Enhanced Koopman operator-based robust data-driven control for 3 degree of freedom autonomous underwater vehicles: A novel approach

Developing an accurate dynamic model for an Autonomous Underwater Vehicle (AUV) is challenging due to the diverse array of forces exerted on it in an underwater environment. These forces include hydrodynamic effects such as drag, buoyancy, and added mass. Consequently, achieving precision in predicting the AUV's behavior requires a comprehensive understanding of these dynamic forces and their interplay. In our research, we have devised a linear data-driven dynamic model rooted in Koopman's theory. The cornerstone of leveraging Koopman theory lies in accurately estimating the Koopman operator. To achieve this, we employ the dynamic mode decomposition (DMD) method, which enables the generation of the Koopman operator. We have developed a Fractional Sliding Mode Control (FSMC) method to provide robustness and high tracking performance for AUV systems. The efficacy of the proposed controller has been verified through simulation results.