Abstract
This paper focuses on the universal control design of permanent magnet synchronous motors (PMSMs) with uncertain system dynamics. In vector control, classical proportional-integral (PI) controllers are used to control d-q axis currents and speed of the PMSM. This paper uses two control methods: conventional field-oriented vector control and simplified control. First, all the control gains are determined for numerous PMSMs with various power ratings using an empirical study and generalized mathematical expressions are derived for each of the gains. Then, these expressions are used for automatic gain calculation for various PMSMs with a wide power-rating range. In vector control, the control gains are determined using only the motor power ratings. In the simplified control, generalized control gain expressions are obtained using the number of pole pairs and the flux linkage. Compared to the vector control, the simplified control method provides much simpler generalized mathematical expressions. Validation is carried out in MATLAB/Simulink environment using various PMSMs from 0.2 HP to 10 HP, and results show accurate tracking of reference speed and d-q axis reference currents. Thus, the proposed gain scheduling approach is effective and can be used for self-commissioning motor drives.
Highlights
Afterwards, to validate this proposed tuning method, four new Permanent magnet synchronous motors (PMSMs) are simulated in MATLAB/Simulink environment using these generalized mathematical expressions, and the results show accurate tracking of the currents and the speed
This paper proposed two gain tuning methods for the control of PMSMs with minimum information of prior system dynamics
The vector control approach is used to obtain some mathematical equations consisting of the respective control gain and the motor power rating as the two variables
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Artificial intelligence techniques such as fuzzy logic and artificial neural networks are gaining more attention in the field of PMSM control due to their many advantages over the conventional methods. In [39], an efficient PID controller gain auto-tuning method is proposed through reinforcement learning neural networks for a complex system like a multicopter. An average curve lying between these two limits is generated through an empirical study, and a generalized mathematical expression is obtained Afterwards, to validate this proposed tuning method, four new PMSMs are simulated in MATLAB/Simulink environment using these generalized mathematical expressions, and the results show accurate tracking of the currents and the speed. Unlike the aforementioned techniques discussed before, the proposed tuning methods do not use complex control algorithms and are much simpler and convenient to apply. These mathematical equations form the basis of the PMSM control strategy which is discussed
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