Papers
10
Total Citations
459
H-Index
7
About
Mouhacine Benosman is a control systems researcher whose work spans flexible robotics, adaptive control, and learning-based optimization. He first gained widespread recognition through his seminal 2004 survey on the control of flexible manipulators, which has accumulated over 338 citations and remains a foundational reference in the field. This comprehensive work synthesized decades of progress in controlling complex multi-link flexible robots, establishing Benosman as a leading authority in the area. His early contributions also include innovative approaches to stable inversion of nonminimum phase linear systems, demonstrated experimentally on flexible arm robots, earning an additional 58 citations. Over time, Benosman's research evolved toward adaptive and learning-based control, where he developed modular frameworks combining robust nonlinear feedback with intelligent optimization strategies — including extremum seeking, Bayesian optimization, and iterative learning — to handle systems with time-varying and parametric uncertainties. More recently, he has extended his expertise into reinforcement learning, exploring trajectory-centric model-based methods for nonlinear systems. Across his career, Benosman has consistently bridged theoretical rigor with practical application, contributing tools that are valuable to both robotics engineers and control theorists seeking principled solutions to complex, real-world dynamical challenges.
Research Focus
Key Achievements
Top Papers
- 1Control of flexible manipulators: A survey338 citations · 2004
- 2
- 3
- 4Learning‐based iterative modular adaptive control for nonlinear systems11 citations · 2018
- 5Modeling and Control of Flexible Robots9 citations · 2007
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- 7End-effector Motion Planning for One-Link Flexible Robot8 citations · 2000
- 8
- 9Accurate Trajectory Tracking of Flexible Arm End-Point6 citations · 2000
- 10Local Policy Optimization for Trajectory-Centric Reinforcement Learning2 citations · 2020