Distributed State Estimation for Continuous-Time Linear Systems With Correlated Measurement Noise

Duan, Peihu; Qian, Jiachen; Wang, Qingyun; Duan, Zhisheng; Shi, Ling

doi:10.1109/tac.2022.3165425

Cited by 17 publications

(3 citation statements)

References 40 publications

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“…The quadrotor controller and differentiator parameters are shown in Table 1. The filter matrices in (17) and (41) are denoted as…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In [16], an adaptive law is introduced to estimate uncertain dynamic and motion parameters, thus improving the robustness of the system. In [17], a distributed state estimation algorithm based on an adaptive approach is proposed for the state estimation problem of continuous linear systems with correlated measurement noise. Similarly, adaptive methods can also improve the performance of quadrotor control systems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty and disturbance estimator‐based geometry tracking control for quadrotors without linear velocity measurements

Hu,

Miao,

Wang

et al. 2023

Asian Journal of Control

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This paper addresses the trajectory tracking control problem of a quadrotor under uncertain model parameters, external disturbances, and unmeasurable linear velocity. The main contribution of this paper is to present a geometric control strategy with a simple structure and easily adjustable parameters, which combines the differentiator and uncertainty and disturbance estimator techniques to achieve global trajectory tracking control. First, a cascade control framework is introduced to solve the strong coupling and underactuated problems of quadrotors. Furthermore, an antidisturbance outer‐loop differentiator‐based position controller is designed based on the uncertainty and disturbance estimators (UDE) to achieve accurate quadrotor tracking of the desired trajectory. The higher order control inputs of the translational dynamics are estimated using a differentiator to determine the desired angular velocity and its derivative. Then, a robust inner‐loop geometric controller is derived in Lie algebraic space using the rotation matrix. After that, the stability analysis of the closed‐loop system is executed using the Lyapunov stability theory. Finally, the numerical simulation verifies that the method enables the quadrotor to have strong robustness and excellent tracking capability in the presence of large initial angles, time‐varying disturbances, and unmeasured linear velocity.

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“…The quadrotor controller and differentiator parameters are shown in Table 1. The filter matrices in (17) and (41) are denoted as…”

Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty and disturbance estimator‐based geometry tracking control for quadrotors without linear velocity measurements

Hu,

Miao,

Wang

et al. 2023

Asian Journal of Control

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show abstract

“…To name a few approaches, Olfati-Saber [12] first formulated a framework of consensus-on-measurement-based distributed filtering (CMDF), where each sensor obtains the average of the global measurement information at every sampling instant through sufficient fusion with neighbors. Kamal et al [15] and Battistelli et al [16], [17] proposed some distributed information fusion techniques such as consensus on information matrix, hybrid consensus on measurement and information matrix, where the applied covariance intersection operation can guarantee the stability of the filter with a low fusion step L. Duan et al [34] proposed CMDF technique for continuous systems with time-correlated noise and gave the asymptotic optimality analysis.…”

Section: B Revisit Consensus-based Distributed Filteringmentioning

confidence: 99%

Observation of Periodic Systems: Bridge Centralized Kalman Filtering and Consensus-Based Distributed Filtering

Qian¹,

Duan²,

Duan³

et al. 2023

Preprint

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Compared with linear time invariant systems, linear periodic system can describe the periodic processes arising from nature and engineering more precisely. However, the time-varying system parameters increase the difficulty of the research on periodic system, such as stabilization and observation. This paper aims to consider the observation problem of periodic systems by bridging two fundamental filtering algorithms for periodic systems with a sensor network: consensus-on-measurement-based distributed filtering (CMDF) and centralized Kalman filtering (CKF). Firstly, one mild convergence condition based on uniformly collective observability is established for CMDF, under which the filtering performance of CMDF can be formulated as a symmetric periodic positive semidefinite (SPPS) solution to a discrete-time periodic Lyapunov equation. Then, the closed form of the performance gap between CMDF and CKF is presented in terms of the information fusion steps and the consensus weights of the network. Moreover, it is pointed out that the estimation error covariance of CMDF exponentially converges to the centralized one with the fusion steps tending to infinity. Altogether, these new results establish a concise and specific relationship between distributed and centralized filterings, and formulate the trade-off between the communication cost and distributed filtering performance on periodic systems. Finally, the theoretical results are verified with numerical experiments.

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Event‐Triggered Distributed Resilient State Estimator for Nonlinear Cyber‐Physical Systems Under Hybrid Cyberattacks

Wu,

Zhang

2024

Adaptive Control & Signal

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This article investigates the problem of event‐triggered distributed resilient state estimator (ET‐DRSE) for nonlinear cyber‐physical systems (NCPSs) under hybrid cyberattacks. A stochastic model for hybrid cyberattacks is constructed by considering both false data injection (FDI) and Denial‐of‐Service (DoS) attacks. By proposing an event‐triggered mechanism, the network burden is reduced by decreasing the transmission rate of measurement information. To improve the flexibility, a detector‐based distributed resilient state estimator framework is derived. Coupled Riccati‐like difference equations (CRDEs) are proposed to obtain an upper bound (UB) of the estimation error covariance (EEC) of every subsystem. An estimation gain is designed to minimize the UB upon the error covariance of the resilient estimator by recursively solving the CRDEs. It is proved that the estimation error is probabilistically bounded through the application of random analytic theory. Finally, the performance of the proposed ET‐DRSE is verified through a numerical example and a chemical process example.

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Distributed State Estimation for Continuous-Time Linear Systems With Correlated Measurement Noise

Cited by 17 publications

References 40 publications

Uncertainty and disturbance estimator‐based geometry tracking control for quadrotors without linear velocity measurements

Uncertainty and disturbance estimator‐based geometry tracking control for quadrotors without linear velocity measurements

Observation of Periodic Systems: Bridge Centralized Kalman Filtering and Consensus-Based Distributed Filtering

Event‐Triggered Distributed Resilient State Estimator for Nonlinear Cyber‐Physical Systems Under Hybrid Cyberattacks

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