Sensorless Control of Permanent Magnet Synchronous Machine with Magnetic Saliency Tracking Based on Voltage Signal Injection

Abstract

This paper presents a sensorless control method of a permanent magnet synchronous machine (PMSM) with magnetic saliency estimation. This is based on a high-frequency injection (HFI) technique applied on the modified PMSM model in the γδ reference frame. Except for sensorless control, an emphasis is placed on the magnetic saliency estimation to indicate a practical approach in tracking PMSM inductance variations. The magnetic saliency is determined using calculations embedded in the speed and position algorithm through current measurements. A notable characteristic of the modified PMSM model is that the corresponding rotor flux integrates both permanent magnet and saliency term fluxes. In applying a HFI technique for sensorless control, the structure of the PMSM flux model is formatted accordingly. A novel inductance matrix is derived that is completely compatible with the HFI methodology, since its elements include terms of angle error differential and average inductances. In addition, a sliding mode observer (SMO) is designed to estimate the speed and angle of rotor flux based on equivalent control applying a smooth function of the angle error instead of a sign one to reduce the chattering phenomenon. The control strategy is principally based on the adequacy of the proposed modified model and on the appropriateness of the SMO structure to successfully track the rotor flux position with the required stability and accuracy. Simulation results demonstrate the performance of the PMSM sensorless control verifying the effectiveness of the proposed algorithm to detect PMSM saliency, speed and position in steady state and transient modes successfully. Keywords: sensorless control of PMSM; sliding mode observer (SMO); magnetic saliency estimation; modified rotor flux; high frequency injection (HFI) Notation u d , u q = dq axis stator voltages i d , i q = dq axis stator currents λ d , λ q = dq axis stator magnetic fluxes λ m = rotor magnetic flux L d , L q = dq axis inductances ΣL =(L d +L q )/2 = average inductance ∆L = (L d -L q )/2 = differential inductance r s = stator resistance u γ , u δ = γδ axis stator voltages i γ , i δ = γδ axis stator currents λ γ , λ δ = γδ axis stator magnetic fluxes p = number of pole pairs θ = θ e = electrical angular position ω = ω e = electrical angular speed Machines 2020, 8, 14 3 of 20However, the observers in the αβ stationary reference frame suffer from phase delay between the real and the estimated EMF, since the real extended EMF is a sinusoidal signal [18,20]. This undesired phase delay is due to the observer operation, which is similar to signal filtering. Despite the αβ stationary frame, PMSM variables, such as flux and extended EMF, are constant quantities in the dq (direct-quadrature) synchronous rotating reference frame that, however, cannot be applied directly. An advantageous alternative proposal is to express PMSM mathematical model in the γδ reference frame, which is rotating at estimated angular velocityω and lagging behind the dq synchronous reference frame by electrical...

An Improved Super-Twisting High-Order Sliding Mode Observer for Sensorless Control of Permanent Magnet Synchronous Motor

“…Although the system state can be close to the equilibrium point, it will not be achieved in a short time. A similar problem also appears in [26], where the SGN function is substituted with the smooth function of the angle error to weaken the chattering phenomenon. In [27], an improved SMO is applied to obtain rotor position and speed, in which the stator current frequency-variable tracker function is used instead of the switching function to reduce the influence of torque ripple and harmonic components on the back-EMF signals.…”

“…Since the change rate of the motor angular velocity is much lower than that of the stator current, we can assume that ω = 0. Then, the back-EMFs model of the PMSM can be expressed as [26]:…”

Position Estimation at Zero Speed for PMSMs Using Artificial Neural Networks

“…In [25], a solution for sensorless PMSM drive was presented to reduce the percentage of HF voltage with respect to the rated voltage to a few percent. In another, the work in [26] presents a simulation verification of a sensorless PMSM control concept based on inductance tracking using high-frequency injection.…”

A Position Sensorless Control of an Axial Flux-Switching Permanent-Magnet Motor Based on High-Frequency Pulsating Voltage Vector Injection

“…A high-performance transmission system needs to install a position sensor to measure the real-time rotor position, but at the same time, the speed control system has high cost, large size, and low reliability, which limits its application range. In order to overcome the defects caused by mechanical position sensors, scholars have carried out a lot of research on the position sensorless technologies of AC motors [5][6][7][8][9][10][11][12][13][14][15][16]. Sensorless technology reduces the size and cost of the motor, and improves the reliability of the system.…”

“…Sensorless technology reduces the size and cost of the motor, and improves the reliability of the system. These technologies can be divided into two categories, namely based on the back EMF [5][6][7] and the based on salient pole effect [8][9][10][11][12][13][14][15]. The method based on the back EMF has superior performance at medium and high speeds, but since the magnitude of the back EMF is proportional to the rotation speed, it is not suitable for zero-speed and low-speed operation.…”