Johan Ubbink
Papers
2
Total Citations
8
H-Index
2
About
Johan Ubbink is a robotics researcher whose work centers on advancing model predictive control (MPC) for robot manipulators, with a particular focus on making these sophisticated controllers more intuitive, tunable, and practical for real-world applications. His most cited paper, "From Instantaneous to Predictive Control: A More Intuitive and Tunable MPC Formulation for Robot Manipulators" (2024, 5 citations), directly addresses a critical barrier in robotics—the difficulty of tuning MPC controllers—by proposing a formulation that balances performance with user-friendly adjustability. Building on this foundation, his second highly cited work, "Contactless Surface Following with Acceleration Limits: Enhancing Robot Manipulator Performance through Model Predictive Control" (2024, 3 citations), tackles the challenge of automating surface following tasks like spray painting and inspection. By introducing an approach that enables contactless surface following within a broader dynamic range, Ubbink expands the capabilities of robots beyond slow, in-contact operations. Though early in his career, his contributions are already shaping how researchers and engineers approach predictive control for dynamic manipulation tasks, offering practical solutions that bridge the gap between theoretical control advances and deployable robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2