Rate My Professor Arménio Costa

Fernando Arménio da Costa Castro e Fontes, known as Arménio Costa, held positions at the Instituto Superior de Engenharia de Coimbra (ISEC), part of the Instituto Politécnico de Coimbra in the Engineering faculty. Official records in the Diário da República document his progression from licenciado assistente in 1999-2000 to professor auxiliar and professor associado, with appointments and leave approvals noted through 2009. He earned his first degree in Electrical and Computer Engineering from the University of Porto, an M.Sc. in Control Systems from Imperial College London, a Ph.D. in Electrical and Electronic Engineering from Imperial College London, and Habilitation in Electrical and Computer Engineering from the University of Porto in 2014. Early in his career, he worked in the Department of Mathematics at the University of Minho, serving as Director of the first degree in Applied Mathematics from 2003 to 2005, Deputy Head of Department from 2002 to 2005, and Head of Department from 2006 to 2007. He also taught in the Department of Operational Research at the London School of Economics and was a Research Assistant at the Centre for Process Systems Engineering, Imperial College London. In 2015-2016, he was a Visiting Scholar at Texas A&M University’s Department of Electrical and Computer Engineering.

Since 2009, he has been Associate Professor with Habilitation in the Department of Electrical and Computer Engineering at the Faculty of Engineering, University of Porto (FEUP), and a researcher at SYSTEC – Institute of Systems and Robotics – Porto (ISR), an Associated Laboratory of ARISE. He coordinates the LSCOE laboratory of Systec and the thematic line Intelligent Systems and Robotics of LA ARISE. His research specializations encompass optimization and control theory, particularly nonlinear and constrained problems, model predictive control, optimal control, and applications in robotics, automation, aerospace, renewable energy including Airborne Wind Energy Systems, where he leads the UPWIND project. Key publications include 'A general framework to design stabilizing nonlinear model predictive controllers' (Systems & Control Letters, 2001), 'Min-max model predictive control of nonlinear systems using discontinuous feedbacks' (IEEE Transactions on Automatic Control, 2003), 'Nondegenerate necessary conditions for nonconvex optimal control problems with state constraints' (Journal of Mathematical Analysis and Applications, 1999), and 'Normality and nondegeneracy for optimal control problems with state constraints' (Journal of Optimization Theory and Applications, 2015). He teaches courses in Mathematics, Signal Processing, Systems and Control, Automation, and Robotics.