Effective Phase Tracking for Bioinspired Undulations of Robotic Fish Models: A Learning Control Approach

Robotic models have been used as one of the approaches to study fish locomotion. Therefore, this paper proposes an effective control scheme that enables robotic models to mimic fin-ray undulation kinematics of live fish. We found in the experiments of robotic fin undulation that the difference betwe...

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Bibliographic Details
Published in:IEEE/ASME transactions on mechatronics 2014-02, Vol.19 (1), p.191-200
Main Authors: Tianjiang Hu, Low, K. H., Lincheng Shen, Xin Xu
Format: Article
Language:English
Subjects:
Biological system modeling
Clearing
Computational fluid dynamics
Computational fluid dynamics (CFD)
Convergence
Educational institutions
Fish
Hysteresis
iterative learning control (ILC)
Kinematics
Learning
Lipschitz condition
Operators
phase lagging
Preisach hysteresis
robotic fish models
Robotics
Robots
Tracking
Trajectory
undulating fins
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Summary:Robotic models have been used as one of the approaches to study fish locomotion. Therefore, this paper proposes an effective control scheme that enables robotic models to mimic fin-ray undulation kinematics of live fish. We found in the experiments of robotic fin undulation that the difference between the desired and actual trajectories can be significant. It is believed that the difference might be caused by the phase lagging effect. To tackle the phase tracking problem, a modified iterative learning control (ILC) scheme is proposed and implemented on the robotic fish model. Furthermore, a memory clearing operator is proposed to satisfy the Lipschitz condition. This is necessary for the convergence and feasibility of the ILC scheme. Finally, experimental results illustrate the effectiveness of the proposed learning control approach, including the memory clearing operator.
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2012.2226049