Mickael Aghajarian

Papers

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About

Mickael Aghajarian’s research lies at the intersection of robotics, intelligent control, and optimization, with a focus on solving complex kinematic and control challenges for robotic manipulators. His most influential work, “Inverse Kinematics solution of PUMA 560 robot arm using ANFIS” (2011, 28 citations), addresses the longstanding difficulty of computing inverse kinematics for high-degree-of-freedom robots. By applying an Adaptive Neuro-Fuzzy Inference System (ANFIS), Aghajarian demonstrated how machine learning can overcome the limitations of traditional geometric and algebraic methods, offering a more flexible and accurate solution for complex joint structures. In a subsequent study (2012, 12 citations), he advanced the design of fuzzy controllers for robot manipulators by employing a Bacterial Foraging Optimization Algorithm, replacing inefficient trial-and-error tuning with an automated, biologically inspired approach. This work highlights his commitment to integrating nature-inspired algorithms with fuzzy logic to enhance robotic performance. Aghajarian’s contributions are particularly valuable for researchers and students exploring intelligent control systems and soft computing in robotics, providing practical frameworks for optimizing both kinematics and controller design in real-world applications.