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.
Highlights
It is well-known that electric motors are the single biggest consumer of electricity in modern society and their consumption of industrial and domestic electric motors per year occupy 46.2% of the global electrical demand [1]
The excellent performance of permanent magnet synchronous motor (PMSM) drive systems comes from the information of rotor position that is measured by position sensors of the motors
Considering the rotor position error during motor starting to lower speeds, this paper proposes an iterative sliding mode observer (ISMO) [11,12], combined with an artificial neural network (ANN)
Summary
It is well-known that electric motors are the single biggest consumer of electricity in modern society and their consumption of industrial and domestic electric motors per year occupy 46.2% of the global electrical demand [1]. For standstill and low speed conditions, signal injection methods [4,5,6,7], which inject the high-frequency voltage and current signals to detect or estimate the rotor position, are the popular candidates of sensorless control schemes. At medium- and high-speed ranges, the fundamental excitation methods [3,8,9,10], which are based on the motor model and use the fundamental signals of voltages and currents for the rotor position and speed detection, are adopted by most researchers. Considering the rotor position error during motor starting to lower speeds, this paper proposes an iterative sliding mode observer (ISMO) [11,12], combined with an artificial neural network (ANN).
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