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Improved Speed Estimation in Sensorless PM Brushless AC Drives Authors: J.X. Shen, IEEE Member Z.Q. Zhu, IEEE Senior Member David Howe IEEE Transactions on Industry Applications, Vol.38, No.4, August 2002 Student: Sergiu Berinde Outline Abstract Introduction Flux-Observer-Based Sensorless Control Speed Estimation From Differential of Rotor Position Filtering of Observed Flux Vector Average Speed Estimation Improved Method of Speed Estimation Conclusions Abstract The application of flux-observer-based sensorless control to PM brushless AC motors is described Methods in which the speed is derived from the differential of the rotor position and from the ratio of the electromotive force to excitation flux linkage are assessed. An improved method combining the best features of both previous methods is proposed. The performance of the methods is verified experimentally. Introduction Two major categories of sensorless control techniques : 1. speed is estimated from an observer and rotor position is obtained by integration 2. rotor position is estimated from an observer and speed is calculated by differentiation In general, derivation of speed from rotor position => significant noise Estimation of average speed is accurate under steady-state conditions, but not fast enough to give good dynamic response. Estimation of speed from the induced EMF and excitation flux linkage => fast dynamic response, but low accuracy. Improved method of speed estimation would be needed. Introduction A TMS320C31 DSP-based drive, which supplies a two-pole surface mounted PM BLAC motor is used in the investigation. An encoder is used to measure the rotor position and deduce the speed from the differential of the position. These values are compared with the ones obtained from the sensorless techniques. Flux-Observer-Based Sensorless Control The phasor diagram of a BLAC is shown. • - excitation flux linkage due to permanent magnets, in phase with the rotor d axis • - resultant stator winding flux linkage • - angle of rotor, we want to know it • We know that • then, • We also know that • is expressed by , • Therefore, the rotor position is obtained as Flux-Observer-Based Sensorless Control The root locus of the observed flux vector is shown. • displaced due to integration in • solution: apply HPF to the variables to be integrated, equivalent to replacing the integrator by a LPF • after applying the HPF, the circular locus of remains stable Flux-Observer-Based Sensorless Control Estimated rotor position and actual position are compared. Expressed in terms of encoder resolution of 4000 pulses / revolution Maximum error is 50 pulses, or 4.5˚ electrical => sufficiently accurate for vector control of most drives. Speed Estimation from Differential of Rotor Position The rotor angular velocity Since, in general, errors will exist in is given by and , then Since is very short, is comparable with => can cause significant error in the estimated speed and errors become greater at lower speed. Speed Estimation from Differential of Rotor Position In order to demonstrate the limitation of this method => speed change every 2 sec, with fuzzy algorithm for speed control Estimated speed contains significant ripple => inappropriate for feedback in sensorless drive system Filtering of Observed Flux Vector The ripple in the estimated speed is caused by position errors, which in turn arise from noise in the observed flux-linkage vector locus => try to apply LPF to the observed flux vector The flux vector estimation is improved, but a steady-state error of 100 pulses, or 9˚ electrical still exists in the estimated rotor position Filtering of Observed Flux Vector Cause of error => phase shift introduced by the LPF Average Speed Estimation Since a flux filter is not always effective in reducing the speed error => apply another LPF to filter the speed => obtain average speed When applying the speed filter, effects of the flux filter are reduced even at high speed is in close agreement with under steady-state conditions Average Speed Estimation However, the speed filter introduces a noticeable time delay in the estimated speed Does not introduce phase shift, unlike the flux filter, however because of the introduced time delay, it may compromise the dynamic response of the drive Accurate for steady-state conditions, but slow Speed Estimation From Induced EMF and Excitation Flux Linkage If is known a priori and the amplitude of the EMF is calculated from the measured current and voltage, then in the d-q rotor reference frame We can express as But , are sensitive to variations in temperature and the differential operation ( ) can cause a significant error in the estimated speed => this method is inherently inaccurate However, by eliminating the differential operation => lower accuracy, but fast response Speed Estimation From Induced EMF and Excitation Flux Linkage Speed change to verify the fast response of the method The most important feature of the method => response is fast, although accuracy of the estimation is not good Improved Method of Speed Estimation Two conclusions can be drawn: When using When using , a time delay exists between estimated and actual speed , a magnitude error exists between estimated and actual speed However, the two methods can be combined to improve the estimation If s -> 0 , then We can write -> , and if s -> ∞ , then -> as The method compensates of , by adding the output of the high-pass filtering Improved Method of Speed Estimation Conclusions Various methods for estimating the speed of a sensorless BLAC motor drive have been implemented and compared. Estimation of the speed from the differential of rotor position -> accurate under steady-state but poor dynamic response Estimation of the speed from the EMF and excitation flux linkage -> not accurate, but fast response The proposed method of speed estimation has been shown to yield improved performance and is considered suitable for closed-loop speed control