摘要
The neural-based approaches inspired by biological neural mechanisms of locomotion are becoming increasingly popular in robot control.This paper investigates a systematic method to formulate a Central Pattern Generator(CPG) based control model for mul-timodal swimming of a multi-articulated robotic fish with flexible pectoral fins.A CPG network is created to yield diverse swim-ming in three dimensions by coupling a set of nonlinear neural oscillators using nearest-neighbor interactions.In particular,a sensitivity analysis of characteristic parameters and a stability proof of the CPG network are given.Through the coordinated con-trol of the joint CPG,caudal fin CPG,and pectoral fin CPG,a diversity of swimming modes are defined and successfully imple-mented.The latest results obtained demonstrate the effectiveness of the proposed method.It is also confirmed that the CPG-based swimming control exhibits better dynamic invariability in preserving rhythm than the conventional body wave method.
The neural-based approaches inspired by biological neural mechanisms of locomotion are becoming increasingly popular in robot control. This paper investigates a systematic method to formulate a Central Pattern Generator （CPG） based control model for multimodal swimming of a multi-articulated robotic fish with flexible pectoral fins. A CPG network is created to yield diverse swimming in three dimensions by coupling a set of nonlinear neural oscillators using nearest-neighbor interactions. In particular, a sensitivity analysis of characteristic parameters and a stability proof of the CPG network are given. Through the coordinated control of the joint CPG, caudal fin CPG, and pectoral fin CPG, a diversity of swimming modes are defined and successfully implemented. The latest results obtained demonstrate the effectiveness of the proposed method. It is also confirmed that the CPG-based swimming control exhibits better dynamic invariability in preserving rhythm than the conventional body wave method.
基金
the National Natural Science Foundation of China (60775053,61075102)
in part by the Beijing Natural Science Foundation (4102063,4122084)
关键词
网络控制
多式联运
机器鱼
中央
游泳
胸鳍
人民政府
神经机制
bio-inspired control, central pattern generator （CPG）, neural network, robotic fish, swimming control
