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Selection of reference model for adaptive PMSM drive based on MRAC approach

Szczepański Rafał, Tarczewski Tomasz, Grzesiak Lech M.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2024

Vol. 72, nr 4

art. no. e149177

The selection of a reference model (RM) for a Model-Reference Adaptive Control is one of the most important aspects of the synthesis process of the adaptive control system. In this paper, the four different implementations of RM are developed and investigated in an adaptive PMSM drive with variable moment of inertia. Adaptation mechanisms are based on the Widrow-Hoff rule (W-H) and the Adaptation Procedure for Optimization Algorithms (APOA). Inadequate order or inaccurate approximation of RM for the W-H rule may provide poor behavior and oscillations. The results prove that APOA is robust against an improper selection of RM and provides high-performance PMSM drive operation.

Efficiency analysis of parallel computing applied to auto-tuning of state feedback speed controller for PMSM drive

Szczepański Rafał, Tarczewski Tomasz, Grzesiak Lech M.

Poznan University of Technology Academic Journals. Electrical Engineering

2019

No. 100

145--156

Nowadays the simulation is inseparable part of researcher's work. Its computation time may significantly exceed the experiment time. On the other hand, multi-core processors are common in personal computers. These processors can be used to reduce computation time by using parallel computing on multiple cores. The most popular software applied to simulate behavior of the plant is MATLAB/Simulink. A single simulation of Simulink model cannot be computed by multiple cores, but there are many engineering problems, that require a multiple simulation of the same model with different parameters. In these problems, the parallel computing can be employed to decrease the overall simulation time. In this paper the parallel computing is used to speed-up the auto-tuning process of state feedback speed controller for PMSM drive. In order to obtain the optimal coefficients of the controller, an Artificial Bee Colony optimization algorithm is employed.