High-Speed Control of AC Servo Motor Using High-Performance RBF Neural Network Terminal Sliding Mode Observer and Single Current Reconstructed Technique

• Published in: Electronics (Basel) Vol. 11; no. 10; p. 1646
• Main Authors: Chen, Huaizhi, Cai, Changxin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: A C motors; Accuracy; Algorithms; Angular velocity; Buffeting; Control theory; Controllers; Coordinates; Data buses; Design; Hall effect; High speed; Mathematical models; Neural networks; Observers; Permanent magnets; Phase current; Reconstruction; Robust control; Sensors; Servocontrol; Servomechanisms; Servomotors; Speed control; Synchronous motors
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11101646

This paper proposes a phase current reconstruction strategy based on a dc bus using a single current sensor for a surface permanent magnet synchronous motor (SPMSM). The method of a single current sensor reduces the number of mechanical hall sensors and shunt resistors by using a modified current reconstruction algorithm. The information of rotor position is estimated by the sliding mode observer for its rapid response and strong anti-interference ability, and the observer needs to detect voltage and current components from α − β coordinate system. In order to reduce the buffeting problem of sliding mode observers, an adaptive neural network is introduced, by the way of extracting angle speed estimated values from sliding mode observers, and these values are trained to obtain the compensate angular velocity and minus index value to suppress speed value. The performance of this sensorless speed regulation strategy in the high-speed region using a single current sensor with an optimized adaptive neural network is verified and evaluated by PSIM simulation and experiments.