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The importance of renewable energy resources in the energy province necessitates the use of photovoltaic (PV) and wind systems to power brushless DC (BLDC) motors for applications such as water pumping and automobiles. In this research, an optimized MPPT-based positive output super-lift Luo converter (PO-SLLC) is presented for a hybrid power system with a BLDC motor to overcome the constraints of ordinary DC–DC converters. The Luo converter produces a positive output voltage which aids in achieving a high power density with fewer ripples in the voltage and current profile. The output voltage grows in geometric progression, increasing the converter’s efficiency, according to its mathematical model. Adjustment parameter enhanced Aquila optimization (APEAO) is used to optimize the duty cycle of the PO-SLLC from the MPPT controller and the controller parameters of the PI controller of the wind system. The proposed system’s behavior for variations in irradiation patterns with constant load torque is simulated in MATLAB/Simulink. The settling time and overshoot obtained with the proposed optimized controller are compared to those obtained with other optimization controllers, such as ant colony optimization (ACO), genetic algorithm (GA), Aquila optimization (AO), advanced version of harmony search algorithm (2N$+$1-HSA) and opposition based quantum bat algorithm (OQBA) and it is demonstrated that the proposed APEAO optimized controller has good dynamic performance.

Due to the expanded industrialization, the necessity of variable speed machines/drives keeps on expanding. The vast majority of computerized Brushless Direct Current (BLDC) motor frame-works are utilized because of their speedier reaction and high stablity. In this paper, an innovative technique, i.e. Adaptive Neuro-Fuzzy Inference System (ANFIS) with Fractional-Order PID (FOPID) controllers for controlling a portion of the parameters, for example, speed, and torque of the BLDC motor are exhibited. With a specific end goal being the performance of the proposed controller under outrageous working conditions, for example, varying load and set speed conditions, simulation results are taken for deliberation. An Opposition-based Elephant Herding Optimization (OEHO) optimization algorithm is utilized to improve the tuning parameters of FOPID controller. At that point, the ANFIS is gladly proposed to adequately control the speed and torque of the motor. The simulation result exhibited that the composed FOPID controller understands a decent dynamic behavior of the BLDC, an immaculate speed tracking with less ascent and gives better execution. The performance investigation of the proposed strategy lessened the error signal contrasted with the existing strategies, for example, FOPID-based Elephant Herding Optimization (EHO), Proportional–Integral–Derivative BAT (PID-BAT), and PID-ANFIS.