Real-Time Self-Tuning Speed Controller: Performance Comparison between Engine Fuelled with Palm Methyl Esters and Petroleum Diesel

2015 ◽  
Vol 815 ◽  
pp. 408-412
Author(s):  
M.N. Azuwir ◽  
Mohd Sazli Saad ◽  
Mohd Zakimi Zakaria
Keyword(s):  
Real Time ◽  
Least Squares Method ◽  
Methyl Esters ◽  
Performance Comparison ◽  
Parameters Estimation ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Automotive Engine ◽  
Speed Controller ◽  
Self Tuning

This paper investigates the performance of a real-time self-tuning speed controller designed to track and regulate at various engine speeds. The controller was tested with an automotive engine fuelled with petroleum diesel and and palm oil biodiesel (Palm Methyl Esters) within speed range of 1800 rpm to 2400 rpm. A self-tuning control algorithm based on pole assignment method together with on-line model parameters estimation strategy based on the recursive least squares method are adopted. The ability of the controller to track, regulate at various engine speed and also to reject disturbances applied for both type of fuel are compared and presented. The results confirmed that the controller performed very satisfactorily.

scholarly journals Current Driver Design for Electromagnetic Coil Using Adaptive Active Disturbance Rejection Control

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hongtao Zhu ◽  
Xiaoting Rui ◽  
Fufeng Yang ◽  
Wei Zhu
Keyword(s):  
Real Time ◽  
Disturbance Rejection ◽  
Least Squares Method ◽  
Parameter Tuning ◽  
Parameters Estimation ◽  
Recursive Least Squares ◽  
Order System ◽  
Electromagnetic Coil ◽  
Active Disturbance Rejection ◽  
Adaptive Ability

An adaptive active disturbance rejection controller is used in the current driver design of the electromagnetic coil. Extended state observer of the 1st-order system is adopted for disturbance observation of ADRC. The supervised recursive least squares method is proposed for real-time parameters estimation, in which the excitation signal variance is used to trigger the parameter estimator. The experimental results demonstrate that ADRC combined with real-time parameter estimation simplifies the parameter tuning and improves the parameter adaptive ability.

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.

MEMS Gyro Random Drift Model Parameter Identification Based on Two-Stage Recursive Least Squares Method

2012 ◽  
Vol 220-223 ◽  
pp. 1044-1047 ◽  
Author(s):  
Zhao Hua Liu ◽  
Jia Bin Chen ◽  
Yu Liang Mao ◽  
Chun Lei Song
Keyword(s):  
Least Squares ◽  
Least Squares Method ◽  
Moving Average ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Random Drift ◽  
Two Stage ◽  
Autoregressive Moving Average Model ◽  
Drift Model ◽  
Recursive Least Squares Method

Autoregressive moving average model (ARMA) was usually used for gyro random drift modeling. Because gyro random drift was a non-stationary, weak non-linear and time-variant random signal, model parameters were random and time-variant, too. For improving precision of gyro and reducing effects of random drift, this paper adopted two-stage recursive least squares method for ARMA parameter estimation. This method overcame the shortcomings of the conventional recursive extended least squares (RELS) algorithm. At the same time, the forgetting factor was introduced to adapt the model parameters change. The simulation experimental results showed that this method is effective.

Model Parameter Identification and Simulation of Ship Power System Based on Recursive Least Squares Method

2012 ◽  
Vol 220-223 ◽  
pp. 482-486 ◽  
Author(s):  
Jin Hui Hu ◽  
Da Bin Hu ◽  
Jian Bo Xiao
Keyword(s):  
Mathematical Model ◽  
Power Systems ◽  
Parameter Identification ◽  
Least Squares ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Design Parameters ◽  
Model Parameters ◽  
Model Parameter ◽  
Model Parameter Identification

According to the lack of the part of the equipment design parameters of a certain type of ship power systems, the algorithm of recursive least squares for model parameter identification is studied. The mathematical model of the propulsion motor is established. The model parameters are calculated and simulated based on parameter identification method of recursive least squares. The simulation results show that a more precise mathematical model can be simple and easily obtained by using of the method.

scholarly journals Self-Tuning Predictive Control of Nonlinear Servo-Motor

2010 ◽  
Vol 61 (6) ◽  
pp. 365-372 ◽  
Author(s):  
Vladimír Bobál ◽  
Petr Chalupa ◽  
Marek Kubalčík ◽  
Petr Dostál
Keyword(s):  
Predictive Control ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Laboratory Model ◽  
Servo Motor ◽  
Real Time Control ◽  
Highly Nonlinear ◽  
Model Control ◽  
Self Tuning

Self-Tuning Predictive Control of Nonlinear Servo-MotorThe paper is focused on a design of a self-tuning predictive model control (STMPC) algorithm and its application to a control of a laboratory servo motor. The model predictive control algorithm considers constraints of a manipulated variable. An ARX model is used in the identification part of the self-tuning controller and its parameters are recursively estimated using the recursive least squares method with the directional forgetting. The control algorithm is based on the Generalised Predictive Control (GPC) method and the optimization was realized by minimization of a quadratic and absolute values objective functions. A recursive control algorithm was designed for computation of individual predictions by incorporating a receding horizon principle. Proposed predictive controllers were verified by a real-time control of highly nonlinear laboratory model — Amira DR300.

Real-Time Battery Model Identification Using a Two Time-Scaled Approach

2013 ◽  
Author(s):  
Yiran Hu ◽  
Yue-Yun Wang
Keyword(s):  
Parameter Identification ◽  
Least Squares ◽  
State Estimation ◽  
Real Time ◽  
Recursive Least Squares ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Model Parameter ◽  
Battery Model ◽  
Model Parameter Identification

Battery state estimation (BSE) is one of the most important design aspects of an electrified propulsion system. It includes important functions such as state-of-charge estimation which is essentially for the energy management system. A successful and practical approach to battery state estimation is via real time battery model parameter identification. In this approach, a low-order control-oriented model is used to approximate the battery dynamics. Then a recursive least squares is used to identify the model parameters in real time. Despite its good properties, this approach can fail to identify the optimal model parameters if the underlying system contains time constants that are very far apart in terms of time-scale. Unfortunately this is the case for typical lithium-ion batteries especially at lower temperatures. In this paper, a modified battery model parameter identification method is proposed where the slower and faster battery dynamics are identified separately. The battery impedance information is used to guide how to separate the slower and faster dynamics, though not used specifically in the identification algorithm. This modified algorithm is still based on least squares and can be implemented in real time using recursive least squares. Laboratory data is used to demonstrate the validity of this method.

Geometry Control of the Deposited Layer in a Nonplanar Laser Cladding Process Using a Variable Structure Controller

2008 ◽  
Vol 130 (3) ◽  
Author(s):  
Alireza Fathi ◽  
Amir Khajepour ◽  
Mohammad Durali ◽  
Ehsan Toyserkani
Keyword(s):  
Laser Cladding ◽  
Closed Loop ◽  
Least Squares Method ◽  
Sliding Mode ◽  
Scanning Speed ◽  
Recursive Least Squares ◽  
Hammerstein Model ◽  
Model Parameters ◽  
Control Input ◽  
Laser Cladding Process

This paper presents a closed-loop laser cladding process used in nonplanar deposition of desired metallic materials. In the proposed system, the deposited layer geometry is continuously controlled via a sliding mode controller (SMC). The controller, which uses the scanning speed as the control input, is designed based on a parametric Hammerstein model. The model is a parametric dynamic model with several unknown parameters, which are identified experimentally using the recursive least squares method. The designed SMC is robust to all model parameters’ uncertainties and disturbances. The results showed that the tracking accuracy improves and the chattering effect reduces if an integrator on the scanning speed is added to the controller. It was observed that this addition decreases the response speed. The performance of the proposed controllers was verified through the fabrication of several parts made of SS303-L. This verification indicates that the developed closed-loop laser cladding process can reduce stair-step effects as well as production time in rapid prototyping of functional parts created with the adaptive slicing technique.

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