Robust weighted fusion Kalman filters for multisensor time-varying systems with uncertain noise variances

Qi, Wenjuan; Zhang, Peng; Deng, Zili

doi:10.1016/j.sigpro.2013.12.013

Cited by 126 publications

(153 citation statements)

References 30 publications

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“…According to the method given in literature [8], the Kalman filter is designed to estimate the torsion angle based on the measured motor speed and wheel speed. The estimated formula is shown in (13).…”

Section: A Kalman Filtermentioning

confidence: 99%

MPC Controller Design For Low Frequency Vehicle Longitudinal Vibration Suppression

Zhang

Huang²,

Dai

et al. 2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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Abstract-The low frequency longitudinal vibration is one of the key factors that affect the drivability. Therefore, the analysis and control of the low frequency longitudinal vibration of the vehicle has always been a key issue in the vehicle Vibration and Harshness (NVH). In this paper, a new active control strategy for motor torque compensation is proposed to apply to the driveline's counterforce so as to realize the active suppression of low frequency vehicle longitudinal vibration. In the MPC controller, reasonable parameter selection can improve not only the system's closed-loop performance, but also its stability and robustness. Based on the design of vehicle low frequency longitudinal vibration controller, we choose and design some important control parameters (e.g., prediction time domain, control time domain and weighting factor) to assure the MPC controller achieve the best performance. Besides, we also study the impact of different control parameters by its results, finding out the general impact of the law. The simulation results show that the variable weights MPC controller can achieve better performance by selecting MPC controller parameters reasonably.

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Section: A Kalman Filtermentioning

confidence: 99%

MPC Controller Design For Low Frequency Vehicle Longitudinal Vibration Suppression

Zhang

Huang²,

Dai

et al. 2017

Proceedings of the 2017 2nd International Conference on Control, Automation and Artificial Intelligence (CAAI 2017)

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“…The resource-constrained networked systems are influenced by noise disturbances, which are generated by the communication facilities and geographical 15 location of sensor nodes. When the parameter uncertainties exist in the systems, the estimation performance would be deteriorated.…”

Section: Introductionmentioning

confidence: 99%

“…The cross-correlation between the measurement noise and process noise are discussed in [12,13,14]. For noise sequences with uncertain variances, the actual filtering error variances [15,16,17] are obtained with a minimal upper bound for all admissible uncertainties. However, the generation mechanism of 30 multi-step cross-correlation for noises is not clearly analyzed.…”

Section: Introductionmentioning

confidence: 99%

Distributed weighted fusion estimation for uncertain networked systems with transmission time-delay and cross-correlated noises

Liu

Yang

et al. 2017

Neurocomputing

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This paper investigates the state estimation issue for uncertain networked systems considering data transmission time-delay and cross-correlated noises. A distributed robust Kalman filtering-based perception and centralized fusion method is proposed to improve the estimation accuracy from perturbed measurement; consequently, reduce the amount of redundant information and alleviate the estimation burden. To describe the transmission time-delay and give rise to cross-correlated and state-dependent noises in the exchange measurement among neighbors, a weighted fusion reorganized innovation strategy is proposed to reduce the computational burden and suppress noise effect. Moreover, to obtain the optimal linear estimate, a fusion estimation approach is used for information collaboration by weighting the error cross-covariance matrices. Finally, an illustrative example is presented to demonstrate the effectiveness and robustness of the proposed method.

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“…Recently, for a system with uncertain noise variances, according to the minimax robust estimation principle, 4 based on the worst-case conservative system with conservative upper bounds of uncertain noise variances, the robust local and fused Kalman filters, 5 predictors, 6 smoothers, 7 and white noise deconvolution smoothers 8 have been presented by the Lyapunov equation approach, respectively, in the sense that the actual estimation error variances of each estimator are guaranteed to have a minimal upper bound for all the admissible uncertainties. However, only the uncertain noise variances were considered in previous works, [5][6][7][8] whereas the model parameters of the considered system were all assumed to be exactly known and the correlated noises were not considered in other works. [2][3][4][5][6][7][8] However, in many engineering applications, the correlated noises often arise.…”

Section: Introductionmentioning

confidence: 99%

Robust time‐varying Kalman estimators for systems with packet dropouts and uncertain‐variance multiplicative and linearly correlated additive white noises

Yang

Deng

2017

Adaptive Control & Signal

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SummaryThis paper is concerned with robust estimation problem for a class of time-varying networked systems with uncertain-variance multiplicative and linearly correlated additive white noises, and packet dropouts. By augmented state method and fictitious noise technique, the original system is converted into one with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst-case system with conservative upper bounds of uncertain noise variance, the robust time-varying Kalman estimators (filter, predictor, and smoother) are presented. A unified approach of designing the robust Kalman estimators is presented based on the robust Kalman predictor.

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Robust weighted fusion Kalman filters for multisensor time-varying systems with uncertain noise variances

Cited by 126 publications

References 30 publications

MPC Controller Design For Low Frequency Vehicle Longitudinal Vibration Suppression

MPC Controller Design For Low Frequency Vehicle Longitudinal Vibration Suppression

Distributed weighted fusion estimation for uncertain networked systems with transmission time-delay and cross-correlated noises

Robust time‐varying Kalman estimators for systems with packet dropouts and uncertain‐variance multiplicative and linearly correlated additive white noises

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