Adaptive Flux Observer for Nonsalient PMSM with Noised Measurements of the Current and Voltage

2019 ◽  
Vol 20 (4) ◽  
pp. 215-218 ◽  
Author(s):  
A. A. 1. Pyrkin ◽  
A. A. Bobtsov ◽  
A. A. Vedyakov ◽  
D. N. Bazylev ◽  
M. M. Sinetova
Keyword(s):  
Measurement Errors ◽  
Adaptive Estimation ◽  
Permanent Magnet Synchronous Motor ◽  
Regression Function ◽  
Estimation Errors ◽  
Persistent Excitation ◽  
Flux Observer ◽  
Nonlinear Parameterization ◽  
Global Exponential Convergence ◽  
Drive Model

An algorithm of adaptive estimation of the magnetic flux for the non-salient permanent magnet synchronous motor (PMSM) for the case when measurable electrical signals are corrupted by a constant offset is presented. A new nonlinear parameterization of the electric drive model based on dynamical regressor extension and mixing (DREM) procedure is proposed. Due to this parameterization the problem of flux estimation is translated to the auxiliary task of identification of unknown constant parameters related to measurement errors. It is proved that the flux observer provides global exponential convergence of estimation errors to zero if the corresponding regression function satisfies the persistent excitation condition. Also, the observer provides global asymptotic convergence if the regression function is square integrable. In comparison with known analogues this paper gives a constructive way of the flux reconstruction for a nonsalient PMSM with guaranteed performance (monotonicity, convergence rate regulation) and, from other hand, a straightforwardly easy implementation of the proposed observer to embedded systems.

Adaptive Observer of Rotor Position and Flux for Salient Synchronous Motor

2019 ◽  
Vol 20 (2) ◽  
pp. 114-121
Author(s):  
D. N. Bazylev ◽  
A. A. Pyrkin ◽  
A. A. Bobtsov
Keyword(s):  
Permanent Magnets ◽  
Adaptive Estimation ◽  
Angular Position ◽  
Regression Function ◽  
Estimation Algorithm ◽  
Synchronous Motor ◽  
Position Estimation ◽  
Adaptive Observer ◽  
Estimation Errors ◽  
Persistent Excitation

An algorithm of adaptive estimation of rotor flux and angular position for the salient synchronous motor with permanent magnets is presented. A new nonlinear parameterization of the dynamic motor model is proposed. Due to this parameterization the problem of position estimation is translated to the task of identification of unknown constant parameters. During the synthesis of estimation algorithm the currents and voltages of the stator windings, as well as the rotor speed, are assumed to be known signals. Two variants of the adaptive observer based on the standard gradient estimator and the algorithm of the dynamic extension of the regressor are proposed. It is proved that the both versions of the observer provide global exponential convergence of estimation errors to zero if the corresponding regression function satisfies the persistent excitation condition. Also, the latter version of the observer provides global asymptotic convergence if the regression function is square integrable. The results of numerical simulation demonstrate that the algorithm with the dynamic extension of the regressor provides a better quality of estimation transient processes in comparison with the standard gradient estimator.

Adaptive Tuning Functions Tracking Control with Nonlinear Adaptive Observers

2014 ◽  
Vol 11 (2) ◽  
pp. 14-21
Author(s):  
R. Mishkov ◽  
V. Petrov
Keyword(s):  
Tracking Control ◽  
Closed Loop ◽  
Adaptive System ◽  
Permanent Magnet Synchronous Motor ◽  
Unified Approach ◽  
Closed Loop System ◽  
Estimation Errors ◽  
Nonlinear Adaptive Control ◽  
Performance Specifications ◽  
System Time

Abstract The paper is dedicated to the derivation of a unified approach for nonlinear adaptive closed loop system design with nonlinear adaptive state and parameter observers combined with tuning functions-based nonlinear adaptive control for trajectory tracking. The proposed approach guarantees asymptotic stability of the closed loop nonlinear adaptive system with respect to the tracking and state estimation errors and Lyapunov stability of the parameter estimator. The advantages of the approach are the lack of over-parametrization, resulting in a minimal number of estimator equations and the preservation of the overdamped performance specifications of the closed loop nonlinear adaptive system in its whole range of operation. The application of the approach to a permanent magnet synchronous motor driven inverted pendulum concludes with simulation of the closed loop nonlinear adaptive system time responses.

Adaptive estimation of UVs navigation parameters by irregular inertial-satellite measurements

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sergey V. Sokolov ◽  
Arthur I. Novikov
Keyword(s):  
Kalman Filter ◽  
Adaptive Estimation ◽  
Unmanned Vehicles ◽  
Dispersion Matrix ◽  
Navigation Systems ◽  
Satellite Measurements ◽  
Preliminary Calculation ◽  
Adaptation Algorithm ◽  
Estimation Errors ◽  
Content Type

PurposeThere are shortcomings of modern methods of ensuring the stability of Kalman filtration in unmanned vehicles’ (UVs) navigation systems under the condition of a priori uncertainty of the dispersion matrix of measurement interference. First, it is the absence of strict criteria for the selection of adaptation coefficients in the calculation of the a posteriori covariance matrix. Secondly, it is the impossibility of adaptive estimation in real time from the condition of minimum covariance of the updating sequence due to the necessity of its preliminary calculation.Design/methodology/approachThis paper considers a new approach to the construction of the Kalman filter adaptation algorithm. The algorithm implements the possibility of obtaining an accurate adaptive estimation of navigation parameters for integrated UVs inertial-satellite navigation systems, using the correction of non-periodic and unstable inertial estimates by high-precision satellite measurements. The problem of adaptive estimation of the noise dispersion matrix of the meter in the Kalman filter can be solved analytically using matrix methods of linear algebra. A numerical example illustrates the effectiveness of the procedure for estimating the state vector of the UVs’ navigation systems.FindingsAdaptive estimation errors are sharply reduced in comparison with the traditional scheme to the range from 2 to 7 m in latitude and from 1.5 to 4 m in longitude.Originality/valueThe simplicity and accuracy of the proposed algorithm provide the possibility of its effective application to the widest class of UVs’ navigation systems.

V̇o2, V̇co2, and RQ in a respiratory chamber: accurate estimation based on a new mathematical model using the Kalman-Bucy method

2004 ◽  
Vol 96 (3) ◽  
pp. 1045-1054 ◽  
Author(s):  
L. Granato ◽  
A. Brandes ◽  
C. Bruni ◽  
A. V. Greco ◽  
G. Mingrone
Keyword(s):  
Mathematical Model ◽  
Measurement Errors ◽  
Estimation Method ◽  
Accurate Estimation ◽  
Production Rates ◽  
Estimation Errors ◽  
Respiratory Chamber ◽  
Residual Noise ◽  
Proposed Model

A respiratory chamber is used for monitoring O2 consumption (V̇o2), CO2 production (V̇co2), and respiratory quotient (RQ) in humans, enabling long term (24-h) observation under free-living conditions. Computation of V̇o2 and V̇co2 is currently done by inversion of a mass balance equation, with no consideration of measurement errors and other uncertainties. To improve the accuracy of the results, a new mathematical model is suggested in the present study explicitly accounting for the presence of such uncertainties and error sources and enabling the use of optimal filtering methods. Experiments have been realized, injecting known gas quantities and estimating them using the proposed mathematical model and the Kalman-Bucy (KB) estimation method. The estimates obtained reproduce the known production rates much better than standard methods; in particular, the mean error when fitting the known production rates is 15.6 ± 0.9 vs. 186 ± 36 ml/min obtained using a conventional method. Experiments with 11 humans were carried out as well, where V̇o2 and V̇co2 were estimated. The variance of the estimation errors, produced by the KB method, appears relatively small and rapidly convergent. Spectral analysis is performed to assess the residual noise content in the estimates, revealing large improvement: 2.9 ± 0.8 vs. 3,440 ± 824 (ml/min)2 and 1.8 ± 0.5 vs. 2,057 ± 532 (ml/min)2, respectively, for V̇o2 and V̇co2 estimates. Consequently, the accuracy of the computed RQ is also highly improved (0.3 × 10-4 vs. 800 × 10-4). The presented study demonstrates the validity of the proposed model and the improvement in the results when using a KB estimation method to resolve it.

A robust regression analysis of recruitment in fisheries

1995 ◽  
Vol 52 (5) ◽  
pp. 993-1006 ◽  
Author(s):  
Y. Chen ◽  
J. E. Paloheimo
Keyword(s):  
Environmental Variables ◽  
Measurement Errors ◽  
Robust Regression ◽  
Estimation Method ◽  
Fish Stock ◽  
Estimation Errors ◽  
Ricker Model ◽  
Stock Recruitment ◽  
Atypical Values ◽  
Special Studies

Variations in environmental variables and (or) errors in measuring stock and recruitment often result in large and heterogeneous variations in fitting fish stock–recruitment (SR) data to a regression model. This makes the commonly used least squares (LS) method inappropriate in estimating the SR relationship. Hence, we propose the following procedure: (i) identify possible outliers in fitting data to a given SR model using the least median of the squared orthogonal distance that is not sensitive to atypical values and requires no assumption on distribution of errors and (ii) apply the LS method to the SR data with defined outliers being down weighted. We showed by simulation that the SR parameters of the Ricker model could be estimated with smaller estimation errors and biases using the proposed procedures than with the traditional LS approach. Examination of four sets of published field data leads us to suggest fitting fish SR data to suitable models using the proposed estimation method and interpreting the results with the assistance of knowledge on the relevant environmental variables and measurement errors. However, our interpretation should be viewed as a working hypothesis requiring special studies to clarify the causal links between environmental variables and recruitment.

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