The Influence of Discretization Step on the Accuracy of the Structural-Parametric Identification of Multisinusoidal Signals

2020 ◽  
Author(s):  
M.A. Novoseltseva ◽  
S.G. Gutova ◽  
E.S. Kagan
Keyword(s):  
Parametric Identification ◽  
Discretization Step

МАТЕМАТИЧЕСКОЕ МОДЕЛИРОВАНИЕ ДИНАМИКИ МИНЕРАЛЬНОГО ПИТАНИЯ РАСТЕНИЙ В СИСТЕМЕ «УДОБРЕНИЕ-ПОЧВА-РАСТЕНИЕ»

2020 ◽  
Vol 65 (6) ◽  
pp. 1219-1229
Author(s):  
В.А. Четырбоцкий ◽  
◽  
А.Н. Четырбоцкий ◽  
Б.В. Левин ◽  
◽  
...  
Keyword(s):  
Experimental Data ◽  
Temporal Dynamics ◽  
Plant Biomass ◽  
Environmental Parameters ◽  
Parametric Identification ◽  
Agricultural Crop ◽  
Parameter System ◽  
Plant Nitrogen ◽  
Rhizosphere Microorganisms ◽  
Nitrogen Phosphorus

A numerical simulation of the spatial-temporal dynamics of a multi-parameter system is developed. The components of this system are plant biomass, mobile and stationary forms of mineral nutrition elements, rhizosphere microorganisms and environmental parameters (temperature, humidity, acidity). Parametric identification and verification of the adequacy of the model were carried out based on the experimental data on the growth of spring wheat «Krasnoufimskaya-100» on peat lowland soil. The results are represented by temporal distributions of biomass from agricultural crop under study and the findings on the content of main nutrition elements within the plant (nitrogen, phosphorus, potassium). An agronomic assessment and interpretation of the obtained results are given.

Offline parametric Identification of a reduce ARX-Laguerre multimodel

2020 ◽  
Author(s):  
Benamor Hajer ◽  
Chakib BEN NJIMA ◽  
Garna Tarek ◽  
ZAAFOURI Abderrahmen ◽  
Messaoud Hassani
Keyword(s):  
Parametric 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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