Raul‐Cristian Roman

About

Raul-Cristian Roman is a researcher specializing in control systems, mobile robotics, and nature-inspired optimization. His work focuses on developing data-driven and model-free control strategies, as well as applying metaheuristic algorithms to solve complex robotics problems. Roman's most cited paper (43 citations) introduces Grey Wolf Optimizer-based approaches for path planning and fuzzy logic-based tracking control of mobile robots, demonstrating how swarm intelligence can enhance autonomous navigation. He is also known for pioneering a mixed Model-Free Adaptive Control (MFAC) and Virtual Reference Feedback Tuning (VRFT) framework (39 citations), which automatically determines control parameters without requiring a mathematical model of the system—a significant advancement for nonlinear and unstable platforms. His research extends to experimentally validated models of two-wheeled mobile robots, addressing the challenges of self-balancing and nonlinear dynamics. Roman's contributions bridge theoretical optimization and practical robotics, offering scalable solutions for autonomous systems. His work has garnered over 100 citations, reflecting its impact on both control theory and applied robotics.

Research Focus

Key Achievements

Top Papers

1. 1
   Grey Wolf Optimizer-Based Approaches to Path Planning and Fuzzy Logic-based Tracking Control for Mobile Robots

   43 citations · 2020
2. 2
   Data-driven Model-Free Adaptive Control Tuned by Virtual Reference Feedback Tuning

   39 citations · 2016
3. 3
   Data-driven Model-Free Adaptive Control Tuned by Virtual Reference

   8 citations · 2016
4. 4
   Models of Two-Wheeled Mobile Robots with Experimental Validation

   5 citations · 2020
5. 5
   Nature-Inspired Optimization Algorithms for Path Planning and Fuzzy Tracking Control of Mobile Robots

   5 citations · 2021

Key Collaborators

Contact & Links