scholarly journals Windowed Least Square Algorithm Based PMSM Parameters Estimation

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Song Wang
Keyword(s):  
Embedded System ◽  
Window Size ◽  
Parameters Estimation ◽  
Least Square ◽  
Time Varying ◽  
Recursive Least Square ◽  
Permanent Magnet Synchronous Motors ◽  
Practical Applications ◽  
Controlling System ◽  
System Resource

Stator resistance and inductances ind-axis andq-axis of permanent magnet synchronous motors (PMSMs) are important parameters. Acquiring these accurate parameters is usually the fundamental part in driving and controlling system design, to guarantee the performance of driver and controller. In this paper, we adopt a novel windowed least algorithm (WLS) to estimate the parameters with fixed value or the parameter with time varying characteristic. The simulation results indicate that the WLS algorithm has a better performance in fixed parameters estimation and parameters with time varying characteristic identification than the recursive least square (RLS) and extended Kalman filter (EKF). It is suitable for engineering realization in embedded system due to its rapidity, less system resource possession, less computation, and flexibility to adjust the window size according to the practical applications.

A Method for Modal Parameter Identification of Time Varying Vibration Systems

2012 ◽  
Vol 479-481 ◽  
pp. 688-693
Author(s):  
Zi Ying Wu ◽  
Kun Shi
Keyword(s):  
Linear Time ◽  
Least Square Method ◽  
Degree Of Freedom ◽  
Least Square ◽  
Modal Parameters ◽  
Time Varying ◽  
Recursive Least Square ◽  
Modal Parameter ◽  
Linear Time Invariant ◽  
Prony Method

In this paper a new time varying multivariate Prony (TVM-Prony) method is put forward to identify modal parameters of time varying (TV) multiple-degree-of-freedom systems from measured vibration responses. The proposed method is based on the classical Prony method that is often used to identify modal parameters of linear time invariant systems. The main advantage of the propose approach is that it can analyze multi-dimensional nonstationary signals simultaneously. A modified recursive least square method based on the traditional one is presented to determine the TV coefficient matrices of the multivariate parametric model established in the proposed method. The efficiency and accuracy of the identification approach is demonstrated by a numerical example, in which a TV mass-string system with three-degree-of-freedom is investigated. Satisfied results are obtained.

scholarly journals Adaptive Filtering for Full-Duplex UWA Systems with Time-Varying Self-Interference Channel

2020 ◽  
Author(s):  
Lu Shen ◽  
Yuriy Zakharov ◽  
Benjamin Henson ◽  
Nils Morozs ◽  
Paul Mitchell
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Mean Squared Error ◽  
Least Square ◽  
Full Duplex ◽  
Time Varying ◽  
Recursive Least Square ◽  
Exponential Window ◽  
Evaluation Metric ◽  
High Level

<div>Abstract:</div><div><br></div><div>To enable full-duplex (FD) in underwater acoustic (UWA) systems, a high level of self-interference (SI) cancellation (SIC) is required. For digital SIC, adaptive filters are used. In time-invariant channels, the SI can be effectively cancelled by classical recursive least-square (RLS) adaptive filters, such as the sliding-window RLS (SRLS) or exponential-window RLS, but their SIC performance degrades in time-varying channels, e.g., in channels with a moving sea surface. Their performance can be improved by delaying the filter inputs. This delay, however, makes the mean squared error (MSE) unsuitable for measuring the SIC performance. In this paper, we propose a new evaluation metric, the SIC factor (SICF), which gives better indication of the SIC performance compared to MSE. The SICF can be used in experiments and in real FD systems. A new SRLS adaptive filter based on parabolic approximation of the channel variation in time, named SRLS-P, is also proposed. The SIC performance of the SRLS-P adaptive filter and classical RLS algorithms (with and without the delay) is evaluated by simulation and in lake experiments. The results show that the SRLS-P adaptive filter significantly improves the SIC performance, compared to the classical RLS adaptive filters.</div>

scholarly journals Parameters estimation of BLDC motor based on physical approach and weighted recursive least square algorithm

2021 ◽  
Vol 11 (1) ◽  
pp. 133
Author(s):  
Rania Majdoubi ◽  
Lhoussaine Masmoudi ◽  
Mohammed Bakhti ◽  
Abderrahmane Elharif ◽  
Bouazza Jabri
Keyword(s):  
Reference Frame ◽  
Parameters Estimation ◽  
Torque Control ◽  
Least Square ◽  
Recursive Least Square ◽  
Brushless Dc Motors ◽  
Brushless Dc ◽  
Physical Approach ◽  
Recursive Least Square Algorithm ◽  
Second Procedure

Brushless DC motors (BLDCM) are widely used when high precision converters are required. Model based torque control schemes rely on a precise representation of their dynamics, which in turn expect reliable system parameters estimation. In this paper, we propose two procedures for BLDCM parameters identification used in an agriculture mobile robot’s wheel. The first one is based on the physical approach or equations using experimentation data to find the electrical and mechanical parameters of the BLDCM. The parameters are then used to elaborate the model of the motor established in Park’s reference frame. The second procedure is an online identification based on recursive least square algorithm. The procedure is implemented in a closed-loop scheme to guarantee the stability of the system, and it provide parameter matrices obtained by transforming electrical equations, established in Parks reference frame, and mechanical equation to discrete-time domain. From these matrices, and using well formulated intermediate variables, all desired parameters are deduced simultaneously. The identification procedures are being verified using simulation under Matlab-Simulink software.

Combining CEEMD and recursive least square for the extraction of time-varying seismic wavelets

2019 ◽  
Vol 170 ◽  
pp. 103854
Author(s):  
Peng Zhang ◽  
Yongshou Dai ◽  
Hongqian Zhang ◽  
Chunxian Wang ◽  
Yuhan Zhang
Keyword(s):  
Least Square ◽  
Time Varying ◽  
Recursive Least Square

Estimating Parameters of SAR Model by Recursive Least Square Method

2012 ◽  
Vol 518-523 ◽  
pp. 4171-4176
Author(s):  
Meng Cheng ◽  
Jo Song Guk ◽  
Jin Wen Wang
Keyword(s):  
Autoregressive Model ◽  
Least Square Method ◽  
Parameters Estimation ◽  
Least Square ◽  
Recursive Least Square ◽  
Moment Estimation ◽  
Runoff Forecasting ◽  
Runoff Series ◽  
Monthly Runoff ◽  
Recursive Least Square Method

Seasonal runoff series can be described by seasonal autoregressive model, which is extensively applied in long-term runoff forecasting. The common way of parameters estimation is moment estimation. This paper estimates parameters of seasonal autoregressive model by recursive least square method and applies the method in forecasting the monthly runoff for the Three Gorges. An effective procedure based upon the least fitting error is proposed to determine the model order. The forecasting results are satisfactory.

scholarly journals ESTIMASI PARAMETER MODEL FUNGSI ALIH ORDE 4 PADA ROL PENGUMPAN SISTEM TRANSPORTASI WEB MATERIAL BERBASIS FORGETTING FACTOR DENGAN MATLAB 7.04

Telematika ◽  
2015 ◽  
Vol 10 (1) ◽  
Author(s):  
Yaya Finayani ◽  
Muhammad Alhan
Keyword(s):  
Transfer Function ◽  
Covariance Matrix ◽  
Transport System ◽  
Sampling Period ◽  
Industrial Applications ◽  
Parameters Estimation ◽  
Least Square ◽  
Recursive Least Square ◽  
Forgetting Factor ◽  
Initial Value

Web Materials Transport system appears in variety of industrial applications such as paper and plastics industries. The main part of web transport system is unwind roll section. The dynamic model for unwind roll section is derived from the Newton’s laws, the Hooke’s law and conservations of mass principle. This model gives a relationship between web tension output and torque input. Futhermore this relationship is expressed in transfer function form. This transfer function contains a phisical time-varying parameter namely the radius of the unwind roll. Usually before designing control system, a model parameter estimation is needed. This on-line estimation problem is investigated in this paper using RLS (recursive least square) method with forgetting factor. The estimation needs data in the form of discrete and the initial value covariance matrix diagonal (P).  This research uses discrete ZOH method with sampling period T = 0.4 sec; 0.35 sec; 0.3 sec; 0.25 sec; 0.2 sec for convert transfer function from s-domain to z-domain. Parameters estimation with mean square error (MSE) in the range  2x10-5 ≤ MSE ≤ 3x10-5 are obtained using initial value covariance matrix diagonal P in the range 102  ≤ P ≤ 108 using  forgetting factor 0.95.

scholarly journals Adaptive Filtering for Full-Duplex UWA Systems with Time-Varying Self-Interference Channel

2020 ◽  
Author(s):  
Lu Shen ◽  
Yuriy Zakharov ◽  
Benjamin Henson ◽  
Nils Morozs ◽  
Paul Mitchell
Keyword(s):  
Adaptive Filter ◽  
Adaptive Filters ◽  
Mean Squared Error ◽  
Least Square ◽  
Full Duplex ◽  
Time Varying ◽  
Recursive Least Square ◽  
Exponential Window ◽  
Evaluation Metric ◽  
High Level

<div>Abstract:</div><div><br></div><div>To enable full-duplex (FD) in underwater acoustic (UWA) systems, a high level of self-interference (SI) cancellation (SIC) is required. For digital SIC, adaptive filters are used. In time-invariant channels, the SI can be effectively cancelled by classical recursive least-square (RLS) adaptive filters, such as the sliding-window RLS (SRLS) or exponential-window RLS, but their SIC performance degrades in time-varying channels, e.g., in channels with a moving sea surface. Their performance can be improved by delaying the filter inputs. This delay, however, makes the mean squared error (MSE) unsuitable for measuring the SIC performance. In this paper, we propose a new evaluation metric, the SIC factor (SICF), which gives better indication of the SIC performance compared to MSE. The SICF can be used in experiments and in real FD systems. A new SRLS adaptive filter based on parabolic approximation of the channel variation in time, named SRLS-P, is also proposed. The SIC performance of the SRLS-P adaptive filter and classical RLS algorithms (with and without the delay) is evaluated by simulation and in lake experiments. The results show that the SRLS-P adaptive filter significantly improves the SIC performance, compared to the classical RLS adaptive filters.</div>

Modal Parameter Identification of Time-Varying Systems Using the Time-Varying Multivariate Autoregressive Model

2005 ◽  
Author(s):  
Lilan Liu ◽  
Hongzhao Liu ◽  
Ziying Wu ◽  
Daning Yuan ◽  
Pengfei Li
Keyword(s):  
Parameter Identification ◽  
Linear Time ◽  
Damping Ratio ◽  
Least Square Method ◽  
Least Square ◽  
Time Varying ◽  
Recursive Least Square ◽  
Modal Parameter ◽  
Modal Parameter Identification ◽  
Multivariate Autoregressive

A new time-varying multivariate autoregressive (TVMAR) model method for modal parameter identification of linear time-varying (TV) systems with multi-output is introduced. Besides, a modified recursive least square method based on the traditional one is presented to determine the coefficient matrices of the TVMAR model. In the proposed method, multi-dimensional nonstationary response signals of the vibrating system can be processed simultaneously. Not only the TV modal frequency and damping ratio of the system, but also the changing behavior of the mode shape in the course of vibration are identified by the proposed procedure. Numerical simulations, in which a three-degree-of-freedom system with TV stiffness is respectively subjected to impulse excitation and white noise excitation, are presented. The validity and accuracy of the method are demonstrated by the good simulation results.

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