B.H.M. Bukkems

Papers

Total Citations

H-Index

About

B.H.M. Bukkems is a researcher whose work lies at the intersection of robotics, adaptive control, and iterative learning. His key contributions focus on advancing control strategies for robotic systems, particularly direct-drive robots, where precision and adaptability are critical. Bukkems is best known for his work on online identification and batch adaptive control, as demonstrated in his 2003 paper "Online identification of a robot using batch adaptive control," which has garnered 4 citations. This research addresses the challenge of real-time system identification, enabling robots to adapt to dynamic environments without prior knowledge of their dynamics. In his equally notable 2003 paper "Frequency domain iterative learning control for direct-drive robots" (3 citations), Bukkems introduced an Iterative Learning Control (ILC) algorithm that assumes linear dynamics, achieved through a nonlinear model-based compensator. By deriving convergence criteria in the frequency domain, he provided a robust framework for designing learning controllers that improve performance over repeated tasks. Though his citation counts are modest, Bukkems’ work offers foundational insights into the integration of adaptive and learning control, making it valuable for researchers exploring precision robotics and automated manufacturing.