Data-Assisted Dynamic Modeling of Bionic Robotic Fish and Its Precise Speed Control

Abstract

Dynamic modeling is essential for comprehending physical mechanisms and devising control strategies in bionic robot research. This letter introduces a novel dynamic modeling method that combines Lagrangian dynamics and data-assisted techniques for robotic fish with bionic morphology, multi-joint structures, and a flexible caudal fin. Firstly, a nonlinear continuous hydrodynamic model has been developed using extensive data derived from computational fluid dynamics (CFD), thereby capturing the high-fidelity locomotion of robotic fish. Secondly, based on mathematical derivation, a stability analysis method and controller design approach for biomimetic systems with periodic behaviors have been proposed. Furthermore, to demonstrate the model's efficacy, we designed a model reference adaptive controller for speed control. Both simulation and experimental results validate the model's accuracy, effectiveness, and potential for improving control consistency in tracking time-varying speeds of robotic fish.