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About

Raffaele Iervolino’s research centers on robotics, control systems, and dynamic simulation, with a focus on improving the efficiency and precision of robotic manipulators and autonomous systems. His most cited work, “New Results on Robot Modeling and Simulation” (2006, 9 citations), introduced a novel method for simulating forward dynamics using only the inertia matrix and kinetic energy gradient—an approach that is simpler and more numerically efficient than classical techniques. This contribution has influenced subsequent work in robot simulation. Iervolino has also advanced trajectory tracking for uncertain systems, developing PD-type controllers with compensation for applications like vehicle platooning and antenna scanning (2018, 3 citations). His earlier work on flexible manipulators employed linear matrix inequalities (LMIs) to design optimal tracking control laws (2002, 2 citations), bridging rigid and flexible robot control. More recently, he has explored switching control strategies for policy selection in stochastic dynamic programming (2024, 3 citations), extending his impact into decision-making under uncertainty. With a career spanning over two decades, Iervolino’s contributions to robot modeling and control continue to inform both theoretical developments and practical engineering solutions.