A Numerical Simulation Study on the Synergistic Effects of Caudal Fin Structural Stiffness and Active Muscle Control on Propulsive Performance in Bionic Fish

Authors:
Publisher website
Google Scholar
Abstract
In biomimetic underwater systems, high-efficiency and low-power propulsion remains a core challenge. Mimicking the characteristics of fish caudal fins and exploring highly biomimetic muscle-driven approaches is regarded as one of the key strategies to address this issue.This study combines the immersed boundary-lattice Boltzmann (IB-LB) method with deep reinforcement learning (DRL) to investigate the interactive effects of caudal fin structural stiffness and active muscle control on propulsive performance and energy consumption.By constructing a virtual fish model with a closed-loop ``perception--decision--action'' feature, the agent can autonomously learn to output tail torque based on environmental feedback, thereby regulating the deflection behavior of the caudal fin. The research evaluates the differences in dynamic responses between rigid and flexible caudal fin configurations under both passive states and active control intervention. The results indicate that rigid caudal fins exhibit significant phase lag and increased energy consumption without control; however, driven by DRL strategies, they can achieve phase compensation and a substantial improvement in propulsive performance. In contrast, flexible caudal fins, relying on stronger passive adaptability, can achieve superior propulsive efficiency in the uncontrolled state, while their speed and energy consumption can be further optimized with the introduction of active regulation.To realize dynamic trade-offs between speed and energy consumption, this study develops a task-sensitive multi-objective dynamic reward function, enabling the agent to switch between ``high-speed propulsion'' and ``high-efficiency energy-saving'' strategies according to requirements. This research not only reveals the synergistic relationship between structural compliance and active control but also demonstrates the potential of deep reinforcement learning in exploring optimal control strategies without prior knowledge. It provides a new research path and theoretical support for the intelligent regulation of bionic fish caudal fins and the design of flexible underwater robots.
Funding Information
  • Chongqing Water Conservancy Science and Technology Project (CQSLK-2024021)
  • Chongqing Deepflow Intelligent Computing Technology Co.,Ltd.Innovation & Research Fund

This publication has 39 references indexed in Scilit: