Iman Sharifi
Papers
9
Total Citations
60
H-Index
4
About
Iman Sharifi is a control systems researcher whose work spans advanced robotics, autonomous vehicles, and model predictive control. His research is anchored in two primary domains: intelligent control of mobile and rehabilitation robots, and the coordinated motion of autonomous vehicle platoons. Sharifi's most significant contribution lies in developing Laguerre-based Model Predictive Control (LMPC) frameworks for nonholonomic mobile robots operating under uncertain, slippery conditions — a practically critical challenge addressed through robust Linear Matrix Inequality (LMI) formulations. His 2023 work on vehicle platoon control, combining Laguerre-based and robust MPC to handle complex merge and exit maneuvers, has garnered 32 citations, reflecting its relevance to the rapidly growing field of connected autonomous vehicles. Beyond autonomous navigation, Sharifi has made notable contributions to rehabilitation robotics, proposing model-free impedance controllers with singularity avoidance and L1 adaptive control schemes enhanced by Gaussian Process Regression for safe human-robot interaction. His more recent explorations into reinforcement learning-based compensation and soft robot curvature tracking demonstrate a forward-looking research trajectory embracing emerging paradigms in intelligent control, making his body of work a valuable reference for researchers across robotics and control engineering.
Research Focus
Key Achievements
Top Papers
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- 8PPO and SAC Reinforcement Learning Based Reference Compensation2 citations · 2024
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