Robust state estimation for uncertain linear discrete systems with d‐step state delay

Wang, Jing; Mao, Yao; Li, Ziqiang; Gao, Junwei; Liu, Huabo

doi:10.1049/cth2.12153

Cited by 4 publications

(3 citation statements)

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“…As we can see from Figure 4, based on the ETM, the MHER and OLER2 in the state estimator operate alternately and efficiently, thus indicating that the efficiency of state estimation has been improved. To demonstrate the good performance of the proposed method (EHME method), this paper compares the EMHE method with the H ∞ Extended Kalman Filter method (HEKF method) proposed in [9] and the robust state estimation method (RBSE method) proposed in [3]. The state estimation results output by the EHME method, the HEKF method, and the RBSE method are shown in Figures 5 and 6, while the state estimation error of the three methods is presented in Figure 7.…”

Section: Simulation Examplementioning

confidence: 99%

“…In recent years, state estimation theory has been extensively investigated [1][2][3]. Among the various state estimation methods, the moving horizon estimation (MHE) method continues to attract an increasing amount of attention because it can explicitly deal with constraints and limit the solutions of optimization problems to a fixed window length [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Event-Triggered State Estimation for Uncertain Systems with Binary Encoding Transmission Scheme

Li,

Xue,

Chen

2023

Mathematics

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This paper proposes an event-triggered state estimation method for parameter-uncertain systems with a binary encoding transmission scheme. Firstly, a binary encoding transmission scheme is introduced between the state estimator and the system to improve the efficiency of network communication. Secondly, an event-triggering mechanism (ETM) is designed to ensure the accuracy of state estimation and reduce the computational burden of the state estimator. At the event-triggered moments, considering the uncertainty of the system, the binary encoding transmission scheme, and the ETM, a moving horizon estimator (MHER) is designed using the robust least squares optimization method to obtain optimal state estimation. At the no-event-triggered moments, the state estimation of the system is computed based on an open-loop state estimator (OLER). Furthermore, stability analysis showed that the state estimation error of the proposed method is bounded. Finally, the practical value of the proposed in this paper is confirmed through numerical simulation.

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Section: Simulation Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Event-Triggered State Estimation for Uncertain Systems with Binary Encoding Transmission Scheme

Li,

Xue,

Chen

2023

Mathematics

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“…One is the state time delay (Tang et al, 2017) coupled with the nonlinear aerodynamic drag. The iterated extended Kalman filter (IEKF) (Moradi and Mohseni, 2022; Pinsard et al, 2018) and derivative-free cubature Kalman filter (CKF) (Basetti et al, 2022) have been proposed for Kalman filtering of nonlinear systems, and some Kalman filters have been designed for linear time-delay systems (Tsai et al, 2019; Wang et al, 2021). However, these studies only partially considered the nonlinearity or state delay of the system, and they still need to be improved to address the nonlinear dynamics of HSTs with time delays.…”

Section: Introductionmentioning

confidence: 99%

Optimized adaptive filtering for fault estimation of the dynamics of high-speed train based on robust extended Kalman filter

Liang

Wang

et al. 2023

Transactions of the Institute of Measurement and Control

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In this paper, an optimized adaptive robust extended Kalman filter is proposed based on random weighting factors and an improved whale optimization algorithm for fault estimation of the dynamics of high-speed trains with constant time delays, drastically changing noise and stochastic uncertainties. Robust upper bounds are proposed to improve the performance of the extended Kalman filter by decreasing the influence of the linearization error on filtering for the dynamics of high-speed trains with constant time delays, and its robustness is proven to guarantee the feasibility of the proposed upper bounds. Furthermore, considering drastically changing noise with unknown statistics, a random weighting adaptive algorithm is proposed to implement unbiased noise estimation so that the robust extended Kalman filter can still be implemented well. In addition, a differential evolution algorithm and adaptive parameter are introduced to improve the performance of the whale optimization algorithm so that the stochastic uncertainties are optimized, and the influence of the stochastic uncertainties on filtering is further decreased. The simulation results in the three conditions show that, compared with the variational Bayes adaptive iterated extended Kalman filter, using the proposed method, the position, speed and fault estimation errors are decreased by 31.8%, 33.2% and 28.3%, respectively, on average, which depends on more accurate noise estimation.

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Robust State Estimation for Uncertain Discrete Linear Systems with Delayed Measurements

Sun

Duan

et al. 2022

Mathematics

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Measurement delays and model parametric uncertainties are meaningful issues in actual systems. Addressing the simultaneous existence of random model parametric uncertainties and constant measurement delay in the discrete-time linear systems, this study proposes a novel robust estimation method based on the combination of Kalman filter regularized least-squares (RLS) framework and state augmentation. The state augmentation method is elaborately designed, and the cost function is improved by considering the influence of modelling errors. A recursive program similar to the Kalman filter is derived. Meanwhile, the asymptotic stability conditions of the proposed estimator and the boundedness conditions of its error covariance are analyzed theoretically. Numerical simulation results show that the proposed method has a better processing capability for measurement delay and better robustness to model parametric uncertainties than the Kalman filter based on nominal parameters.

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Robust state estimation for uncertain linear discrete systems with d‐step state delay

Cited by 4 publications

References 50 publications

Event-Triggered State Estimation for Uncertain Systems with Binary Encoding Transmission Scheme

Event-Triggered State Estimation for Uncertain Systems with Binary Encoding Transmission Scheme

Optimized adaptive filtering for fault estimation of the dynamics of high-speed train based on robust extended Kalman filter

Robust State Estimation for Uncertain Discrete Linear Systems with Delayed Measurements

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