Motion controller design for a biomimetic robotic fish

This paper investigates motion control of a biomimetic robotic fish through a general internal model (GIM)-based learning approach. By virtue of the universal function approximation ability and the temporal/spatial seal-abilities of the GIM, the learning approach is able to generate the same or similar fish swimming motion patterns. Through experimental analysis, we also find out that motion of the robot can be controlled by simply tuning two GIM parameters. To achieve desired motion, based on tuning the GIM parameters, two model-free feedback control schemes are designed. A proportional-integral-derivative (PID) controller and a Fuzzy logic controller (FLC) are employed to control the speed and the turning diameter of the robot, respectively. Finally, experiment results verify the effectiveness of the motion control approach.