scholarly journals Parameter Identification of BLDC Motor Using Electromechanical Tests and Recursive Least-Squares Algorithm: Experimental Validation

Actuators ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 143
Author(s):  
Jose Jimenez-Gonzalez ◽  
Felipe Gonzalez-Montañez ◽  
Victor Manuel Jimenez-Mondragon ◽  
Jesús Ulises Liceaga-Castro ◽  
Rafael Escarela-Perez ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Real Time ◽  
Digital Simulation ◽  
Least Square ◽  
Recursive Least Squares ◽  
Rotor Speed ◽  
Recursive Least Square ◽  
Brushless Dc ◽  
Speed Controller ◽  
Two Stages

In this article, the parameter identification of a brushless DC motor (BLDC) is presented. The approach here presented is based on a direct identification considering a three-phase line-to-line voltage electromagnetic torque as function of the electric currents and rotor speed. The estimation is divided into two stages. First, the electrical parameters are estimated by well-known no-load and DC tests. Consequently, estimation of mechanical parameters is performed using a recursive Least Square Algorithm. The proposed approach is validated by comparing model responses to motor real time responses. Additionally, the design, digital simulation and real time implementation of a PI rotor speed controller, based on the estimated model, validate the identification proposal presented here.

Parameter Identification of Spatial–Temporal Varying Processes by a Multi-Robot System in Realistic Diffusion Fields

Robotica ◽  
2020 ◽  
pp. 1-20 ◽  
Author(s):  
Wencen Wu ◽  
Jie You ◽  
Yufei Zhang ◽  
Mingchen Li ◽  
Kun Su
Keyword(s):  
Parameter Identification ◽  
Mobile Robots ◽  
Sensor Network ◽  
Nonlinear Partial Differential Equation ◽  
Least Square ◽  
Diffusion Field ◽  
Recursive Least Square ◽  
Robot System ◽  
Parameterized Model ◽  
Multi Robot

SUMMARY In this article, we investigate the problem of parameter identification of spatial–temporal varying processes described by a general nonlinear partial differential equation and validate the feasibility and robustness of the proposed algorithm using a group of coordinated mobile robots equipped with sensors in a realistic diffusion field. Based on the online parameter identification method developed in our previous work using multiple mobile robots, in this article, we first develop a parameterized model that represents the nonlinear spatially distributed field, then develop a parameter identification scheme consisting of a cooperative Kalman filter and recursive least square method. In the experiments, we focus on the diffusion field and consider the realistic scenarios that the diffusion field contains obstacles and hazard zones that the robots should avoid. The identified parameters together with the located source could potentially assist in the reconstruction and monitoring of the field. To validate the proposed methods, we generate a controllable carbon dioxide (CO2) field in our laboratory and build a static CO2 sensor network to measure and calibrate the field. With the reconstructed realistic diffusion field measured by the sensor network, a multi-robot system is developed to perform the parameter identification in the field. The results of simulations and experiments show satisfactory performance and robustness of the proposed algorithms.

REAL-TIME BLIND SOURCE SEPARATION OF ACOUSTIC SIGNALS WITH A RECURSIVE APPROACH

2004 ◽  
Vol 04 (02) ◽  
pp. 193-206 ◽  
Author(s):  
SHUXUE DING ◽  
JIE HUANG ◽  
DAMING WEI
Keyword(s):  
Real Time ◽  
Blind Source Separation ◽  
Correlation Matrix ◽  
Lower Cost ◽  
Source Separation ◽  
Least Square ◽  
Gradient Algorithm ◽  
Normal Equation ◽  
Recursive Least Square ◽  
Second Order Statistics

We propose an approach for real-time blind source separation (BSS), in which the observations are linear convolutive mixtures of statistically independent acoustic sources. A recursive least square (RLS)-like strategy is devised for real-time BSS processing. A normal equation is further introduced as an expression between the separation matrix and the correlation matrix of observations. We recursively estimate the correlation matrix and explicitly, rather than stochastically, solve the normal equation to obtain the separation matrix. As an example of application, the approach has been applied to a BSS problem where the separation criterion is based on the second-order statistics and the non-stationarity of signals in the frequency domain. In this way, we realise a novel BSS algorithm, called exponentially weighted recursive BSS algorithm. The simulation and experimental results showed an improved separation and a superior convergence rate of the proposed algorithm over that of the gradient algorithm. Moreover, this algorithm can converge to a much lower cost value than that of the gradient algorithm.

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.

Weighted Recursive Least Square for Parameter Identification of Nonlinear Wiener–Hammerstein Systems

2021 ◽  
pp. 447-455
Author(s):  
Ruiguang Lan ◽  
Xuemei Ren ◽  
Linwei Li
Keyword(s):  
Parameter Identification ◽  
Least Square ◽  
Recursive Least Square ◽  
Hammerstein Systems
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