Sensorless Indirect Stator Field Orientation Speed Control for Single-Phase Induction Motor Drive

10International audienceThe industrial requirements for the control of an induction machine without a mechanical sensor continue to be of interest, as evidenced by the most recent publications. The focus is on improvements of control without a mechanical sensor. A new method for the implementation of a sensorless indirect stator-fluxoriented control (ISFOC) of a single-phase induction motor (SPIM) drive is proposed in this paper. The proposed method of rotor speed estimation is based only on themeasurement of themain and auxiliary windings stator currents and that of a reference q-axis current generated by the control algorithm. The error of the measured qaxis current from its reference value feeds the proportional plus integral controller, the output of which is the estimated slip angular frequency. Experimental results for sensorless ISFOC speed control of a SPIM drive are presented and analyzed using a dSPACE system with DS1104 controller board based on the digital signal processor TMS320F240. Digital simulation and experimental results are presented to show the improvement in performance of the proposed sensorless algorithm

Robust Filtering for State and Fault Estimation of Linear Stochastic Systems with Unknown Disturbance

This paper presents a new robust filter structure to solve the simultaneous state and fault estimation problem of linear stochastic discrete-time systems with unknown disturbance. The method is based on the assumption that the fault and the unknown disturbance affect both the system state and the output, and no prior knowledge about their dynamical evolution is available. By making use of an optimal three-stage Kalman filtering method, an augmented fault and unknown disturbance models, an augmented robust three-stage Kalman filter (ARThSKF) is developed. The unbiasedness conditions and minimum-variance property of the proposed filter are provided. An illustrative example is given to apply this filter and to compare it with the existing literature results

Fault Tolerant Robust Control Applied for Induction Motor (LMI approach)

This paper foregrounds fault tolerant robust control of uncertain dynamic linear systems in the state space representation. In fact, the industrial systems are more and more complex and the diagnosis process becomes indispensable to guarantee their surety of functioning and availability, that’s why a fault tolerant control law is imperative to achieve the diagnosis. In this paper, we address the problem of state feedback H2 /H∞ mixed with regional pole placement for linear continuous uncertain system. Sufficient conditions for feasibility are derived for a general class of convex regions of the complex plan. The conditions are presented as a collection of linear matrix inequalities (LMI 's). The efficiency and performance of this approach are then tested taking into consideration the robust control of a three- phase induction motor drive with the fluctuation of its parameters during the functioning

PWM-Switching pattern-based diagnosis scheme for single and multiple open-switch damages in VSI-fed induction motor drives

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Model Reference Adaptive System Based Adaptive Speed Estimation for Sensorless Vector Control with Initial Rotor Position Estimation for Interior Permanent Magnet Synchronous Motor Drive

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Robust parameter estimation with noisy data Robust parameter estimation with noisy data

Abstract. For process control improvement, coherency of information supplied by instrument lines and sensors must first be ensured; because of the presence of random and possibly gross errors, the model equations of the process are not generally satisfied. Moreover, the parameters of the considered model are not always exactly known. The problem of how to reconcile the measurements so that they satisfy the model constraints is considered in this article. The simultaneous presence of measurement errors in process input and output measurements coupled with the model parameter uncertainty poses serious problem in the rectification of data. In that paper, the problem is solved using a special filter to estimate both the parameters, the input and the output of a process represented by an autoregressive model

A Nonlinear Observer for High-Performance Sensorless Speed Control of IPMSM Drive

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Robust input and state estimation for linear discrete-time systems using PI two stages Kalman filter

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Unbiased Minimum-Variance Filter for State and Fault Estimation of Linear Time-Varying Systems with Unknown Disturbances

This paper presents a new recursive filter to joint fault and state estimation of a linear time-varying discrete systems in the presence of unknown disturbances. The method is based on the assumption that no prior knowledge about the dynamical evolution of the fault and the disturbance is available. As the fault affects both the state and the output, but the disturbance affects only the state system. Initially, we study the particular case when the direct feedthrough matrix of the fault has full rank. In the second case, we propose an extension of the previous case by considering the direct feedthrough matrix of the fault with an arbitrary rank. The resulting filter is optimal in the sense of the unbiased minimum-variance (UMV) criteria. A numerical example is given in order to illustrate the proposed method

Observateur proportionnel multi-intégral pour les systèmes linéaires à entrées inconnues

National audienceCe travail s'intéresse à l'estimation d'état de systèmes linéaires à temps discret et en présence d'entrées inconnues en utilisant un observateur de type proportionnel multi-intégral sous une forme générale (PI^p). L'introduction de multiples actions intégrales dans l'observateur d'état, permet d'assurer une bonne précision de l'estimation même en présence des entrées inconnues décrites par des polynômes de degré p. Un exemple d'application numérique montre la validité de cette approche en utilisant un observateur doté de trois actions intégrales sur un système affecté par une entrée inconnue polynomiale