No abstract

No abstract

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.

With the widespread application of electric vehicles, the study of the power lithium-ion battery (LIB) has broad prospects and great academic significance. The state of charge (SOC) is one of the key parts in battery management system (BMS), which is used to provide guarantee for the safe and efficient operation of LIB. To obtain the reliable SOC estimation result under the influence of simple model and measurement noise, a novel estimation method with adaptive feedback compensator is presented in this paper. The simplified dynamic external electrical characteristic of LIB is represented by the one-order Thevenin equivalent circuit model (ECM) and then the ECM parameters are identified by the forgetting factor recursive least squares method (FFRLS). Fully taking into account the feedback effect of terminal voltage innovation, the combination of adaptive extended Kalman filter (AEKF) and innovation vector-based proportional-integral-derivative (PID) feedback is proposed to estimate the LIB SOC. The common single proportional feedback of Kalman filter (KF) is replaced by the innovation vector-based PID feedback, which means that the multiple prior terminal voltage innovation is used in the measurement correction step of KF. The results reveal that the AEKF with PID feedback compensation strategy can improve the SOC estimation performance compared with the common AEKF, and it reveals good robust capability and rapid convergence speed for initial SOC errors. The maximum absolute error and average absolute error for SOC estimation are close to 4% and 2.6%, respectively.

This paper is concerned with guaranteed cost robust weighted measurement fusion (WMF) estimation problem for multisensor system with both uncertain noise variances and missing measurements. By introducing the fictitious measurement white noises, the original multisensor system is converted into one only with uncertain noise variances. Two classes of guaranteed cost robust WMF Kalman estimators (predictor, filter and smoother) are presented by the Lyapunov equation approach, based on the minimax robust estimation principle and the parameterization representation of uncertain noise variance perturbations. The maximal lower bound and minimal upper bound of actual accuracy deviations are given. A unified approach of designing the robust WMF Kalman estimators is presented based on the robust WMF Kalman predictor. A simulation example shows the correctness and effectiveness of the proposed results.

No abstract

No abstract

The relationship between med-long term load forecasting and socio-economic