Biomimicking a Brain-Map Based BCF Mode Carangiform Swimming Behaviour in a Robotic-Fish Underwater Vehicle

摘要

The objective of this paper is to biomimic a rectilinear swimming behaviour propulsion mechanism of a carangiform robotic fish with impressive speeds and show its exceptional characteristics in the fluid environment. Major kinematic parameters analysed from Lighthill (LH) mathematical model are chosen as major parameters to be adapted. Parameter information is drawn from a brain map similar to fish memory. Based on an environment based error signal robotic fish selects the proper parameter/s value showing adaptive behaviour. A DES methodology has been used to model the brain-kinematic-map, then integrated with a set-point control to track the brain map generated reference command for fish-tail undulation. Parameter adaptation for the three parameters have been shown to successfully emulate fish swimming. Joint-position tracking results are found to be satisfactory as the error converges in quick time.