Papers
8
Total Citations
114
H-Index
6
About
Zied Ben Hazem is a prolific researcher at the forefront of robotics, control theory, and intelligent systems, whose work bridges classical engineering principles with cutting-edge machine learning techniques. His research centers on robotic manipulator control, trajectory tracking, and the application of deep reinforcement learning to complex mechanical systems. Ben Hazem has made particularly significant contributions through his extensive work on the Mitsubishi RV-2AJ 5-DOF robotic arm, developing a suite of advanced control frameworks — including model-free deep deterministic policy gradient methods, hybrid PID-reinforcement learning architectures, and fuzzy-TD3 integration — that push the boundaries of precision and robustness in robotic trajectory tracking. His highly cited comprehensive review of pendulum structures (22 citations) demonstrates a broader commitment to synthesizing foundational knowledge for the robotics and control communities. With multiple papers accumulating over 100 citations collectively in a remarkably short span, his influence is rapidly growing. Notably, his work on hybrid reinforcement learning frameworks represents a creative fusion of classical and modern control paradigms. Ben Hazem also contributes to robotics education, evidenced by his ROS-based joint control implementation work, making him a researcher who values both innovation and accessibility in engineering.
Research Focus
Key Achievements
Top Papers
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