Nonlinear control and estimation in induction machine using state estimation techniques

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

In this paper, several techniques are addressed for both estimation and control to be integrated into a unified closed-loop or feedback control system that is applicable for a general family of nonlinear control structures. The estimation techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). Specifically, two comparative studies are performed. In the first comparative study, the state variables are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square errors with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of the number of estimated model parameters on the accuracy and convergence of these techniques is also assessed. The results of both comparative studies show that the UKF provides a higher accuracy than the EKF, due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the PF provides a significant improvement over the UKF and EKF and can still provide both convergence as well as accuracy-related advantages over other estimation methods. This is because the covariance is propagated through linearization of the underlying nonlinear model, when the state transition and observation models are highly nonlinear.

Original languageEnglish
Pages (from-to)642-654
Number of pages13
JournalSystems Science and Control Engineering
Volume2
Issue number1
DOIs
Publication statusPublished - 1 Jan 2014

Fingerprint

Induction Machine
Nonlinear Estimation
Nonlinear Control
State Estimation
State estimation
Kalman Filter
Extended Kalman filters
Kalman filters
Comparative Study
Particle Filter
Nonlinear Model
Covariance matrix
Linearization
Mean square error
Feedback control
Nonlinear Process
Closed-loop Control
Feedback Systems
State Transition
Feedback Control

Keywords

  • Induction machine
  • Nonlinear control
  • Particle filter
  • States and parameters estimation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering
  • Control and Optimization

Cite this

@article{21dd74a4d2684966b84bed52127b835c,
title = "Nonlinear control and estimation in induction machine using state estimation techniques",
abstract = "In this paper, several techniques are addressed for both estimation and control to be integrated into a unified closed-loop or feedback control system that is applicable for a general family of nonlinear control structures. The estimation techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). Specifically, two comparative studies are performed. In the first comparative study, the state variables are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square errors with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of the number of estimated model parameters on the accuracy and convergence of these techniques is also assessed. The results of both comparative studies show that the UKF provides a higher accuracy than the EKF, due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the PF provides a significant improvement over the UKF and EKF and can still provide both convergence as well as accuracy-related advantages over other estimation methods. This is because the covariance is propagated through linearization of the underlying nonlinear model, when the state transition and observation models are highly nonlinear.",
keywords = "Induction machine, Nonlinear control, Particle filter, States and parameters estimation",
author = "Majdi Mansouri and Hazem Nounou and Mohamed Nounou",
year = "2014",
month = "1",
day = "1",
doi = "10.1080/21642583.2014.956842",
language = "English",
volume = "2",
pages = "642--654",
journal = "Systems Science and Control Engineering",
issn = "2164-2583",
publisher = "Taylor and Francis Ltd.",
number = "1",

}

TY - JOUR

T1 - Nonlinear control and estimation in induction machine using state estimation techniques

AU - Mansouri, Majdi

AU - Nounou, Hazem

AU - Nounou, Mohamed

PY - 2014/1/1

Y1 - 2014/1/1

N2 - In this paper, several techniques are addressed for both estimation and control to be integrated into a unified closed-loop or feedback control system that is applicable for a general family of nonlinear control structures. The estimation techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). Specifically, two comparative studies are performed. In the first comparative study, the state variables are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square errors with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of the number of estimated model parameters on the accuracy and convergence of these techniques is also assessed. The results of both comparative studies show that the UKF provides a higher accuracy than the EKF, due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the PF provides a significant improvement over the UKF and EKF and can still provide both convergence as well as accuracy-related advantages over other estimation methods. This is because the covariance is propagated through linearization of the underlying nonlinear model, when the state transition and observation models are highly nonlinear.

AB - In this paper, several techniques are addressed for both estimation and control to be integrated into a unified closed-loop or feedback control system that is applicable for a general family of nonlinear control structures. The estimation techniques include the extended Kalman filter (EKF), unscented Kalman filter (UKF), and particle filter (PF). Specifically, two comparative studies are performed. In the first comparative study, the state variables are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square errors with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of the number of estimated model parameters on the accuracy and convergence of these techniques is also assessed. The results of both comparative studies show that the UKF provides a higher accuracy than the EKF, due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the PF provides a significant improvement over the UKF and EKF and can still provide both convergence as well as accuracy-related advantages over other estimation methods. This is because the covariance is propagated through linearization of the underlying nonlinear model, when the state transition and observation models are highly nonlinear.

KW - Induction machine

KW - Nonlinear control

KW - Particle filter

KW - States and parameters estimation

UR - http://www.scopus.com/inward/record.url?scp=84973173968&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84973173968&partnerID=8YFLogxK

U2 - 10.1080/21642583.2014.956842

DO - 10.1080/21642583.2014.956842

M3 - Article

VL - 2

SP - 642

EP - 654

JO - Systems Science and Control Engineering

JF - Systems Science and Control Engineering

SN - 2164-2583

IS - 1

ER -