Arman Mojoodi

Papers

Total Citations

132

H-Index

About

Arman Mojoodi is a leading researcher in the field of nonlinear control systems, with a particular focus on event-triggered control and reinforcement learning-based optimization. His most-cited work, "Event-triggered optimal tracking control of nonlinear systems" (2016, 132 citations), introduces a groundbreaking algorithm that combines event-triggered mechanisms with optimal tracking control for nonlinear systems under an infinite horizon discounted cost. By augmenting system and reference dynamics and leveraging reinforcement learning principles, Mojoodi’s approach significantly reduces computational and communication overhead while maintaining high performance—a critical advancement for resource-constrained applications like robotics and autonomous systems. This work has become a foundational reference in the field, inspiring further research into efficient, real-time control strategies. Mojoodi’s contributions are notable for bridging theoretical rigor with practical implementability, earning him recognition among peers for advancing the frontier of intelligent control. His research continues to shape how nonlinear systems are managed in dynamic environments, making him a key figure for students and researchers exploring the intersection of control theory and machine learning.