Data-Driven Fractional-Order PID Controller Tuning for Liquid Slosh Suppression Using Marine Predators Algorithm

Abstract

Traditional control system development for liquid slosh problems often relies on modelbased approaches, which are challenging to implement in practice due to the chaotic and complex nature of fluid motion in containers. In response, this study introduces a data-driven fractional-order PID (FOPID) controller designed using the Marine Predators Algorithm (MPA) for suppressing liquid slosh. The MPA serves as a data-driven tuning tool to optimize the FOPID controller parameters based on a fitness function comprising the total norms of tracking error, slosh angle, and control input. A motor-driven liquid container undergoing horizontal motion is employed as a mathematical model to validate the proposed data-driven control methodology. The effectiveness of the MPA-based FOPID controller tuning approach is assessed through the convergence curve of the average fitness function, statistical results, Wilcoxon's rank test, and the ability to track the cart's horizontal position while minimizing the slosh angle and control input energy. The proposed data-driven tuning tool demonstrates superior performance compared to other recent metaheuristic optimization algorithms across the majority of evaluation criteria.