Online Identification of the System Order with ANARX Structure

2011 ◽  
pp. 5-15 ◽  
Author(s):  
Sven Nõmm ◽  
Juri Belikov
Keyword(s):  
Online Identification ◽  
System Order

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.

Exploring System of Systems Resilience vs. Affordability Trade-Space Using a Bio-Inspired Metric

2021 ◽  
pp. 1-13
Author(s):  
Abheek Chatterjee ◽  
Richard Malak ◽  
Astrid Layton
Keyword(s):  
Design Process ◽  
Early Stage ◽  
System Of Systems ◽  
Ecological Research ◽  
Highly Efficient ◽  
Trade Offs ◽  
System Order ◽  
Range Of Values

Abstract The objective of this study is to investigate the value of an ecologically inspired architectural metric called the Degree of System Order in the System of Systems (SoS) architecting process. Two highly desirable SoS attributes are the ability to withstand and recover from disruptions (resilience) and affordability. In practice, more resilient SoS architectures are less affordable and it is essential to balance the trade-offs between the two attributes. Ecological research analyzing long-surviving ecosystems (nature's resilient SoS) using the Degree of System Order metric has found a unique balance of efficient and redundant interactions in their architecture. This balance implies that highly efficient ecosystems tend to be inflexible and vulnerable to perturbations while highly redundant ecosystems fail to utilize resources effectively for survival. Motivated by this unique architectural property of ecosystems, this study investigates the response to disruptions vs. affordability trade-space of a large number of feasible SoS architectures. Results indicate that the most favorable SoS architectures in this trade-space share a specific range of values of Degree of System Order. This suggests that Degree of System Order can be a key metric in engineered SoS development. Evaluating the Degree of System Order does not require detailed simulations and can, therefore, guide the early stage SoS design process towards more optimal SoS architectures.

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