Centered Error Entropy-Based Sigma-Point Kalman Filter for Spacecraft State Estimation with Non-Gaussian Noise

Yang, Baojian; Huang, Hao Qian; Cao, Lu

doi:10.34133/2022/9854601

Cited by 31 publications

(4 citation statements)

References 47 publications

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“…The obtained results would be applied to the automatic parking, automated warehouse system, and production assembly line. Further work entails investigating the region based tracking control of spacecraft [50,51] or flexible manipulator systems [52], and addressing broader performance aspects.…”

Section: Discussionmentioning

confidence: 99%

A system decomposition method for region tracking control of a non‐holonomic mobile robot with dynamic parameter uncertainties

Yu,

Wu,

Yang

et al. 2023

Asian Journal of Control

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The tracking control problem of non‐holonomic mobile robot systems has been extensively investigated in the past decades, however, most of the existing control strategies were developed specifically for the fixed‐point tracking. This technical note focuses on the region tracking control for a non‐holonomic mobile robot system with parameter uncertainties in the robot dynamics. With the system decomposition and adaptive control method, some restrictions imposed on the angular and linear velocities of the non‐holonomic mobile robot in recent literature are removed, enabling to track dynamic trajectories with any values of the angular and line velocities. The proposed adaptive control scheme can simultaneously solve both the regulation and region tracking problems of a non‐holonomic mobile robot with one passive wheel and two actuated wheels. By utilizing the designed control laws, the mobile robot system is able to globally reach inside a moving region specified by potential functions whose path can be a circular curve, a straight line, or sinusoidal curve, by using a single adaptive controller. Since the dynamic region can be specified arbitrarily small, the fixed‐point tracking can be regarded as a special case of region tracking studied in this paper. Compared with the traditional fixed‐point tracking, region tracking has more flexibility and better robustness. Numerical results are presented to show the effectiveness of the designed strategy.

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Section: Discussionmentioning

confidence: 99%

A system decomposition method for region tracking control of a non‐holonomic mobile robot with dynamic parameter uncertainties

Yu,

Wu,

Yang

et al. 2023

Asian Journal of Control

View full text Add to dashboard Cite

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“…Recently, the information theory and learning (ITL) has been used to deal with non‐Gaussian problems, such as the correntropy method [17], and the centered error entropy (CEE) [18]. Replacing the MMSE, the correntropy KF (C‐filter) for linear non‐Gaussian systems is first proposed [17], but it does not calculate the extension of the covariance matrix.…”

Section: Introductionmentioning

confidence: 99%

Novel iterative cubature Kalman filters under maximum correntropy criterion for the robust state estimation

Lu,

Zhou,

et al. 2024

Asian Journal of Control

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The cubature Kalman filter (CKF) based on maximum correntropy criterion (MCC) is robust under non‐Gaussian noises, but it may encounter numerical problems when there are large outliers. First, based on the MCC and weighted least square (WLS) methods, a new cost function is introduced to calculate the state and covariance updates, which can better solve numerical problems caused by large outliers. Then, a nonlinear measurement function is used, and the measurement information is updated through the latest iterative values, which can obtain more accurate results under various non‐Gaussian noises. In addition, the Gauss–Newton, Levenberg–Marquardt, and Quasi–Newton methods are used, and three iterative algorithms named GN‐IMCC‐CKF, LM‐IMCC‐CKF, and QN‐IMCC‐CKF are respectively derived for the updating steps. Compared with the existing algorithms, the simulation results of two classical models show that the proposed algorithms have better numerical stability and estimation accuracy under non‐Gaussian systems.

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“…The second method based is based on filtering and optimization on of measurement results observed by ground facility facilities. Many data processing algorithms including the modified Kalman filter [8], spatial-based Least Square Estimation [9], multiple model adaptive estimation [10], centered error entropy Unscented Kalman filter [11] and the predictive Attitude Determination algorithm [12] etc. are used to filter out the observation noise tangled in the data.…”

Section: Introductionmentioning

confidence: 99%

Satellite Attitude Identification and Prediction Based on Neural Network Compensation

2023

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This paper proposed a new attitude determination method for low orbit spacecraft. The attitude prediction accuracy is greatly improved by adding the unmodeled environmental torque to the dynamic equation. Specifically, the environmental torque extraction algorithm based on extended Kalman filter (EKF) and series extended state observer is introduced, and the unmodeled part of dynamic is identified through the inverse dynamic model. Then the collected data are analyzed and trained by a back propagation neural network, resulting in an attitude-torque mapping network with compensation ability. The simulation results show that the proposed feedback attitude prediction algorithm can outperform standard methods and provide a high accurate picture of prediction and reliability with discontinuous measurement.

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Centered Error Entropy-Based Sigma-Point Kalman Filter for Spacecraft State Estimation with Non-Gaussian Noise

Cited by 31 publications

References 47 publications

A system decomposition method for region tracking control of a non‐holonomic mobile robot with dynamic parameter uncertainties

A system decomposition method for region tracking control of a non‐holonomic mobile robot with dynamic parameter uncertainties

Novel iterative cubature Kalman filters under maximum correntropy criterion for the robust state estimation

Satellite Attitude Identification and Prediction Based on Neural Network Compensation

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