Investigation of slime mould algorithm optimized PI controller for solar powered hybrid DC-DC converter fed PMBLDC...

This research work focuses on optimizing outer and inner PI controllers by employing Slime Mould Algorithm (SMA) and Ant Colony Optimization (ACO) techniques for precise speed control of a PMBLDC drive which finds applications in electric vehicles. Power from the solar photovoltaic system is processed by the hybrid DC-DC converter and subsequently delivered to the voltage source inverter to drive PMBLDC drive. State space modeling of the hybrid DC-DC converter is developed using small-signal modeling, and the transfer function is derived using the state space averaging technique. Owing to the presence of five energy storage elements, the derived converter transfer function is of fifth order. To reduce the system complexity, model order reduction using the Hankel matrix is applied to obtain a third-order system. A closed loop control scheme is implemented using ACO tuned and SMA tuned outer and inner PI controllers. The system performance is assessed under line, load, and set point variations for both optimization techniques. Simulation results demonstrate that the SMA tuned outer and inner PI controllers deliver superior performance, achieving the desired motor speed with reduced time domain specifications under all operating conditions. To validate the simulation results, a 240-W experimental prototype controlled by a dsPIC30F2010 is developed. Simulation Results reveal that the SMA-tuned PI controller offers improved dynamic performance with reduced time domain specifications. and achieves an efficiency of 91.61%.