Performance index for optimization method based on PMSM drives using ABC and PSO

Abstract

The permanent magnet synchronous motor (PMSM) is commonly used in industrial and home appliances for its high efficiency and dynamic performance. In this research, a PMSM drive based on field-oriented control (FOC) is designed and simulated using MATLAB/Simulink. The speed controller of the drive is tuned using the trial-and-error method. However, the method requires more time for testing and adjustment of the speed controller to generate an optimal output response. Thus, particle swarm optimization (PSO) and artificial bee colony (ABC) algorithms, which require less computational effort and effectively produce good responses, are used to optimize the speed controller of the drive. PSO and ABC also offer an attractive optimization framework because of their independent, agnostic model structures, global searching capability, and low-load real-time calculation. In this study, the results obtained from different tuning methods are compared to determine the best optimization method of the speed controller in the PMSM drive under different operations. Other than that, performance measures such as integral square error (ISE), integral absolute error (IAE), and integral time absolute error (ITAE), which are commonly used to measure the effectiveness of a controller, are also discussed. The results show that the PMSM drive based on the ITAE criterion using the ABC algorithm gives better performance under different conditions.