Farshad Rahimi

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About

Farshad Rahimi’s research lies at the intersection of distributed control, multi-agent systems, and networked robotics, with a focus on enabling reliable coordination under real-world constraints like communication delays. His major contributions center on developing distributed model predictive control (MPC) frameworks that allow teams of mobile robots to achieve formation and motion coordination without centralized oversight. In his most-cited work (2018, 5 citations), Rahimi introduced a distributed MPC that uses online optimization to determine control parameters for each robot, robustly handling data delays in the communication network. He extended this approach in 2021 (3 citations) by employing a dual decomposition optimization method within a model predictive control structure, further improving coordination under information delays. Though early in his career, Rahimi’s work is notable for addressing a critical bottleneck in practical multi-robot systems: the gap between theoretical control designs and the imperfect, delayed communication channels found in real deployments. His contributions provide a foundation for scalable, delay-tolerant coordination strategies, making his research valuable for students and engineers working on autonomous drone swarms, warehouse robots, or any application where networked robots must act cohesively despite imperfect connectivity.