Rasoul Zahedifar

Kharazmi University, Sharif University of Technology

Papers

Total Citations

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About

Rasoul Zahedifar is a robotics researcher whose work bridges the frontiers of autonomous underwater vehicles (AUVs) and large language models (LLMs) for dynamic robot control. His primary research areas include formation control, nonlinear dynamics, and adaptive control systems. Zahedifar’s most impactful contribution is his 2019 paper, "Lyapunov-Based Formation Control of Underwater Robots," which has garnered 32 citations and addresses the formidable challenges of AUV coordination—namely, nonlinear dynamics, model uncertainty, and underactuation. This work provides a rigorous theoretical framework for stable, multi-robot formation control in complex underwater environments, advancing the reliability of autonomous data collection and inspection missions. More recently, his 2025 paper, "LLM-controller: Dynamic robot control adaptation using large language models," has already earned 10 citations, showcasing his pioneering integration of LLMs into real-time robotic control. This innovative approach enables robots to adapt their behavior on the fly, a leap forward for flexible, intelligent automation. Zahedifar’s research is vital for students and engineers seeking to master the intersection of theoretical control theory and cutting-edge AI, offering practical solutions for next-generation autonomous systems.