Modeling and Neural-Network-Based Tail Oscillation Control of a Fish-Like Bionic Soft Actuation Mechanism

With the progress in ocean exploration, bionic soft robotic fish have garnered significant attention, with their key feature being the actuation mechanism made from soft materials. However, the complex properties of these materials pose challenges in modeling and control. In this letter, we design and fabricate a Fish-like Bionic Soft Actuation Mechanism (FBSAM) and aim to achieve its tail oscillation control. First, we construct an experimental platform to collect data on FBSAM's motion characteristics, revealing complex nonlinear hysteresis influenced by varying liquid environments. Next, we develop a phenomenological model for FBSAM based on the Hammerstein architecture and identify its parameters via nonlinear least squares algorithm. Subsequently, we propose an integral sliding mode hybrid control strategy, introducing an inverse hysteresis compensator to address hysteresis issue and using the neural network to estimate uncertain disturbances caused by liquid environments. Finally, experimental results demonstrate that the designed FBSAM can oscillate in water like a real fish, and the proposed control strategy adapts to various external environments, maintaining excellent performance even in dynamic flow conditions, showcasing its effectiveness and superiority.