Abstract
Resolver sensor is utilized as absolute position transducer in many industrial motion-control applications where robustness and ability to operate in harsh environment is required. In real system, the quality of position and speed measurement is badly affected by the resolver errors. In this paper, the software-based error-compensation technique is integrated into the control structure of the drive. Compensation scheme adapts online to errors in the resolver signals in order to improve accuracy of the measured position and speed. Performance of the compensation scheme and the effect on the drive operation is verified through experimental testing.
Highlights
In motion control, the quality of measured position and speed is one of the most important factors to achieve high performance
The position and speed is extracted from the analog resolver signals that are in ideal case sine and cosine waves
The major contribution to the position error is caused by different amplitudes, dc offsets and phase error of the resolver signals
Summary
The quality of measured position and speed is one of the most important factors to achieve high performance. Controller of the electric drive is implemented using Digital Signal Processor (DSP) with high computational power This computational power can be utilized to improve the position and speed measurement by software-based approach. In [2], the ellipse fitting technique is used to correct resolver signals in servo drive application to improve accuracy of the position and speed measurement. In [3], the ellipse fitting technique is modified in order to solve the problem, but the computational costs are too high for the online calibration Another approach of self-calibration was presented by Heydemann in 1981 [4]. In this paper the self-calibration method is integrated into the variable-speed field-oriented control scheme of permanent magnet synchronous motor (PMSM) drive equipped with resolver in order to improve accuracy of the position and speed measurement. The block diagram of self-calibration algorithm is presented and individual parts are described in more details
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