|Name:||Fernando Arménio da Costa Castro e Fontes|
|Telephone:||22 508 1811|
Fernando A.C.C. Fontes is Associate Professor, with Habilitation, in the Department of Electrical and Computer Engineering, Faculty of Engineering, University of Porto (FEUP) and researcher in Systec - Institute of Systems and Robotics – Porto (ISR).
He received the first degree in Electrical and Computer Engineering from the University of Porto, the M.Sc. in Control Systems and the Ph.D. degrees from the Department of Electrical and Electronic Engineering, Imperial College London, U.K. In 2014 he received the Habilitation degree (Agregação) in Electrical and Computer Engineering from the University of Porto.
He started his academic carreer in the Department of Mathematics of University of Minho, Portugal. He taught in the Department of Operational Research at LSE - The London School of Economics, and was a Research Assistant in the Centre for Process Systems Engineering at Imperial College London. In 2015/16 he was a Visiting Scholar in the Department of Electrical and Computer Engineering at Texas A&M University.
In University of Minho he served as Director of the first degree in Applied Mathematics (2003-05), Deputy-head of Department (2002-05) and Head of the Department (2006-07). He was a member of the Officina Mathematica research centre where he coordinated the Optimization and Control Theory group during 2003-06.
In 2009, he moved to the present position at Faculty of Engineering, University of Porto. He joined the Institute of Systems and Robotics – Porto (Systec- ISR), where he coordinates the Systec-Control thematic line.
He has been teaching in the areas of Mathematics, Signal Processing, Systems and Control, as well as Automation and Robotics.
His research interests are in optimization and control theory, having a specific interest in nonlinear and constrained problems, optimal control, and model predictive control.
His main scientific contributions are in model predictive control (stability and robustness conditions for nonlinear and sampled-data systems), in optimal control (stronger forms of the maximum principle and numerical methods) and in nonlinear optimization methods (dynamic programming based and other global optimization algorithms).
Lately, he has been interested in the application of these methodologies to robotics and to energy systems.