Synchronous machine drive observability analysis and sensorless control design
ABSTRACT Permanent magnet synchronous (PMSM) drives become very popular in many industrial applications. Their construction provides better performance and reliability than DC drives. But there is also one significant disadvantage - actual rotor position knowledge is necessary to control the drive even in the case when only speed control is required. A position sensor has to be used increasing the drive cost. Algorithms for rotor position and speed estimation from electrical quantities have been developed by many authors. This paper presents PMSM observability analysis to be able to define conditions under which it is possible to compute rotor speed and position. Following the observability analysis results a simple PMSM rotor position estimation algorithm is proposed using a MRAS concept.
Conference Proceeding: Comparison of MRAS and novel simple method for position estimation in PMSM drives[show abstract] [hide abstract]
ABSTRACT: The control of a high performance permanent magnet synchronous motor drive needs accurate information on rotor angle and angular speed. Two methods to estimate the position angle and angular speed of a surface magnet motor without the use of mechanical motion sensors are compared in this paper. The proposed estimator is compared to the model reference adaptive system (MRAS) estimator, which is a well known method of estimating the angle and speed of the PMSM. The simple algorithms studied in this paper do not need any information about mechanical system of the motor. The applicability of the estimators for closed-loop, estimator based, feedback control was studied using simulations and measurements with a real PMSM drive.Power Electronics Specialist Conference, 2003. PESC '03. 2003 IEEE 34th Annual; 07/2003
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ABSTRACT: This paper considers the stability problem of the model reference adaptive control systems by means of the properties of hyperstable systems. A theorem concerning the hyperstability of model reference adaptive control systems is presented. This theorem directly gives a structure of the adaption mechanism. The results presented here include all the results obtained by Butchart, Shackcloth, Parks, Winsor, Roy, and Dressler. The hyperstability approach presented in this paper also allows for other solutions to the adaption mechanism and represents a general method for studying this type of adaptive systems. The results are directly applicable to the design of model reference adaptive control systems and they were verified for some particular cases by analogical simulation.IEEE Transactions on Automatic Control 11/1969; · 2.72 Impact Factor
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ABSTRACT: The extended Kalman filter (EKF) is probably the most widely used estimation algorithm for nonlinear systems. However, more than 35 years of experience in the estimation community has shown that is difficult to implement, difficult to tune, and only reliable for systems that are almost linear on the time scale of the updates. Many of these difficulties arise from its use of linearization. To overcome this limitation, the unscented transformation (UT) was developed as a method to propagate mean and covariance information through nonlinear transformations. It is more accurate, easier to implement, and uses the same order of calculations as linearization. This paper reviews the motivation, development, use, and implications of the UT.Proceedings of the IEEE 04/2004; · 6.91 Impact Factor