Aminuddin Qureshi

King Fahd University of Petroleum and Minerals

Papers

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About

Aminuddin Qureshi is a control systems researcher specializing in the intersection of nonlinear dynamics, multi-agent coordination, and intelligent control. His primary research areas include port-controlled Hamiltonian (PCH) systems, adaptive neural network control, and cooperative tracking for networked agents. Qureshi’s major contributions lie in developing robust neuro-adaptive frameworks that enable distributed control of complex Hamiltonian systems under parametric uncertainties. His most-cited work, "Robust neuro‐adaptive cooperative control of multi‐agent port‐controlled Hamiltonian systems" (2015, 9 citations), introduces a novel approach to achieving cooperative tracking in directed networks, leveraging neural networks to handle system uncertainties without compromising stability. This work has been foundational for researchers exploring resilient multi-agent systems. In his subsequent paper, "Neuro-based Canonical Transformation of Port Controlled Hamiltonian Systems" (2020, 4 citations), Qureshi advances the field by employing neural networks to perform canonical transformations, simplifying the control design for PCH systems. His research bridges theoretical rigor with practical applicability, offering tools for robotics, power networks, and autonomous systems. Qureshi’s work is notable for its innovative fusion of Hamiltonian mechanics with adaptive learning, making him a key figure in the evolution of intelligent nonlinear control.