Adaptive robust extended Kalman filter for nonlinear stochastic systems

Xiong, Kai; Zhang, H.; Liu, L.

doi:10.1049/iet-cta:20070096

Cited by 100 publications

(71 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, adaptive Extended Kalman Filter is used for online estimation of covariance matrices but it is useful in some cases. Then, the Robust Extended Kalman Filter is used because it is easy to use and there should be no any complicated computation procedures [7]. Kandepu Van der Pol in which behavior and robustness are compared.…”

Section: Extended Kalman Filtermentioning

confidence: 99%

A Review on Tuning of Extended Kalman Filter using Optimization Techniques for State Estimation

Kaur¹,

Kaur²

2016

IJCA

View full text Add to dashboard Cite

State estimation is the common problem in every area of engineering. There are different filters used to overcome the problem of state estimation like Kalman filter, Particle filters etc. Kalman Filter is popular when the system is linear but when the system is highly non-linear then the different derivatives of Kalman Filter are used like Extended Kalman Filter (EKF), Unscented Kalman filter. But these estimation techniques require tuning of process and noise covariance matrices. The different optimization techniques are used to tune the filter parameters of EKF. In this paper, various optimization techniques have been studied for non-linear state estimation based on EKF.

show abstract

Section: Extended Kalman Filtermentioning

confidence: 99%

A Review on Tuning of Extended Kalman Filter using Optimization Techniques for State Estimation

Kaur¹,

Kaur²

2016

IJCA

View full text Add to dashboard Cite

show abstract

“…REKF is designed in such a way, that the relation between process/measurement noise and estimation error is less than the tuning parameter ξ described in Table I. Therefore, ξ states the relative weight given to reduce the variation of the estimation error due to process or measurement noise fluctuations and chosen in such a way, that the performance and robustness is balanced [3], [14]. When = 0 (no measurement noise), the error covariance becomes [3] …”

Section: A Design Of Robust Extended Kalman Filtermentioning

confidence: 99%

Adaptive On-line State-of-available-power Prediction of Lithium-ion Batteries

Fleischer¹,

Waag²,

Bai³

et al. 2013

Journal of Power Electronics

View full text Add to dashboard Cite

This paper presents a new overall system for state-of-available-power (SoAP) prediction for a lithium-ion battery pack. The essential part of this method is based on an adaptive network architecture which utilizes both fuzzy model (FIS) and artificial neural network (ANN) into the framework of adaptive neuro-fuzzy inference system (ANFIS). While battery aging proceeds, the system is capable of delivering accurate power prediction not only for room temperature, but also at lower temperatures at which power prediction is most challenging. Due to design property of ANN, the network parameters are adapted on-line to the current battery states (state-of-charge (SoC), state-of-health (SoH), temperature). SoC is required as an input parameter to SoAP module and high accuracy is crucial for a reliable on-line adaptation. Therefore, a reasonable way to determine the battery state variables is proposed applying a combination of several partly different algorithms. Among other SoC boundary estimation methods, robust extended Kalman filter (REKF) for recalibration of amp hour counters was implemented. ANFIS then achieves the SoAP estimation by means of time forward voltage prognosis (TFVP) before a power pulse occurs. The trade-off between computational cost of batch-learning and accuracy during on-line adaptation was optimized resulting in a real-time system with TFVP absolute error less than 1%. The verification was performed on a software-in-the-loop test bench setup using a 53 Ah lithium-ion cell.

show abstract

“…However, the error curves also fluctuate widely during the maneuver. On the other hand, it is specified in (Xiong et al, 2008) the AEKF is somewhat less accurate than the standard EKF when there are no disturbances in dynamic model, for the noise covariance matrix is reset to the estimated value t Q in every step of the filter, and t Q which may deviate from its true value due to the inaccuracy of the state estimate. In addition, to implement the adaptive EKF, many tuning parameters (such k, N and the elements in matrices A and B) have to be designed.…”

Section: Fig 3 Estimation Error Of the Rekfmentioning

confidence: 99%