TY - JOUR
T1 - A Unified Switching Control Framework for Continuous Robot-Assisted Training
AU - Zhang, Mingming
AU - Wang, Cui
AU - Zhang, Changqi
AU - Li, Ping
AU - Liu, Lu
PY - 2024/8
Y1 - 2024/8
N2 - Variant human conditions require the adaptation of robot-assisted training tasks, which involves the switching of robot's roles and training challenges and thus inevitably leads to the change of robotic controllers. While efforts have been devoted to either switching robot's roles or training difficulty, integrating both into a unified control framework for seamless smooth training remains elusive, as well as the corresponding system stability. In this work, we present a unified control framework to achieve smooth switching of robotic operation modes and training challenges. The unified framework consists of 1) a mode controller with two motion-dependent switching functions to achieve smooth switching of robotic operation modes, and 2) a task controller via a time-dependent switching function to adjust training challenge parameters. The overall system passivity and stability were analyzed using the Lyapunov methods. Experiments with human participants were conducted in a robot-assisted training scenario. Results validated the feasibility of the proposed unified control framework in the switching of robot-assisted training, as well as the training smoothness and safety. © 2023 IEEE.
AB - Variant human conditions require the adaptation of robot-assisted training tasks, which involves the switching of robot's roles and training challenges and thus inevitably leads to the change of robotic controllers. While efforts have been devoted to either switching robot's roles or training difficulty, integrating both into a unified control framework for seamless smooth training remains elusive, as well as the corresponding system stability. In this work, we present a unified control framework to achieve smooth switching of robotic operation modes and training challenges. The unified framework consists of 1) a mode controller with two motion-dependent switching functions to achieve smooth switching of robotic operation modes, and 2) a task controller via a time-dependent switching function to adjust training challenge parameters. The overall system passivity and stability were analyzed using the Lyapunov methods. Experiments with human participants were conducted in a robot-assisted training scenario. Results validated the feasibility of the proposed unified control framework in the switching of robot-assisted training, as well as the training smoothness and safety. © 2023 IEEE.
KW - Adaptive control
KW - Control systems
KW - Force
KW - Lyapunov methods
KW - robotic-assisted training
KW - Robots
KW - smooth switching control
KW - Springs
KW - Switches
KW - Task analysis
KW - Training
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UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85179812345&origin=recordpage
U2 - 10.1109/TMECH.2023.3330875
DO - 10.1109/TMECH.2023.3330875
M3 - RGC 21 - Publication in refereed journal
SN - 1083-4435
VL - 29
SP - 2743
EP - 2755
JO - IEEE/ASME Transactions on Mechatronics
JF - IEEE/ASME Transactions on Mechatronics
IS - 4
ER -