Ionela Prodan
Université Grenoble Alpes, Institut polytechnique de Grenoble
Papers
3
Total Citations
23
H-Index
3
About
Ionela Prodan is a leading researcher in optimization-based control, with a particular focus on mixed-integer representations and model predictive control (MPC) for complex, constrained systems. Her foundational work, *Mixed-Integer Representations in Control Design* (2015, 17 citations), provides rigorous mathematical frameworks for handling non-convex constraints—critical for applications like collision and obstacle avoidance in autonomous systems. This contribution has shaped how researchers formulate feasible sets in optimization-based control design. More recently, Prodan has advanced real-time motion planning for multi-agent systems, introducing a distributed MPC approach using online B-spline trajectories (2024). Her work on integrating computed-torque control laws within nonlinear MPC schemes (2019) further demonstrates her ability to bridge theoretical stability guarantees with practical implementation, ensuring stable linear closed-loop dynamics beyond the prediction horizon. With a career spanning foundational theory to cutting-edge swarm robotics, Prodan’s research is essential reading for anyone working in constrained control, autonomous navigation, or distributed optimization. Her contributions continue to influence both academic research and real-world robotic applications.
Research Focus
Key Achievements
Top Papers
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