Closed-Loop Identification of the Data-Driven SKR with Deterministic Disturbance for Fault Detection

2018 ◽  
Author(s):  
Kuan Li ◽  
Hao Luo ◽  
Baoran An ◽  
Tianyu Liu ◽  
Shen Yin
Keyword(s):  
Fault Detection ◽  
Closed Loop ◽  
Data Driven ◽  
Closed Loop Identification

scholarly journals Adaptive Predictive Control: A Data-Driven Closed-Loop Subspace Identification Approach

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaosuo Luo ◽  
Yongduan Song
Keyword(s):  
Predictive Control ◽  
Closed Loop ◽  
Control Method ◽  
Negative Impact ◽  
Data Driven ◽  
Recursive Identification ◽  
Subspace Identification ◽  
Identification Algorithm ◽  
Closed Loop Identification ◽  
Adaptive Predictive Control

This paper presents a data-driven adaptive predictive control method using closed-loop subspace identification. As the predictor is the key element of the predictive controller, we propose to derive such predictor based on the subspace matrices which are obtained through the closed-loop subspace identification algorithm driven by input-output data. Taking advantage of transformational system model, the closed-loop data is effectively processed in this subspace algorithm. By combining the merits of receding window and recursive identification methods, an adaptive mechanism for online updating subspace matrices is given. Further, the data inspection strategy is introduced to eliminate the negative impact of the harmful (or useless) data on the system performance. The problems of online excitation data inaccuracy and closed-loop identification in adaptive control are well solved in the proposed method. Simulation results show the efficiency of this method.

Minimal required excitation for closed-loop identification: Some implications for data-driven, system identification

2015 ◽  
Vol 27 ◽  
pp. 22-35 ◽  
Author(s):  
Yuri A.W. Shardt ◽  
Biao Huang ◽  
Steven X. Ding
Keyword(s):  
System Identification ◽  
Closed Loop ◽  
Data Driven ◽  
Driven System ◽  
Closed Loop Identification

Data-driven I-PD Gain Tuning using Closed-loop Step Response Data

2019 ◽  
Vol 139 (8) ◽  
pp. 882-888
Author(s):  
Shiro Masuda ◽  
Jongho Park ◽  
Yoshihiro Matsui
Keyword(s):  
Closed Loop ◽  
Data Driven ◽  
Step Response ◽  
Response Data

scholarly journals Overview on hybrid approaches to fault detection and diagnosis: Combining data-driven, physics-based and knowledge-based models

Procedia CIRP ◽  
2021 ◽  
Vol 99 ◽  
pp. 278-283
Author(s):  
Yannick Wilhelm ◽  
Peter Reimann ◽  
Wolfgang Gauchel ◽  
Bernhard Mitschang
Keyword(s):  
Fault Detection ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Knowledge Based ◽  
Hybrid Approaches ◽  
Combining Data ◽  
Detection And Diagnosis

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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