TY - GEN
T1 - Deep learning-based approximate optimal control of a reaction-wheel-actuated spherical inverted pendulum
AU - Baimukashev, Daulet
AU - Sandibay, Nazerke
AU - Rakhim, Bexultan
AU - Varol, Huseyin Atakan
AU - Rubagotti, Matteo
N1 - Funding Information:
This work was partially supported by the Nazarbayev University Faculty Development Competitive Research Grant no. 090118FD5339, “Hardware and Software Based Methods for Safe Human-Robot Interaction with Variable Impedance Robots”.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - 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.
AB - 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.
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U2 - 10.1109/AIM43001.2020.9158920
DO - 10.1109/AIM43001.2020.9158920
M3 - Conference contribution
AN - SCOPUS:85090398465
T3 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
SP - 1322
EP - 1328
BT - 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2020
Y2 - 6 July 2020 through 9 July 2020
ER -