Abstract
This paper is a proposal of a modified internal model control based on an intelligent technique. The indirect field oriented control strategy (IFOC) is used as a permanent magnet synchronous motor (PMSM) drive platform. Neural network controller and estimator are respectively added to replace the conventional speed regulator and the speed encoder in the global drive scheme. A wide speed working range is considered and high speed mode is incorporated in the study testes. In the IFOC inner control loops, the commonly used synchronous frame conventional proportional plus integral (PI) controllers are replaced by two modified internal model control (IMC) regulators. Therefore, a method based on the bacterial foraging optimization (BFO) algorithm is performed to optimize and adjust the IMC low pass filter parameters. The robustness of the proposed PMSM sensorless drive scheme is confirmed by simulation tests in the MATLAB/SIMULINK. Moreover, a comparative evaluation results are illustrated to prove the effectiveness of the proposed control algorithm according to different controllers combinations.
Highlights
permanent magnet synchronous motor (PMSM) drives become more and more attractive in motioncontrol applications such as High Speed Trains and electricA novel internal model control (IMC) controller based on bacterial foraging optimization algorithm applied to a high speed range the artificial neural architecture is used to replace the real speed encoder in this application.The PMSM control problems present an extended research sector
Many authors, who look for the PMSM robustness drive, use the field oriented control (FOC) strategy as a reliable method, which is characterized by a simple architecture based on three proportional plus integral controllers as described in [2]
The indirect field oriented control strategy (IFOC) strategy with a plus integral (PI) speed regulator and two standard IMC current regulators (IMC without bacterial foraging optimization (BFO) method) gives the worst performances and the speed signal becomes unstable when a stator resistance variation occurs at a high speed regimes
Summary
A novel IMC controller based on bacterial foraging optimization algorithm applied to a high speed range the artificial neural architecture is used to replace the real speed encoder in this application. The PMSM control problems present an extended research sector. Many authors, who look for the PMSM robustness drive, use the field oriented control (FOC) strategy as a reliable method, which is characterized by a simple architecture based on three proportional plus integral controllers as described in [2]. Some other authors relied on the artificial intelligence techniques in their robustness control problems as presented in [11]. These methods need an exact mathematical identification for the learning system’s starting phase, in the artificial case, and for adjusting the controller parameters in the MRAC case
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