Real-Time Diagnosis of the Exhaust Recirculation in Diesel Engines Using Least-Squares Parameter Estimation

2009 ◽  
Vol 132 (1) ◽  
Author(s):  
Javad Mohammadpour ◽  
Karolos Grigoriadis ◽  
Matthew Franchek ◽  
Benjamin J. Zwissler
Keyword(s):  
Diesel Engine ◽  
Least Squares ◽  
Real Time ◽  
Diesel Engines ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Low Flow ◽  
Robust Version ◽  
Orifice Flow ◽  
Gas Recirculation

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.

scholarly journals Real-time fractional order system identification using recursive least squares method

2020 ◽  
Vol 5 (2) ◽  
pp. 138-141
Author(s):  
Walid TOUIL ◽  
Samir LADACI ◽  
Abdelhafid CHAABI
Keyword(s):  
Least Squares ◽  
Real Time ◽  
Fractional Order ◽  
Least Squares Method ◽  
Original System ◽  
Recursive Least Squares ◽  
Order System ◽  
Recursive Least Squares Method ◽  
Real Time Identification ◽  
Fractional Order Integral

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.

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.

scholarly journals Gait Phase Classification and Assist Torque Prediction for a Lower Limb Exoskeleton System Using Kernel Recursive Least-Squares Method

Sensors ◽  
2019 ◽  
Vol 19 (24) ◽  
pp. 5449 ◽  
Author(s):  
Yue Ma ◽  
Xinyu Wu ◽  
Can Wang ◽  
Zhengkun Yi ◽  
Guoyuan Liang
Keyword(s):  
Least Squares ◽  
Joint Angle ◽  
Least Squares Method ◽  
Classification Model ◽  
Recursive Least Squares ◽  
Support Vector ◽  
Exoskeleton Robot ◽  
Phase Classification ◽  
Gait Features ◽  
Gait Phase

The gait phase classification method is a key technique to control an exoskeleton robot. Different people have different gait features while wearing an exoskeleton robot due to the gap between the exoskeleton and the wearer and their operation habits, such as the correspondence between the joint angle and the moment at which the foot contacts the ground, the amplitude of the joint angle and others. In order to enhance the performance of the gait phase classification in an exoskeleton robot using only the angle of hip and knee joints, a kernel recursive least-squares (KRLS) algorithm is introduced to build a gait phase classification model. We also build an assist torque predictor based on the KRLS algorithm in this work considering the adaptation of unique gait features. In this paper, we evaluate the classification performance of the KRLS model by comparing with two other commonly used gait recognition methods—the multi-layer perceptron neural network (MLPNN) method and the support vector machine (SVM) algorithm. In this experiment, the training and testing datasets for the models built by KRLS, MLPNN and SVM were collected from 10 healthy volunteers. The gait data are collected from the exoskeleton robot that we designed rather than collected from the human body. These data depict the human-robot coupling gait that includes unique gait features. The KRLS classification results are in average 3% higher than MLPNN and SVM. The testing average accuracy of KRLS is about 86%. The prediction results of KRLS are twice as good as MLPNN in assist torque prediction experiments. The KRLS performs in a good, stable, and robust way and shows model generalization abilities.

Diesel Engine Intake Condition Control-Oriented Models for Active Fuel Injection Profile Control

2011 ◽  
Author(s):  
Fengjun Yan ◽  
Junmin Wang
Keyword(s):  
Diesel Engine ◽  
Diesel Engines ◽  
Fuel Injection ◽  
Combustion Process ◽  
Exhaust Gas Recirculation ◽  
High Fidelity ◽  
Exhaust Gas ◽  
Engine Model ◽  
Profile Control ◽  
Gas Recirculation

Fueling control in Diesel engines is not only of significance to the combustion process in one particular cycle, but also influences the subsequent dynamics of air-path loop and combustion events, particularly when exhaust gas recirculation (EGR) is employed. To better reveal such inherently interactive relations, this paper presents a physics-based, control-oriented model describing the dynamics of the intake conditions with fuel injection profile being its input for Diesel engines equipped with EGR and turbocharging systems. The effectiveness of this model is validated by comparing the predictive results with those produced by a high-fidelity 1-D computational GT-Power engine model.

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