scholarly journals Identification by Recursive Least Squares with Kalman Filter (RLS-KF) Applied to a Robotic Manipulator

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Darielson A. Souza ◽  
Josias G. Batista ◽  
Felipe J. S. Vasconcelos ◽  
Laurinda L. N. Dos Reis ◽  
Gabriel F. Machado ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Robotic Manipulator ◽  
Recursive Least Squares

Sensor-based estimation of BTEX concentrations in water samples using recursive least squares and Kalman filter techniques

2016 ◽  
Author(s):  
Karthick Sothivelr ◽  
Florian Bender ◽  
Fabien Josse ◽  
Edwin E. Yaz ◽  
Antonio J. Ricco
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Water Samples ◽  
Recursive Least Squares

scholarly journals Online parameter identification of synchronous machines using Kalman filter and recursive least squares

2019 ◽  
Author(s):  
Erick Alves ◽  
Jonas Noeland ◽  
Giancarlo Marafioti ◽  
Geir Mathisen
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Least Squares ◽  
Electrical Power ◽  
Recursive Least Squares ◽  
Synchronous Machines ◽  
Online Assessment ◽  
Machine Model ◽  
Salient Pole ◽  
Saturation Effects

This paper investigates and implements a procedure<br>for parameter identification of salient pole synchronous machines that is based on previous knowledge about the equipment and can be used for condition monitoring, online assessment of the electrical power grid, and adaptive control. It uses a Kalman filter to handle noise and correct deviations in measurements caused by uncertainty of instruments or effects not included in the model.<br>Then it applies a recursive least squares algorithm to identify<br><div>parameters from the synchronous machine model. Despite being affected by saturation effects, the proposed procedure estimates 8 out of 13 parameters from the machine model with minor deviations from data sheet values and is largely insensitive to noise and load conditions.</div><div><br></div><div>Submitted to IEEE IECON 2019.<br></div>

scholarly journals Identification of a synchronous generator parameters using recursive least squares and Kalman filter

2021 ◽  
Vol 12 (1) ◽  
pp. 13-21
Author(s):  
Diego Alberto Bravo Montenegro ◽  
Carlos Felipe Rengifo ◽  
Cristian Giron ◽  
Jhon Palechor
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Synchronous Generator ◽  
Measured Data ◽  
Operating Conditions ◽  
Recursive Least Squares ◽  
Phase Voltage ◽  
Three Phase ◽  
Better Than

The comparison between recursive least squares (RLS) and Kalman filter (KF) is presented in this paper, both methods were adequate to estimate six parameters of a synchronous machine. The work focused on finding the operating conditions which the quality of the identification achieved with Kalman filter is better than recursive least squares. A linear model of the machine is used in order to considerate the currents and their derivatives as the system inputs while the three-phase voltage signals are the outputs. Furthermore two experiments with simulated and measured data were carried out, three operating scenarios and two variations of the algorithms respectively were considered. Despite the great similarity and good performance of both methods, it was found that Kalman filter slightly exceeded least squares due to the fact that it presented smaller oscillations in the estimated value of the parameters for any operating condition.

scholarly journals Online parameter identification of synchronous machines using Kalman filter and recursive least squares

2019 ◽  
Author(s):  
Erick Alves ◽  
Jonas Noeland ◽  
Giancarlo Marafioti ◽  
Geir Mathisen
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Least Squares ◽  
Electrical Power ◽  
Recursive Least Squares ◽  
Synchronous Machines ◽  
Online Assessment ◽  
Machine Model ◽  
Salient Pole ◽  
Saturation Effects

This paper investigates and implements a procedure<br>for parameter identification of salient pole synchronous machines that is based on previous knowledge about the equipment and can be used for condition monitoring, online assessment of the electrical power grid, and adaptive control. It uses a Kalman filter to handle noise and correct deviations in measurements caused by uncertainty of instruments or effects not included in the model.<br>Then it applies a recursive least squares algorithm to identify<br><div>parameters from the synchronous machine model. Despite being affected by saturation effects, the proposed procedure estimates 8 out of 13 parameters from the machine model with minor deviations from data sheet values and is largely insensitive to noise and load conditions.</div><div><br></div><div>Submitted to IEEE IECON 2019.<br></div>

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