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About

Pooria Ghanooni is a control systems researcher whose work bridges intelligent control theory and real-world electromechanical systems. His primary research areas include adaptive and neuro-fuzzy control, intelligent motor control, and robust trajectory tracking for robotic manipulators. Ghanooni’s most impactful contribution is the development of advanced controllers for hybrid stepper motors (HSMs), where he demonstrated that a brain emotional learning-based intelligent controller (BELBIC) can achieve precise speed tracking even under model uncertainty—a significant advance for applications requiring high resolution and accuracy. This work, published in 2018, has garnered 27 citations. His most cited paper (28 citations, 2019) introduced an adaptive critic-based neuro-fuzzy controller for robust trajectory tracking, further solidifying his expertise in combining learning algorithms with classical control. More recently, Ghanooni has tackled the challenge of prescribed performance control for robotic manipulators with unknown control gains and assigned settling times, using a tangent-type barrier Lyapunov function within an adaptive backstepping framework. With over 67 total citations across his key works, Ghanooni is recognized for making intelligent control more reliable and applicable under practical constraints and uncertainties.