Design of a Tube Model Predictive Controller for Pneumatic Muscle Actuators Servomechanism under Uncertainty
Jan 1, 2024·
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0 min read
Mobina Zibandehpoor
Fatemeh Rahmati
Mehdi Delrobaei
Amirhossein Nikoofard
Abstract
Pneumatic muscle actuators (PMAs) are efficient and versatile, mimicking natural muscle-tendon systems. They are widely used in manufacturing, automotive, and aerospace industries due to their lightweight design, high force-to-weight ratio, and flexibility, making them suitable for delicate tasks and powerful exertions. This research aimed to develop a PMA regulator using the Model Predictive Control (MPC) framework. Researchers identified key PMA characteristics and defined attributes for the regulator, ensuring accurate signal tracking, effective uncertainty handling, and real-time operation.
Type
Publication
10th International Conference on Control, Instrumentation and Automation (CCIA)
Status
Peer-reviewed
Authors
Mobina Zibandehpoor
(she/her)
M.Sc. Researcher
Hi, I’m Mobina, an M.Sc. mechatronics researcher passionate about non-invasive assessment of human cognitive performance. My work sits at the intersection of computer vision, biosignal analysis, and machine learning, developing multimodal frameworks that combine vision-based pose estimation, IMU motion capture, and eye tracking for interpretable behavioral analysis. I enjoy designing algorithms and experimental systems that connect mechatronic instrumentation with real-world cognitive assessment.
Please reach out if you’re interested in collaborating on Human-Computer Interaction, biosignal analysis, or AI-driven behavioral assessment!😜