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 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.