Papers
6
Total Citations
222
H-Index
4
About
A.M.S. Hamouda is a robotics and control systems researcher whose work has made significant contributions to the field of robot manipulator kinematics and intelligent control. Specializing in the application of artificial neural networks (ANNs) and adaptive learning algorithms to robotic systems, Hamouda has consistently addressed some of the most challenging problems in robotic control, particularly inverse kinematics, singularity handling, and trajectory tracking for serial manipulators. Among his most impactful contributions is his development of adaptive-learning algorithms for solving the inverse kinematics problem of 6 DOF serial robot manipulators, a paper that has garnered over 100 citations since its publication in 2006. Closely related work on ANN-based Jacobian solutions for manipulators navigating singular configurations has accumulated nearly 100 citations, underscoring the lasting relevance of his approaches. His research is notable for enabling robotic systems to operate without requiring explicit kinematic models, instead leveraging neural networks to learn complex, nonlinear system behaviors directly. More recently, Hamouda has extended his expertise to higher-degree-of-freedom systems, exploring backstepping fuzzy control strategies for anthropomorphic manipulators. His body of work reflects a sustained commitment to advancing intelligent, adaptive control methodologies that bridge theoretical robotics and practical implementation challenges.
Research Focus
Key Achievements
Top Papers
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- 3A new adaptive learning algorithm for robot manipulator control11 citations · 2007
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