Sliding Mode and Neural Network Control of Sensorless PMSM Controlled System for Power Consumption and Performance Improvement

Abstract

This paper deals with the design of sliding mode control and neural network compensation for a sensorless permanent magnet synchronous motor (PMSM) controlled system that is able to improve both power consumption and speed response performance. The position sensor of PMSM is unreliable in harsh environments. Therefore, the sensorless control technique is widely proposed in industry. A sliding mode observer can estimate the rotor angle and has the robustness to load disturbance and parameter variations. However, the sliding mode observer is not conducive to standstill and low speed conditions because the amplitude of the back EMF is almost zero. As a result, this paper combines an iterative sliding mode observer (ISMO) and neural networks (NNs) as an angle compensator to improve the above problems. A dsPIC30F6010A-based PMSM sensorless drive system is implemented to validate the proposed algorithm. The simulation and experimental results prove its effectiveness.

Low-Cost Position Sensorless Speed Control of PMSM Drive Using Four-Switch Inverter

“…The detailed classifications of the sensorless control methods are depicted in Figure 1 [1][2][3][4][5][6][7]. Recently, improvements in model-based and signal injection methods for PMSM drives have been investigated in the literature [8][9][10][11][12][13][14][15]. To suppress the unwanted audible noises in the signal injection methods, pseudo-random high-frequency signal injection (HFSI) method is proposed as an alternative to the classical HFSI technique [8,9].…”

“…Hybrid methods become an alternative approach to improve the issues observed in the model-based sensorless schemes. The combination of sliding mode observer (SMO) and neural networks (NNs) is proposed as an angle compensator to improve the starting and low speed performance of the PMSM drive in [12]. Adaptive synchronous-requency tracking-mode observer (SFTO) which is a combination of SMO and model reference adaptive system (MRAS) is investigated for a surface mounted PMSM drive in [13].…”

Comparative Evaluation of a Permanent Magnet Machine Saliency-Based Drive with Sine-Wave and Square-Wave Voltage Injection

“…Elimination of a separated position sensor by using the spatial information in the machine itself (self-sensing) has become a promising solution for next-generation machine drives [5,8]. Different from sensor-based drives, the rotor position has to be estimated from the position-dependent signal in the electromotive force (EMF) voltage or the saliency of a machine [9][10][11].…”

Sliding Mode Observer-Based Load Angle Estimation for Salient-Pole Wound Rotor Synchronous Generators

“…The study conducted in [12] dealt with the load angle estimation by using dynamic ANN with the following inputs: active power, reactive power, field current, and terminal voltage at the current and the previous time instant. The performance of the ANN based load angle estimation generally depends on the training process and the quality of the obtained training data, which represents a significant drawback of this type of estimation methods, as such amount of measurements that would include all operating states of the synchronous generator is hardly feasible for practical applications.In recent years, sliding mode observers (SMOs) [13][14][15][16][17] and sliding mode control (SMC) [18][19][20][21][22][23] have gained much research interest in the field of state estimation and control of electrical drives, including permanent magnet synchronous machines (PMSMs) and induction machines (IMs). The main reasons for increasing interest in application of SMOs in the field of electrical drives are their attractive features, such as simple implementation, strong robustness, order reduction, and disturbance rejection [24].…”

“…In recent years, sliding mode observers (SMOs) [13][14][15][16][17] and sliding mode control (SMC) [18][19][20][21][22][23] have gained much research interest in the field of state estimation and control of electrical drives, including permanent magnet synchronous machines (PMSMs) and induction machines (IMs). The main reasons for increasing interest in application of SMOs in the field of electrical drives are their attractive features, such as simple implementation, strong robustness, order reduction, and disturbance rejection [24].…”