research interest due to the rapid development of network and communication technology in recent years. For the conventional periodic mechanism, the signal will be 4666…”

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confidence: 99%

Event‐triggered filtering and intermittent fault detection for time‐varying systems with stochastic parameter uncertainty and sensor saturation

Zhang

Christofides

et al. 2018

Intl J Robust & Nonlinear

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Summary In this paper, the event‐triggered filtering and intermittent fault detection problems are investigated for a class of time‐varying systems with stochastic parameter uncertainty and sensor saturation. Due to the existence of event‐triggered mechanism, the measured signal could be transmitted only when it satisfies the triggering condition. An event‐triggered filter is developed, which takes the event‐triggered mechanism, parameter uncertainty, and sensor saturation into full consideration but does not depend on any specific uncertainty structure. By utilizing the inductive and stochastic analysis technique, the filter gain is designed to ensure that the upper bound of the estimation error covariance is minimized at each time step. Based on the proposed filter, a residual is generated and the corresponding evaluation function and detection threshold are given to achieve fault detection. At last, two simulation studies are carried out to demonstrate the effectiveness and applicability of the proposed method.

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Robust Kalman Filtering Under Model Perturbations

Cited by 102 publications

References 22 publications

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

Robust Cubature Kalman Filter for Dynamic State Estimation of Synchronous Machines Under Unknown Measurement Noise Statistics

On the coupling of model predictive control and robust Kalman filtering

Event‐triggered filtering and intermittent fault detection for time‐varying systems with stochastic parameter uncertainty and sensor saturation

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