Ali Deylami
Papers
5
Total Citations
77
H-Index
5
About
Ali Deylami is an emerging researcher specializing in robotics and control systems, with a particular focus on cooperative manipulator systems, adaptive control, and uncertainty approximation techniques. His work addresses one of the most challenging problems in modern robotics: enabling multiple robotic arms to collaboratively manipulate payloads with precision, even in the presence of system uncertainties, disturbances, and incomplete sensor data. Deylami's most notable contributions center on applying Function Approximation Technique (FAT)-based methods to design model-free controllers for cooperative robotic systems. His 2021 paper on FAT-based robust adaptive control, which has garnered 25 citations, introduced velocity-measurement-free solutions for pose and force control in electrically driven multi-manipulator systems — a significant practical advancement for real-world deployments. Complementing this, his observer-based approaches leveraging novel mathematical operators, including Mastroianni operators, Bernstein–Chlodowsky approximators, and q-analogue Bernstein–Schurer–Stancu operators, demonstrate a sophisticated and creative application of approximation theory to robotics control problems. With over 75 cumulative citations across five publications spanning just two years, Deylami has established a productive and focused research trajectory, making meaningful contributions to intelligent robot control that bridge rigorous mathematics and practical engineering challenges.
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