Robotic fishes kinematics SSPR modeling and adaptive iterative learning control

Guang Ren*, Ya Ping Dai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

A class of modeling and control combination method is proposed to realize the speed adjustment of robotic fish with multi-joint caudal fin. Targeting at controllability, a swing rule and propulsion speed performance reference(SSPR) model is established based on an energy conversion coefficient, and the efficiency is converged to a controllable and adjustable range by system's known parameters. Then, an adaptive iterative learning control policy is designed to match the SSPR model, the control system can identify and update the energy conversion coefficient timely and periodically, and realize the speed self-adjustment of robotic fish in a strange water environment. Simulation analysis verifies the correctness of the model and control method.

Original languageEnglish
Pages (from-to)1605-1610
Number of pages6
JournalKongzhi yu Juece/Control and Decision
Volume29
Issue number9
DOIs
Publication statusPublished - 1 Sept 2014

Keywords

  • Adaptive iterative learning control
  • Energy conversion coefficient
  • Reference model
  • Robotic fishes

Fingerprint

Dive into the research topics of 'Robotic fishes kinematics SSPR modeling and adaptive iterative learning control'. Together they form a unique fingerprint.

Cite this

Ren, G., & Dai, Y. P. (2014). Robotic fishes kinematics SSPR modeling and adaptive iterative learning control. Kongzhi yu Juece/Control and Decision, 29(9), 1605-1610. https://doi.org/10.13195/j.kzyjc.2013.0722