Global Stability of Backsteping Control with Robust Nonlinear Observer of Induction Motor in 􁈺􀢻, 􀢼􁈻 Frame

2019 ◽  
Vol 1 (1) ◽  
pp. 193-198
Author(s):  
Amine CHENAFA
Keyword(s):  
Global Stability ◽  
Induction Motor ◽  
Nonlinear Observer ◽  
Robust Nonlinear Observer

scholarly journals A Robust Nonlinear Observer for Real-Time Attitude Estimation Using Low-Cost MEMS Inertial Sensors

Sensors ◽  
2013 ◽  
Vol 13 (11) ◽  
pp. 15138-15158 ◽  
Author(s):  
José Guerrero-Castellanos ◽  
Heberto Madrigal-Sastre ◽  
Sylvain Durand ◽  
Lizeth Torres ◽  
German Muñoz-Hernández
Keyword(s):  
Real Time ◽  
Inertial Sensors ◽  
Low Cost ◽  
Attitude Estimation ◽  
Nonlinear Observer ◽  
Robust Nonlinear Observer

Nonlinear Observer for Induction Motor: A Benchmark Test

2000 ◽  
Vol 33 (13) ◽  
pp. 153-158 ◽  
Author(s):  
J. DeLeon ◽  
A. Glumineau ◽  
I. Souleiman
Keyword(s):  
Induction Motor ◽  
Nonlinear Observer ◽  
Benchmark Test

A Robust Nonlinear Observer for State Variables Estimation in Multi-Input Multi-Output Chemical Reactors

2008 ◽  
Vol 6 (1) ◽  
Author(s):  
Bachir Daaou ◽  
Abdellah Mansouri ◽  
Mohamed Bouhamida ◽  
Mohammed Chenafa
Keyword(s):  
Model Uncertainty ◽  
Chemical Reactor ◽  
Measurement Noise ◽  
Nonlinear Observer ◽  
State Variables ◽  
Robust Performance ◽  
Chemical Reactors ◽  
On Line ◽  
Error System ◽  
Robust Nonlinear Observer

In this paper we tackle the on-line estimation of state variables in MIMO continuous stirred chemical reactor (CSTR) using a nonlinear observer. We prove the asymptotic stability of the resulting error system. Moreover, this observer has robust performance in the presence of model uncertainty and measurement noise. Finally, computer simulations are developed for showing the performance of the proposed nonlinear observer.

scholarly journals A comparative study of nonlinear circle criterion based observer and H∞ observer for induction motor drive

2019 ◽  
Vol 10 (3) ◽  
pp. 1229
Author(s):  
Farid Berrezzek ◽  
Wafa Bourbia ◽  
Bachir Bensaker
Keyword(s):  
Comparative Study ◽  
Induction Motor ◽  
Observer Design ◽  
Nonlinear Observer ◽  
Linear Matrix ◽  
Optimization Approach ◽  
Lmi Optimization ◽  
Circle Criterion ◽  
True State ◽  
The Stability

<span lang="EN-US">This paper deals with a comparative study of circle criterion based nonlinear observer<em> </em>and <em>H<sub>∞</sub></em> observer for induction motor (IM) drive. The  advantage of the circle criterion approach for nonlinear observer design is that it directly handles the nonlinearities of the system with less restriction  conditions in contrast of the other methods which attempt to eliminate them. However the <em>H<sub>∞</sub></em> observer guaranteed the stability taking into account disturbance and noise attenuation. Linear matrix inequality (LMI) optimization approach is used to compute the gains matrices for the two observers. The simulation results show the superiority of <em>H<sub>∞</sub></em> observer in the sense that it can achieve convergence to the true state, despite the nonlinearity of model and the presence of disturbance.</span>

scholarly journals A novel nonlinear observer for fault diagnosis of induction motor

2020 ◽  
Vol 14 ◽  
pp. 174830262092272
Author(s):  
Lingzhi Yi ◽  
Yue Liu ◽  
Wenxin Yu ◽  
Jian Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Induction Motor ◽  
Bp Neural Network ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Adaptive Observer ◽  
Nonlinear Observer ◽  
Feedback Gain

In order to accurately diagnose the fault of induction motor, a fault diagnosis of nonlinear observer method based on BP neural network and Cuckoo Search algorithm is proposed. It is a new method which mixes analytical model and artificial neural network; firstly, the induction motor model is divided into linear and nonlinear parts, and BP neural network is used to approximate the nonlinear part. Then an adaptive observer is established, in which a simple and effective method for selecting the feedback gain matrix is offered. Cuckoo Search algorithm is utilized to improve the convergence speed and approximation accuracy in BP Neural Network. Compared with some other algorithms, the simulation results show that the proposed method has higher prediction accuracy. The designed nonlinear observer can estimate the current and speed accurately. Finally, the experiment of winding fault is implemented, and the online fault detection of induction motor is realized by analyzing the current residual errors.

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