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Abstract In this study, an electromyography (EMG) signal-based learning is integrated with a Sliding-Mode Control (SMC) law for an effective human-exoskeleton synergy. A modified Recursive Newton-Euler Algorithm (RNEA) with SMC was used to determine and control the inverse dynamics of a highly nonlinear 4 degree-of-freedom exoskeleton designed for the automation of upper-limp therapeutic exercises. The exoskeleton position and velocity, along with the raw EMG signal from the bicep Brachii muscle were used as a feedback. The root mean square (RMS) values of targeted muscles EMG are tracked in a predetermined time window to quantify an adaptive threshold value and control the torque at the exoskeleton joints accordingly. Simulations of the proposed robust control law have been done in MATLAB-Simulink. Results have shown that the designed hybrid Control strategy offers the ability to adjust the needed support instantly based on the amount of muscle engagement presented in the combined motion of the human-exoskeleton system while maintaining the state trajectory errors and input torque bounded to ±7 × 10−3 rads and ±5 N.m, respectively.
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A Hybrid Adaptive Feedback Law for Robust Obstacle Avoidance and Coordination in Multiple Vehicle Systems
This paper presents an adaptive hybrid feedback law designed to robustly steer a group of autonomous vehicles toward the source of an unknown but measurable signal, at the same time that an obstacle is avoided and a prescribed formation is maintained. The hybrid law overcomes the limitations imposed by the topological obstructions induced by the obstacle, which precludes the robust stabilization of the source of the signal by using smooth feedback. The control strategy implements a leader-follower approach, where the followers track, in a coordinated way, the position of the leader.
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- Award ID(s):
- 1710621
- PAR ID:
- 10094308
- Date Published:
- Journal Name:
- American Control Conference
- Page Range / eLocation ID:
- 616 to 621
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.23919/ACC.2018.8431064
