A motion control approach for a robotic fish with iterative feedback tuning

This paper proposes a model-free motion control approach for a robotic fish. The fish-like swimming gaits first are generated by a general internal model (GIM)-based learning approach for the robot. Then, a feedforward controller and a proportional-integral-derivative (PID)-based feedback controller...

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Bibliographic Details
Main Authors: Qinyuan Ren, Jianxin Xu, Xuefang Li
Format: Conference Proceeding
Language:English
Subjects:
Adaptive control
Feedforward neural networks
Motion control
Oscillators
Robot kinematics
Tuning
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Summary:This paper proposes a model-free motion control approach for a robotic fish. The fish-like swimming gaits first are generated by a general internal model (GIM)-based learning approach for the robot. Then, a feedforward controller and a proportional-integral-derivative (PID)-based feedback controller are scheduled to control the swimming gaits of the robot to achieve desired motion. To improve the performance of the feedback controller and avoid tedious manual tuning, a pure data-driven iterative feedback (IFT) method is adopted for tuning the parameters of the feedback controller. Finally, experiment results verify the effectiveness of the motion control approach.
DOI:10.1109/ICIT.2015.7125073