A Distributed Tool for Online Identification of Communities in Co-authorship Networks at a University

2020 ◽  
pp. 179-189
Author(s):  
David Fernandes ◽  
Nuno David ◽  
Maria João Cortinhal
Keyword(s):  
Online Identification

scholarly journals Lithium-Ion Battery SOC Estimation Based on Adaptive Forgetting Factor Least Squares Online Identification and Unscented Kalman Filter

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1733
Author(s):  
Hao Wang ◽  
Yanping Zheng ◽  
Yang Yu
Keyword(s):  
Kalman Filter ◽  
Least Squares ◽  
Unscented Kalman Filter ◽  
Equivalent Circuit Model ◽  
Lithium Ion ◽  
Forgetting Factor ◽  
Soc Estimation ◽  
Online Identification ◽  
Adaptive Forgetting Factor ◽  
Adaptive Forgetting

In order to improve the estimation accuracy of the battery state of charge (SOC) based on the equivalent circuit model, a lithium-ion battery SOC estimation method based on adaptive forgetting factor least squares and unscented Kalman filtering is proposed. The Thevenin equivalent circuit model of the battery is established. Through the simulated annealing optimization algorithm, the forgetting factor is adaptively changed in real-time according to the model demand, and the SOC estimation is realized by combining the least-squares online identification of the adaptive forgetting factor and the unscented Kalman filter. The results show that the terminal voltage error identified by the adaptive forgetting factor least-squares online identification is extremely small; that is, the model parameter identification accuracy is high, and the joint algorithm with the unscented Kalman filter can also achieve a high-precision estimation of SOC.

scholarly journals Rapid Online Identification of Adverse Events After Influenza Immunization in Children by PCIRN’s National Ambulatory Network

2014 ◽  
Vol 33 (10) ◽  
pp. 1060-1064 ◽  
Author(s):  
Julie A. Bettinger ◽  
Otto G. Vanderkooi ◽  
Judy MacDonald ◽  
James D. Kellner
Keyword(s):  
Adverse Events ◽  
Influenza Immunization ◽  
Online Identification

scholarly journals A MODEL-FREE APPROACH FOR ACCURATE JOINT MOTION CONTROL IN HUMANOID LOCOMOTION

2011 ◽  
Vol 08 (01) ◽  
pp. 27-46 ◽  
Author(s):  
JORGE VILLAGRA ◽  
CARLOS BALAGUER
Keyword(s):  
Nonlinear Effects ◽  
Control Approach ◽  
Transmission Delays ◽  
Online Identification ◽  
Model Free ◽  
Uncertain Dynamics ◽  
Parameter Variations ◽  
Network Transmission ◽  
Model Free Approach ◽  
The One

A new model-free approach to precisely control humanoid robot joints is presented in this article. An input–output online identification procedure will permit to compensate neglected or uncertain dynamics, such as, on the one hand, transmission and compliance nonlinear effects, and, on the other hand, network transmission delays. Robustness to parameter variations will be analyzed and compared to other advanced PID-based controllers. Simulations will show that not only good tracking quality can be obtained with this novel technique, but also that it provides a very robust behavior to the closed-loop system. Furthermore, a locomotion task will be tested in a complete humanoid simulator to highlight the suitability of this control approach for such complex systems.

Online parameter estimation and adaptive control with limited information in structures

2021 ◽  
pp. 107754632110191
Author(s):  
Fereidoun Amini ◽  
Elham Aghabarari
Keyword(s):  
Parameter Estimation ◽  
Adaptive Control ◽  
Least Squares ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Limited Information ◽  
Online Identification ◽  
Recursive Least Squares Method ◽  
Adaptive Control Algorithm

An online parameter estimation is important along with the adaptive control, that is, a time-dependent plant. This study uses both online identification and the simple adaptive control algorithm with velocity feedback. The recursive least squares method was used to identify the stiffness and damping parameters of the structure’s stories. Identification was carried out online without initial estimation and only by measuring the structural responses. The limited information regarding sensor measurements, parameter convergence, and the effects of the covariance matrix is examined. The integration of the applied online identification, the appropriate reference model selection in simple adaptive control, and adopting the proportional integral filter was used to limit the structural control response error. Some numerical examples are simulated to verify the ability of the proposed approach. Despite the limited information, the results show that the simultaneous use of online identification with the recursive least squares method and simple adaptive control algorithm improved the overall structural performance.

Exakte posenabhängige Strukurmodelle*/Accurate virtual machine tool structures - Online-identification of the general model of a lightweight machine tool structure with pose-dependent dynamic behavior

2017 ◽  
Vol 107 (05) ◽  
pp. 323-328
Author(s):  
S. Apprich ◽  
F. Wulle ◽  
A. Prof. Pott ◽  
A. Prof. Verl
Keyword(s):  
Machine Tool ◽  
Least Squares ◽  
Dynamic Behavior ◽  
Natural Frequencies ◽  
Recursive Least Squares ◽  
Machine Model ◽  
Online Identification ◽  
Working Space ◽  
Machine Tool Structure ◽  
Machine Tool Structures

Serielle Werkzeugmaschinenstrukturen weisen ein posenabhängiges, dynamisches Verhalten auf, wobei die Eigenfrequenzen um mehrere Hertz im Arbeitsraum variieren können. Die genaue Kenntnis dieses Verhaltens gestattet eine verbesserte Regelung der Strukturen. Ein generelles parametrisches Maschinenmodell, dessen Parameter online durch einen Recursive-Least-Squares-Algorithmus an das reale Maschinenverhalten angepasst werden, stellt Informationen über dieses Maschinenverhalten bereit.   Serial machine tool structures feature a pose-dependent dynamic behavior with natural frequencies varying by serveral hertz within the working space. The accurate knowledge of this behavior allows an improved control of the structures. A general parametric machine model, whose parameters are adapted online to the actual machine tool behavior by a Recursive Least Squares algorithm, provides information about the pose-dependent dynamic behavior.

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