Papers
8
Total Citations
124
H-Index
6
About
Mazen Alamir is a leading figure in model predictive control (MPC) and bio-inspired robotics, whose work bridges rigorous control theory with the complex dynamics of flexible and underactuated systems. His research centers on developing pragmatic, optimization-based control strategies for challenging platforms, from continuum manipulators to snake-like and bipedal robots. A major contribution is his pioneering use of optimal control frameworks—specifically drawing from Cosserat rod theory—to solve both the forward and inverse dynamics of continuous manipulators, a breakthrough detailed in his highly cited 2022 work (48 citations). His influential 2013 monograph, *A Pragmatic Story of Model Predictive Control* (25 citations), provides accessible, self-contained algorithms that have made MPC more practical for real-world applications. Alamir has also made significant strides in bio-inspired locomotion, designing feedback laws for the 3D movement of eel-like robots (15 citations) and generating multi-step limit cycles for bipedal walkers like Rabbit (12 citations). His work on electric fish-inspired navigation further showcases his ability to solve complex inverse problems for novel robotic sensing. With a career spanning foundational theory and hands-on robotic implementation, Alamir’s research continues to shape how engineers design controllers for highly deformable and agile systems.
Research Focus
Key Achievements
Top Papers
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- 3Feedback design for 3D movement of an Eel-like robot15 citations · 2007
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- 6On solving inverse problems for electric fish like robots6 citations · 2010
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