Trajectory Tracking Control of a Bionic Robotic Fish Based on Iterative Learning
Trajectory Tracking Control of a Bionic Robotic Fish Based on Iterative Learning
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基于迭代学习的仿生机器鱼轨迹跟踪控制
DOI：
10.1007/s11432-019-2760-5
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通讯作者：
Junzhi Yu
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文献类型：
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作者：
Ming Wang;Yanlu Zhang;Huifang Dong;Junzhi Yu
仿生机器鱼具有巨大的潜在应用前景。仿生机器鱼的高机动性游动控制一直是机器鱼领域的研究热点之一。本文提出了一种迭代学习方法来解决机器鱼游动的轨迹跟踪控制问题。首先，建立了多关节仿生机器鱼的动力学模型。以三关节机器鱼为例，利用拉格朗日法得到了三关节仿生机器鱼动力学方程的统一表达式。其次，设计了仿生机器鱼的迭代学习控制器。然后证明了迭代学习控制器的收敛性。最后，进行了基于迭代学习的轨迹跟踪控制仿真实验。仿真结果表明，基于迭代学习的仿生机器鱼轨迹跟踪控制方法是有效的。
A bionic robotic fish has great potential application prospect. High maneuverability swimming control of a bionic robotic fish has been one of the research hotspots in the robotic fish field. In this paper, an iterative learning method has been proposed to solve the trajectory tracking control problem of robotic fish swimming. First, a dynamic model of the multi-joint bionic robotic fish is established. By considering a three-joint robotic fish as an example, the unified expression of the dynamic equation of the three-joint bionic robotic fish is obtained by Lagrange method. Second, the iterative learning controller for controlling the bionic robotic fish is designed. Then the convergence of the iterative learning controller is proved. Finally, the trajectory tracking control simulation experiment based on iterative learning is conducted. The simulation results show that the trajectory tracking control method based on iterative learning for a bionic robotic fish is effective.