Convergence analysis of the modified adaptive extended Kalman filter for the parameter estimation of a brushless DC motor

Ding, Jie; Chen, Lijuan; Cao, Zhengxin; Guo, Honghao

doi:10.1002/rnc.5706

Cited by 17 publications

(6 citation statements)

References 60 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then the adaptive filter obtains the best-estimated states in the case of minimum variance from the observation value. The widely used adaptive filters mainly include the extended Kalman filter (EKF), [10][11][12] the unscented Kalman filter (UKF), 13,14 the particle filter (PF), 15,16 and so on. [17][18][19] However, the adaptive filters have high requirements on the precision of the battery model.…”

Section: The Traditional Soc Estimation Methodsmentioning

confidence: 99%

A Fletcher‐Reeves conjugate gradient optimized multi‐reservoir echo state network for state of charge estimation in vehicle battery

Wang

Jiao

2022

Intl J of Energy Research

View full text Add to dashboard Cite

Summary This paper investigates a Fletcher‐Reeves conjugate gradient optimized multi‐reservoir echo state network (FRCG‐MESN) to identify battery state of charge (SOC). First, an echo state network with multiple reservoirs is established to estimate battery SOC by using its discharge terminal voltage and current as the inputs, and the FR conjugate gradient algorithm is explored to tune the output weights of the MESN to avoid calculating the inverse matrix. Second, an appropriate amount of Gaussian noise with zero mean is added to the training set to prevent overfitting. Finally, a battery test platform is adopted to sample the discharging data under two working conditions: the Los Angeles 92 and the urban dynamometer driving schedule. The simulation results show that the presented FRCG‐MESN method can accurately identify the battery SOC with different initial SOCs. Applying an appropriate amount of noise to the training set can prevent overfitting efficiently.

show abstract

Section: The Traditional Soc Estimation Methodsmentioning

confidence: 99%

A Fletcher‐Reeves conjugate gradient optimized multi‐reservoir echo state network for state of charge estimation in vehicle battery

Wang

Jiao

2022

Intl J of Energy Research

View full text Add to dashboard Cite

show abstract

“…The proposed parameter estimation algorithms in this article are based on this identification model. Many identification methods are derived based on the identification models of the systems [37][38][39][40] and these methods can be used to estimate the parameters of other linear systems and nonlinear systems [41][42][43][44] and can be applied to other fields [45][46][47][48][49][50] such as chemical process control systems. There exists the product of the parameter vector b of the CAR model in the forward channel and c of the nonlinear block in the feedback channel in (7) such that the identification model is a typical bilinear-in-parameter model.…”

Section: System Descriptionmentioning

confidence: 99%

Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

Wei

Xiao

et al. 2022

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article deals with the problems of the parameter estimation for feedback nonlinear controlled autoregressive systems (i.e., feedback nonlinear equation‐error systems). The bilinear‐in‐parameter identification model is formulated to describe the feedback nonlinear system. An overall recursive least squares algorithm is developed to handle the difficulty of the bilinear‐in‐parameter. For the purpose of avoiding the heavy computational burden, an overall stochastic gradient algorithm is deduced and the forgetting factor is introduced to improve the convergence rate. Furthermore, the convergence analysis of the proposed algorithms are established by means of the stochastic process theory. The effectiveness of the proposed algorithms are illustrated by the simulation example.

show abstract

“…Linear system identification has been mature [1][2][3][4][5] and many methods have been proposed for nonlinear systems. [6][7][8][9][10] In industrial production and daily life, almost all practical systems are nonlinear.…”

Section: Introductionmentioning

confidence: 99%

Filtering‐based multi‐innovation recursive identification methods for input nonlinear systems with piecewise‐linear nonlinearity based on the optimization criterion

Fan

Liu

2022

Optim Control Appl Methods

View full text Add to dashboard Cite

Piecewise-linear nonlinearity is an effective representation for hard nonlinearities such as saturation, dead zone, and backlash, which can be also used as a general tool for approximating nonlinear characteristics. For the input piecewise-linear output-error autoregressive systems, we develop a parametric expression of the piecewise-linear nonlinearity through a switching function and position functions, and derive the identification model of the system by using the key item separation. Based on the optimization criterion, an auxiliary model-based multi-innovation recursive generalized least-squares algorithm is deduced for estimating the unknown parameters according to the obtained model. Since the system is disturbed by colored noise, we introduce the data filtering technique from a view point of improving the parameter estimation accuracy. The filtering identification model of the system is derived and a filtering-based multi-innovation recursive generalized least-squares algorithm is proposed. The simulation example demonstrates the effectiveness of the proposed algorithms and shows that the filtering-based multi-innovation recursive generalized least-squares algorithm has better identification performance.

show abstract

Convergence analysis of the modified adaptive extended Kalman filter for the parameter estimation of a brushless DC motor

Cited by 17 publications

References 60 publications

A Fletcher‐Reeves conjugate gradient optimized multi‐reservoir echo state network for state of charge estimation in vehicle battery

A Fletcher‐Reeves conjugate gradient optimized multi‐reservoir echo state network for state of charge estimation in vehicle battery

Overall recursive least squares and overall stochastic gradient algorithms and their convergence for feedback nonlinear controlled autoregressive systems

Filtering‐based multi‐innovation recursive identification methods for input nonlinear systems with piecewise‐linear nonlinearity based on the optimization criterion

Contact Info

Product

Resources

About