Hamed Rahimi Nohooji

About

Hamed Rahimi Nohooji is a robotics and control systems researcher whose work sits at the intersection of intelligent control, machine learning, and robotic manipulation. He is best known for his pioneering contributions to neural adaptive control strategies for robot manipulators, particularly in developing constrained and uncertainty-aware frameworks that enhance precision and safety in robotic systems. His 2020 paper on constrained neural adaptive PID control has garnered 89 citations, establishing him as a significant voice in adaptive robotics control, while his subsequent work on actor–critic learning-based PID control reflects his ongoing commitment to integrating reinforcement learning into classical control paradigms. Nohooji's research extends into human–robot interaction, where his neural impedance adaptation framework (45 citations) advances assistive robotics, and into mobile manipulator trajectory optimization using Pontryagin's minimum principle. His more recent innovations include TRIGGER, a lightweight universal jamming gripper for aerial grasping, and topology-optimized soft robotic fingers, demonstrating a versatile range spanning both rigid and compliant robotic systems. His early survey on machine learning applications in robot manipulators further highlights his role as a bridge-builder between computational intelligence and practical robotics engineering. With over 400 cumulative citations, Nohooji's body of work represents a sustained and impactful contribution to intelligent robotic systems research.

Research Focus

Key Achievements

Top Papers

1. 1
   Constrained neural adaptive PID control for robot manipulators

   89 citations · 2020
2. 2
   Neural adaptive tracking control for an uncertain robot manipulator with time-varying joint space constraints

   65 citations · 2018
3. 3
   Path Planning of Mobile Elastic Robotic Arms by Indirect Approach of Optimal Control

   46 citations · 2011
4. 4
   Neural impedance adaption for assistive human–robot interaction

   45 citations · 2018
5. 5
   Actor–critic learning based PID control for robotic manipulators

   44 citations · 2023
6. 6
   Neural network adaptive control design for robot manipulators under velocity constraints

   32 citations · 2017
7. 7
   The Classification of the Applicable Machine Learning Methods in Robot Manipulators

   26 citations · 2012
8. 8
   TRIGGER: A Lightweight Universal Jamming Gripper for Aerial Grasping

   25 citations · 2023
9. 9
   Optimal point-to-point motion planning of non-holonomic mobile robots in the presence of multiple obstacles

   25 citations · 2013
10. 10
    Development of Topology Optimized Bending-Twisting Soft Finger

    18 citations · 2021

Key Collaborators

Contact & Links