Panagiotis Patrinos

KU Leuven, Dynamic Systems (United States)

Papers

Total Citations

109

H-Index

About

Panagiotis Patrinos is a leading researcher in optimization-based control, with a focus on real-time nonlinear model predictive control (NMPC) and constrained optimization. His major contributions include the development of the PANOC algorithm (Proximal Averaged Newton-type method for Optimal Control), which enables embedded NMPC for obstacle avoidance with nonconvex constraints—a breakthrough for autonomous systems operating in dynamic environments. This work, cited 98 times, introduced a novel modeling framework that handles generic obstacles like polytopes and ellipsoids, making it highly practical for robotics and autonomous vehicles. Patrinos also advanced penalty methods for set exclusion constraints and recently proposed the Anderson Accelerated Feasible Sequential Linear Programming (AA(d)-FSLP) algorithm, which accelerates feasibility-preserving optimization. His research bridges theoretical rigor and real-time implementation, with applications in control, robotics, and safety-critical systems. With a growing citation impact and a focus on computationally efficient solutions, Patrinos is shaping the future of embedded optimization for autonomous decision-making.