Deep learning-based approximate optimal control of a reaction-wheel-actuated spherical inverted pendulum

Daulet Baimukashev, Nazerke Sandibay, Bexultan Rakhim, Huseyin Atakan Varol, Matteo Rubagotti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In recent years, the robotics research community has focused on variable impedance actuation for its potential in safe physical interaction. Despite many advantages such as safety, efficiency, and dynamic adaptation, these systems usually have a low motion bandwidth due to the presence of impedance elements between the joints and the links. Presumably, reaction wheels, frequently employed in spacecraft attitude control for high bandwidth actuation, can be employed to improve the motion control performance of variable impedance robots. In order to test this hypothesis, in this work, we present the control of a dual-axis compliant inverted pendulum using reaction wheels. Two controller alternatives are considered. The first relies on the approximation of an offline optimal controller using deep neural networks (DNNs), and the second one is based on nonlinear model predictive control (NMPC). Both simulation and experimental results show successful control performance of both the DNN and NMPC controllers. However, the DNN control law can be executed in a much shorter time period than the NMPC one (0.4 ms versus 2.68 ms on average). This proves the feasibility of using approximate optimal controllers based on DNNs at high sampling rates for the control of variable impedance robots.

Original languageEnglish
Title of host publication2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1322-1328
Number of pages7
ISBN (Electronic)9781728167947
DOIs
Publication statusPublished - Jul 2020
Event2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020 - Boston, United States
Duration: Jul 6 2020Jul 9 2020

Publication series

NameIEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
Volume2020-July

Conference

Conference2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020
CountryUnited States
CityBoston
Period7/6/207/9/20

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering
  • Computer Science Applications
  • Software

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