Design of an Improved Robust Fractional-Order PID Controller for Buck–Boost Converter using Snake Optimization Algorithm

Abstract

With the increasing complexity of modern power systems, effective control of DC–DC converters has become crucial to ensure stability and efficiency. This paper focuses on optimizing the parameters of a known fractional-order proportional–integral–derivative (FOPID) controller for the control of a DC–DC buck–boost converter. The control of a DC–DC buck–boost converter is achieved using aFOPID approach. The gains of this technique have been enhanced utilizing the snake optimization (SO) algorithm. This converter exhibits unfavourable behaviour due to its non-minimum structure, necessitating a well-regulated controller to guarantee stability. The fractional concept is suggested here to enhance the dynamics of the classical PID controller, leveraging its simplicity and minimizing computational load in real-time applications. The fractional idea is an advantageous method that offers several benefits, such as reduced overshoot and settling time, enhanced frequency response, non-integer order dynamics, and, more importantly, higher robustness to noise and parametric variation. Despite the advantages reported by this control technique, a proper gain tuning is needed to enhance its dynamical performance and decrease its sensitivity to error. Thus, a modern algorithm known as SO tunes the values of the gains in the controller to affect the efficiency of this method. This algorithm is a novel strategy with numerous merits compared to others, using its bi-directional search and elite opposition-based learning strategies. The SO algorithm and its variants offer a promising alternative for solving optimization problems, combining efficiency, adaptability, and competitive performance. The contribution of this work lies in utilizing the SO algorithm to enhance the performance of the FOPID controller, enabling faster convergence and improved stability under varying operating conditions. The proposed approach is validated through both simulation and hardware-in-loop experiments, demonstrating superior performance compared to conventional control methods.