# Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive
###### Abstract  
This paper presents a modeling and performance evaluation of a five-phase PMBLDC motor with different controllers to analyze its transients and dynamic response with time domain specifications. Four different types of speed controllers, namely an Adaptive Neuro-Fuzzy Inference System (ANFIS), an Adaptive Hybrid fuzzy-PI, a Proportional Integral (PI) control and a Fuzzy Logic Control (FLC), were considered and compared for this purpose. The mathematical model of the five-phase Permanent Magnet Brushless Direct Current (PMBLDC) motor was developed and simulated using MATLAB/Simulink to analyze its performance. The simulation results for all controllers with step and linear load fluctuations were evaluated. It was evident that the ANFIS controller provided a better dynamic time domain response than the other controllers. It gave eight times the peak torque at starting, and settled at 92.6% of the rated speed, with negligible overshoot, a very low rise time and a quick settling time compared to other controllers, which are ideally suited for an electric vehicle (EV). A real-time experimental setup was also developed, and experiments were carried out to validate the simulation results. Brushless DC motors (BLDC); five-phase; Permanent Magnet (PM) motor; fuzzy logic controller; proportional plus Integral controller (PI); ANFIS controller; hybrid fuzzy-PI +
Footnote †: journal: Applied Sciences  
torque applications in general. The focus of researchers has migrated to five-phase PMBLDC motors over three-phase PMBLDC motors due to their fault tolerance capabilities [10; 11; 12]. Consider the case of a three-phase PMBLDC motor driving a mechanical load. In a three-phase star-connected stator-winded BLDC motor connected to a load, while switching, one phase will always be ideal, and the remaining series connected to two phases will carry the load current. Due to overload, or short circuiting in the windings, if the working phase is opened, the motor suddenly drops to zero speed, ignoring the load demand at a given time. This results in poor load regulation and zero tolerance capability [13; 14; 15]. Five-phase motors overcome this issue in conventional three-phase motors and provide good reliability. In five-phase motors under a faulty phase, the remaining three phases can still drive the load and the drive needs not be instantly halted [16; 17]. The occurrence of torque ripple in three-phase PMBLDC motors is considered a significant worry in applications where position and speed control accuracy are of great significance, and for which the five-phase counterparts are the only solution. PI controller-based PMBLDC motors have a better performance than fuzzy logic controllers do in a steady state than transients [18]. Despite this, the design of a PI controller requires a mathematical model of the motor with a high degree of accuracy. The FLC controller does not depend on an accurate mathematical model of the motor; and hence, it effectively substitutes the PI controller, as it is a rule-based linguistic controller. Still, the performance of the fuzzy controller is only better under transient conditions, but not in a steady state condition [18; 19; 20]. An adaptive hybrid fuzzy-PI controller combines the benefits of the fuzzy and PI controllers in both steady state and transient conditions [21; 22]. One of the main shortcomings of FLC is the presence of the trial-and-error process in the identification of fuzzy rules, the fuzzy membership function [23; 24], and their definitions, along with the universe of discourse. To overcome this, the controller must adapt itself to changes in plant dynamics. This can be achieved with the ANFIS controller. The selection of the rule base, based on the situation based on trained neural network techniques, is used in the ANFIS controller; and hence, this approach to control yields excellent results [25; 26; 27].