Wang Yu, Wang Jian, Dong Huijie, Chen Di, Kong Shihan, Yu Junzhi
The School of Intelligence Science and Technology, The Institute of Artificial Intelligence, University of Science and Technology Beijing, Beijing 100083, China.
The Laboratory of Cognitive and Decision Intelligence for Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Biomimetics (Basel). 2025 Aug 20;10(8):548. doi: 10.3390/biomimetics10080548.
Fault-tolerant control for bionic robotic fish presents significant challenges due to the complex dynamics and asymmetric propulsion introduced by joint failures. To address this issue, this paper proposes a fault-tolerant following control framework for multi-joint bionic robotic fish by combining fuzzy control methodologies and dynamic model correction. Firstly, offline fault analysis is conducted based on the dynamic model under multi-variable parameter conditions, quantitatively deriving influence factor functions that characterize the effects of different joint faults on velocity and yaw performance of the robotic fish. Secondly, an adaptive-period yaw filtering algorithm combined with an improved line-of-sight navigation method is employed to accommodate the motion characteristics of bionic robotic fish. Thirdly, a dual-loop following control strategy based on fuzzy algorithms is designed, comprising coordinated velocity and yaw control loops, where velocity and yaw influence factors serve as fuzzy controller inputs with expert experience-based rule construction. Finally, extensive numerical simulations are conducted to verify the effectiveness of the proposed method. The obtained results indicate that the bionic robotic fish can achieve fault-tolerant following control under multiple fault types, offering a valuable solution for underwater operations in complex marine environments.
由于关节故障引入的复杂动力学和不对称推进，仿生机器人鱼的容错控制面临重大挑战。为解决这一问题，本文结合模糊控制方法和动态模型校正，提出了一种多关节仿生机器人鱼的容错跟踪控制框架。首先，基于多变量参数条件下的动态模型进行离线故障分析，定量推导表征不同关节故障对机器人鱼速度和偏航性能影响的影响因素函数。其次，采用结合改进视线导航方法的自适应周期偏航滤波算法，以适应仿生机器人鱼的运动特性。第三，设计了一种基于模糊算法的双环跟踪控制策略，包括协调的速度和偏航控制环，其中速度和偏航影响因素作为模糊控制器输入，并基于专家经验构建规则。最后，进行了大量数值模拟以验证所提方法的有效性。所得结果表明，仿生机器人鱼在多种故障类型下均可实现容错跟踪控制，为复杂海洋环境中的水下作业提供了有价值的解决方案。
