Real-time dynamic displacement monitoring with double integration of acceleration based on recursive least squares method

Measurement ◽  
2019 ◽  
Vol 141 ◽  
pp. 460-471 ◽  
Author(s):  
Wenhao Zheng ◽  
Danhui Dan ◽  
Wei Cheng ◽  
Ye Xia
2020 ◽  
Vol 5 (2) ◽  
pp. 138-141
Author(s):  
Walid TOUIL ◽  
Samir LADACI ◽  
Abdelhafid CHAABI

In this paper, we address the problem of fractional order systems real-time identification based on recursive least squares technique. The fractional order model is approximated using the Charef Singularity function method and Grünwald numerical approximation for fractional order integral and derivative. We show by numerical simulation example that the identified model represents the original system efficiently.


2021 ◽  
pp. 107754632110191
Author(s):  
Fereidoun Amini ◽  
Elham Aghabarari

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.


Author(s):  
Javad Mohammadpour ◽  
Karolos Grigoriadis ◽  
Matthew Franchek ◽  
Benjamin J. Zwissler

In this paper, we present a real-time parameter identification approach for diagnosing faults in the exhaust gas recirculation (EGR) system of Diesel engines. The proposed diagnostics method has the ability to detect and estimate the magnitude of a leak or a restriction in the EGR valve, which are common faults in the air handling system of a Diesel engine. Real-time diagnostics is achieved using a recursive-least-squares (RLS) method, as well as, a recursive formulation of a more robust version of the RLS method referred to as recursive total-least-squares method. The method is used to identify the coefficients in a static orifice flow model of the EGR valve. The proposed approach of fault detection is successfully applied to diagnose low-flow or high-flow faults in an engine and is validated using experimental data obtained from a Diesel engine test cell and a truck.


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