Recently, the ecological environment is deteriorating due to various natural disasters caused by global warming and domestic waste. Therefore, it is important to develop a reliable USV control approach to collect river or sea waste under the challenging environment.
Kim Chung Il, a researcher at the Faculty of Naval Architecture and Ocean Engineering, established a mathematical model of USV motion and a wind model in the presence of mass variation, and then designed a fuzzy neural adaptive sliding mode controller to estimate uncertain mass and wind disturbance simultaneously when collecting sea waste.
First, he devised a mathematical model of USV motion and a wind model to consider added mass and wind variation when collecting sea waste. Then, he used a fuzzy neural method to determine varying wind effect during the mass variation and designed a nonlinear disturbance observer to estimate uncertain disturbances involving mass variation. Finally, he designed an adaptive sliding mode controller to consider varying displacement and wind effect.
The simulation results showed that this methodology enables USV to deal with added mass and varying wind disturbance during a transient period of time to collect sea waste.
You can find more information in his paper “Fuzzy Neural Adaptive SMC of an Unmanned Surface Vehicle for Sea Waste Collection with Varying Displacement and Wind Effect” in “Proceedings of KUTIC-2025”.