Data-Driven Control of an Ultracompact Industrial Robot

In recent years, automation has become more accessible to small- and medium-sized businesses, leading to an increase in popularity of ultra-compact and easy-to-integrate industrial robot arms like Mecademic’s Meca500. However, because of their size constraints, it is harder for these robots to accurately follow a programmed path. This research project aims to improve the path-tracking performance of Mecademic’s Meca500 robot by fusing state-of-the-art machine learning techniques with modern control design techniques. Improving the path-accuracy of the Meca500 will strengthen Mecademic’s competitive advantage in the fast-paced industrial automation market.

Intern: 
Steven Dahdah
Faculty Supervisor: 
James Richard Forbes
Province: 
Quebec
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