Learning-Based Control for Deployment of Tethered Space Robot via Sliding Mode and Zero-Sum Game

Zhiqiang Ma, Panfeng Huang, Choon Ki Ahn

Research output: Contribution to journalArticlepeer-review

Abstract

The underactuated dynamics with input saturation due to limited tension is the greatest challenge of stabilizing the deployment of tethered space robot (TSR), and existing sliding mode methods rely on the assumptions of inherently bounded control command and vanishing state-dependent disturbance. This brief proposes a nearly optimal control law based on variable construct control and neural network technique, which can get rid of the assumptions. The new reduced-order system with synchronization states can be established by the nearly optimal reaching law based on the zero-sum game strategy, which is approximately solved by the actor-disturbance-critic neural network. Meanwhile, the terminal attractor is synthesized into the sliding surface to enhance fast convergence. A numerical simulation was conducted to verify the proposed analyses, and this study's results show that the method works effectively to ensure a fast response of the underactuated dynamics under limited input and disturbance.

Original languageEnglish
Pages (from-to)1457-1461
Number of pages5
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume69
Issue number3
DOIs
Publication statusPublished - 2022 Mar 1

Keywords

  • actor-critic neural network
  • input saturation
  • Sliding mode control
  • tethered space robot
  • underactuated system

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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