scholarly journals Analysis of Closed-loop Identification via the Joint Input-output Method and Stochastic Realization

2011 ◽  
Vol 47 (1) ◽  
pp. 61-69
Author(s):  
Hideyuki TANAKA ◽  
Sosuke OMURA
Keyword(s):  
Closed Loop ◽  
Input Output ◽  
Stochastic Realization ◽  
Closed Loop Identification

Two-Degree-of-Freedom Helicopter Closed-Loop Identification through a Cascade Controller

2011 ◽  
Vol 403-408 ◽  
pp. 4649-4658 ◽  
Author(s):  
Pouya Ghalei ◽  
Alireza Fatehi ◽  
Mohamadreza Arvan
Keyword(s):  
Closed Loop ◽  
Flight Simulation ◽  
Degree Of Freedom ◽  
Input Output ◽  
Closed Loop Identification ◽  
Practical Algorithm ◽  
Nonlinear Autoregressive Model ◽  
And Control ◽  
Nonlinear Autoregressive ◽  
Two Degree Of Freedom

Input-Output data modeling using multi layer perceptron networks (MLP) for a laboratory helicopter is presented in this paper. The behavior of the two degree-of-freedom platform exemplifies a high order unstable, nonlinear system with significant cross-coupling between pitch and yaw directional motions. This paper develops a practical algorithm for identifying nonlinear autoregressive model with exogenous inputs (NARX) and nonlinear output error model (NOE) through closed loop identification. In order to collect input-output identifier pairs, a cascade state feedback (CSF) controller is introduced to stabilize the helicopter and after that the procedure of system identification is proposed. The estimated models can be utilized for nonlinear flight simulation and control and fault detection studies.

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.

Sign in / Sign up

Export Citation Format

Share Document