Hasan Jalali
Papers
6
Total Citations
43
H-Index
4
About
Hasan Jalali is a roboticist and control engineer whose work sits at the intersection of intelligent automation, nonlinear dynamics, and machine learning. His primary research focuses on the modeling, identification, and advanced control of Delta parallel robots—high-speed manipulators critical for industrial pick-and-place applications. Jalali’s major contributions include the design and practical implementation of a neural network self-tuned inverse dynamic controller that leverages an arc length function for smooth trajectory tracking, a method that has garnered 14 citations. He has also pioneered the use of deep reinforcement learning for model-free dynamic control of Delta robots, demonstrating that accurate dynamic models are not always necessary for effective performance. In parallel, Jalali has advanced actuator identification by comparing nonlinear NN-ARX and linear ARMAX models, providing a rigorous experimental framework for controller design. His work extends to real-world applications, including a deep learning-based waste detection and recycling system integrated with a Delta robot. With over 40 total citations, Jalali’s research bridges theoretical control science and practical robotics, offering scalable solutions for automation in manufacturing and environmental sustainability.
Research Focus
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