A Lyapunov-Stable Adaptive Method to Approximate Sensorimotor Models for Sensor-Based Control

David Navarro-Alarcon (Corresponding Author), Jiaming Qi, Jihong Zhu, Andrea Cherubini

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

In this article, we present a new scheme that approximates unknown sensorimotor models of robots by using feedback signals only. The formulation of the uncalibrated sensor-based regulation problem is first formulated, then, we develop a computational method that distributes the model estimation problem amongst multiple adaptive units that specialize in a local sensorimotor map. Different from traditional estimation algorithms, the proposed method requires little data to train and constrain it (the number of required data points can be analytically determined) and has rigorous stability properties (the conditions to satisfy Lyapunov stability are derived). Numerical simulations and experimental results are presented to validate the proposed method.

Original languageEnglish
Article number59
JournalFrontiers in Neurorobotics
Volume14
DOIs
Publication statusPublished - 17 Sep 2020

Keywords

  • adaptive systems
  • robotics
  • sensor-based control
  • sensorimotor models
  • servomechanisms
  • visual servoing

ASJC Scopus subject areas

  • Biomedical Engineering
  • Artificial Intelligence

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