Muhammad Alhaddad
Moscow Institute of Physics and Technology, University of Aleppo
Papers
7
Total Citations
31
H-Index
4
About
Muhammad Alhaddad is a robotics researcher whose work focuses on intelligent control, motion planning, and adaptive systems for robotic manipulators and mobile platforms operating in human-oriented environments. His most impactful contribution, an adaptive LQ-based computed-torque controller for robotic manipulators (10 citations), addresses the challenge of motion control under unknown dynamic parameters, combining linear quadratic control with adaptive estimation. Alhaddad has also pioneered the Neural Potential Field approach (6 citations), a novel method for obstacle-aware local motion planning that uses neural networks to represent collision costs for arbitrary robot footprints and obstacle maps—a significant advance for model predictive control in complex environments. His work on integrated control architectures for mobile manipulators in human-oriented settings (5 citations) and door-opening strategies for constrained configurations (4 citations) demonstrates a practical, application-driven approach to real-world robotics challenges. With additional contributions in adaptive control for wheeled robots and manipulators with closed kinematic chains, Alhaddad’s research consistently bridges theoretical control methods with deployable robotic systems, making his work valuable for researchers in autonomous navigation, human-robot interaction, and adaptive robotics.
Research Focus
Key Achievements
Top Papers
- 1Adaptive LQ-Based Computed-Torque Controller for Robotic Manipulator10 citations · 2019
- 2Neural Potential Field for Obstacle-Aware Local Motion Planning6 citations · 2024
- 3
- 4
- 5
- 6
- 7