scholarly journals Closed-loop Identification and Dead Time Estimation Based on Generalized Minimum Variance Evaluation

2016 ◽  
Vol 52 (6) ◽  
pp. 330-337
Author(s):  
Ryota UEMATSU ◽  
Shiro MASUDA
Keyword(s):  
Closed Loop ◽  
Dead Time ◽  
Time Estimation ◽  
Minimum Variance ◽  
Closed Loop Identification

Closed Loop Identification of a Steam Superheater

1979 ◽  
Vol 12 (8) ◽  
pp. 961-968
Author(s):  
J.A. de la Puente ◽  
P. Albertos
Keyword(s):  
Closed Loop ◽  
Steam Superheater ◽  
Closed Loop Identification

scholarly journals Software Sensor to Enhance Online Parametric Identification for Nonlinear Closed-Loop Systems for Robotic Applications

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3653
Author(s):  
Lilia Sidhom ◽  
Ines Chihi ◽  
Ernest Nlandu Kamavuako
Keyword(s):  
Degrees Of Freedom ◽  
Closed Loop ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Parametric Identification ◽  
Recursive Least Squares ◽  
Unknown Parameters ◽  
Closed Loop Identification ◽  
Input Variables ◽  
Robotic Applications

This paper proposes an online direct closed-loop identification method based on a new dynamic sliding mode technique for robotic applications. The estimated parameters are obtained by minimizing the prediction error with respect to the vector of unknown parameters. The estimation step requires knowledge of the actual input and output of the system, as well as the successive estimate of the output derivatives. Therefore, a special robust differentiator based on higher-order sliding modes with a dynamic gain is defined. A proof of convergence is given for the robust differentiator. The dynamic parameters are estimated using the recursive least squares algorithm by the solution of a system model that is obtained from sampled positions along the closed-loop trajectory. An experimental validation is given for a 2 Degrees Of Freedom (2-DOF) robot manipulator, where direct and cross-validations are carried out. A comparative analysis is detailed to evaluate the algorithm’s effectiveness and reliability. Its performance is demonstrated by a better-quality torque prediction compared to other differentiators recently proposed in the literature. The experimental results highlight that the differentiator design strongly influences the online parametric identification and, thus, the prediction of system input variables.

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