Low Speed Sensorless Control Based on an Improved Sliding Mode Observation and the Inverter Nonlinearity Compensation for SPMSM

Abstract

It is a difficult to accurately estimate the rotor position in a surface permanent magnet synchronous motor (SPMSM), especially for low speed conditions because the back electromotive force (EMF) is almost zero. In this paper, an improved sliding mode observer (SMO) with a continuous sliding mode switching function (which could reduce the chattering effect) is proposed to estimate the real-time rotor position at a low speed. Through the introduction of an intermediate variable H , the back EMF is extended at low speed, which could improve the estimation accuracy for the rotor position. In addition, the chattering phenomenon is reduced by redesigning the sliding switching function. On the other hand, inverter nonlinearity also results in torque ripple and deteriorates the drive performance because of the harmonic components in the three-phase stator current. Here, a generalized sliding discrete Fourier transform (GSDFT) strategy is studied to extract the harmonic components, which can then be compensated with the corresponding compensation voltage calculated from the motor mathematical model. With the proposed strategy, the estimation accuracy can be apparently restricted when the rotor speed is lower than 50 rpm, while the chattering effect is also improved. With the nonlinearity compensation, the total harmonic distortion obviously decreases, which can improve the rotor estimation and the torque performance. In addition, the GSDFT algorithm executes in approximately half the time of the SDFT compensation method. INDEX TERMS Surface permanent magnet synchronous motor (SPMSM), low speed, sliding mode observer (SMO), inverter nonlinearity compensation, generalized sliding discrete fourier transform (GSDFT).

A new simplified fundamental model‐based sensorless control method for surface‐mounted permanent magnet synchronous machines

“…It is known that saliency tracking‐based methods cannot be applied to SPMSM due to the lack of rotor saliency; fundamental model‐based methods are the main way to realise sensorless control of SPMSM. For the fundamental model‐based sensorless control methods [1–12], rotor position estimation is based on calculating either back‐EMF or flux‐linkage. In this part, the basic approaches of utilising back‐EMF and flux‐linkage for position estimation are demonstrated.…”

“…Basically, sensorless techniques can be divided into fundamental model‐based techniques [1–12] and saliency tracking‐based techniques [3–19]. Fundamental model‐based methods utilising back‐electromotive force (EMF) or flux‐linkage have a good performance at middle and high speeds.…”

“…Fundamental model‐based methods utilising back‐electromotive force (EMF) or flux‐linkage have a good performance at middle and high speeds. Back‐EMF and flux‐linkage can be estimated simply using the phase‐locked loop (PLL) or other observers including adaptive observer [4–6], sliding‐mode observer (SMO) [7–10] and extended Kalman filter (EKF) [11,12]. However, the magnitude of back‐EMF is proportional to the rotor speed, these methods present poor performance and cannot be employed in the zero‐ and low‐speed range, whereas saliency tracking‐based methods are more suitable in the zero‐ and low‐speed range.…”

Sensorless Control of Induction Motors Based on Fractional-Order Linear Super-Twisting Sliding Mode Observer With Flux Linkage Compensation

“…Based on the stator current error term, by increasing the fractional-order integral term to enhance the flexibility of the sliding mode surface, the chattering problem can be further suppressed, and the sliding mode convergence speed can be accelerated [20], [23] , (14) where A=-ηφ rα -ω r φ rβ +ηL m i sα , and B=ω r φ rα -ηφ rβ +ηL m i sβ . According to (1), y and S I satisfy the following inequalities .…”

“…Excessively high sliding mode chattering will increase a system's energy consumption and affect estimation accuracy [11], [13]. An improved SMO is designed in [14] using the sigmoid function instead of the sign function to reduce the chattering problem in the rotor position estimation process. In [15], a new SMO is proposed to estimate rotor position and speed.…”