.png){kind=icon}
.png){kind=icon}
Return
Propulsion Control of Bionic Robotic Fish Based on Deep Deterministic Policy Gradient Algorithm
.png){kind=icon}
OA
AI
DOI：10.1109/OJIES.2025.3606965.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Robotic fish exhibit considerable potential for a wide range of applications. However, the limitation of battery size highlights the need to improve swimming efficiency. This article develops a deep deterministic policy gradient (DDPG)-based control method that makes the stiffness of robotic fish can be adjusted dynamically. First, the mathematical model of the two-joint robotic fish is established. Then, the conventional proportional–integral–derivative control system and the DDPG-based control system are developed. In the end, the feasibility of the DDPG-based approach was validated through simulation and experiments. The results indicate that the control method improved the system efficiency by approximately 9.77%, suggesting that the proposed method holds promise as a high-efficiency propulsion control approach for robotic fish.
Keywords:
Deep deterministic policy gradient (DDPG)
deep reinforcement learning (DRL)
propulsion efficiency
robotic fish
Journal
I
IF:
4.3
Papers:
1.7K
Citations:
991