Abstract
In this paper, a finite control set model-free predictive current control (FCS-MFPCC) of a permanent magnet synchronous motor is presented. The control scheme addresses the problems of large current fluctuation and decline of the motor system performance during parameter perturbation for the traditional finite control set model predictive current control (FCS-MPCC). Firstly, the mathematical model of the motor is analyzed and derived during parameter perturbation, and a new hyperlocal model of the motor is established based on this mathematical model. Secondly, a finite control set model-free predictive current controller is designed based on the new hyperlocal model, and a current error correction factor is introduced to correct the prediction error. Meanwhile, the stability of the observer is demonstrated via the Lyapunov theory. The simulation results show that the proposed control strategy reduces current fluctuation compared with the FCS-MPCC strategy, and the system is robust during parameter perturbation.
Highlights
Permanent magnet synchronous motors (PMSMs) have numerous advantages, such as small size, a flexible structure, high reliability, and high power density; they have been widely adopted in many applications, e.g., aerospace, robotics, electric vehicles, etc. [1,2,3].At present, in traditional PMSM speed control systems, a PI controller is generally used to adjust the speed; the algorithm is simple, and the parameters are easy to adjust
Motor systems have required fast current response and small fluctuation for PMSMs in high-precision, high-performance control problems; many scholars have carried out research at home and abroad, and various advanced control methods have been applied to the field of motor control, e.g., model predictive control (MPC) [9,10,11], sliding mode control [12,13], fuzzy control [14,15], neural network control [16,17], etc
Algorithm, and the finite control set model-free predictive current controller is designed on the basis of the new hyperlocal model
Summary
Permanent magnet synchronous motors (PMSMs) have numerous advantages, such as small size, a flexible structure, high reliability, and high power density; they have been widely adopted in many applications, e.g., aerospace, robotics, electric vehicles, etc. [1,2,3].At present, in traditional PMSM speed control systems, a PI controller is generally used to adjust the speed; the algorithm is simple, and the parameters are easy to adjust. Motor systems have required fast current response and small fluctuation for PMSMs in high-precision, high-performance control problems; many scholars have carried out research at home and abroad, and various advanced control methods have been applied to the field of motor control, e.g., model predictive control (MPC) [9,10,11], sliding mode control [12,13], fuzzy control [14,15], neural network control [16,17], etc. MPC has attracted the attention of many scholars due to its fast response speed and its ability to achieve multiple nonlinear objectives; this has become a hot topic in the field of motor control
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