A novel-enhanced metaheuristic algorithm for FOPID-controlled and Bode’s ideal transfer function–based buck converter system

Abstract

The search for a better approach to design a convenient controlling scheme is one of the main challenges of the research field related to power converters. This paper brings forward a significant contribution as part of this challenge. In this regard, novel approaches have been proposed for both controller scheme and tuning algorithm. In terms of controlling scheme, a fractional-order proportional–integral–derivative (FOPID) controller was used alongside the Bode’s ideal transfer function to achieve optimum performance of a buck converter system. To achieve a greater performance of the FOPID controller, a novel metaheuristic algorithm, which consists of a balanced structure in terms of explorative and exploitative phases, has also been developed by hybridizing the hunger games search algorithm with simulated annealing technique. The proposed hybrid algorithm helped tuning the FOPID controller optimally. The proposed algorithm and controlling scheme have been proved to be a better approach for controlling a buck converter system in terms of time and frequency domains along with disturbance rejection. To further verify the effectiveness of the proposed hybrid algorithm–based controlling scheme, it was compared with other reported and capable approaches which have also confirmed the greater performance of the proposed approach.

Effective PID controller design using a novel hybrid algorithm for high order systems

“…These algorithms are inspired by physical and biological phenomena and are well-suited to solving complex optimization problems that traditional methods struggle to handle [2]. With their straightforward and adaptable structure, coupled with the ability to avoid getting stuck in local optima through random search, metaheuristics have become increasingly popular for tackling a wide range of problems [3][4][5][6][7][8][9][10]. In recent years, these techniques have become a prominent area of research for solving high-order real-world problems, thanks to their ability to provide efficient and optimal solutions with ease of implementation, making them a go-to tool for a wide range of engineering design applications [11][12][13][14][15][16][17][18][19].…”