Deploying Strategy of Tethered Space Robot with Approximate Dynamic Programming

Zhiqiang Ma, Zhengxiong Liu, Chengxu Ge

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper concerns the deployment of a tethered space robot with only tension control under the optimal policy, which is generated from Q-learning iteration with fuzzy approximation. The Q-learning iteration gives rise to a feasible sequence of control input, that does not have to well consider the constrained tension, and the optimal policy is generated offline and runs onboard with the low computational requirements. Underactuated dynamics is transformed into the specified reduced-order system, which is uniformly ultimately bounded based on the analysis of the motion on the nonlinear sliding surface. Continuous inputs are generated from the interpolation strategy of discrete Q-learning iteration, which owns a better dynamic and steady-state performance. The proposed method is high real-Time, effective and efficient, which has been verified by numerical simulations.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages222-226
Number of pages5
ISBN (Electronic)9781728172927
DOIs
StatePublished - 28 Sep 2020
Event2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020 - Virtual, Asahikawa, Hokkaido, Japan
Duration: 28 Sep 202029 Sep 2020

Publication series

Name2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020

Conference

Conference2020 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2020
Country/TerritoryJapan
CityVirtual, Asahikawa, Hokkaido
Period28/09/2029/09/20

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