Pedro Coutinho is a Professor at the Department of Electronics and Telecommunication Engineering (DETEL) of the State University of Rio de Janeiro (UERJ).
He is a researcher at D!FCOM lab of the Federal University of Minas Gerais (UFMG) with a Petrobras scholarship.
He received the MSc and PhD degrees in Electrical Engineering at UFMG in 2019 and 2021, respectively, and a Bachelor's in Electrical Engineering at the State University of Santa Cruz (UESC) in 2017. He held a postdoc position at UFMG with a CNPq scholarship (2021 - 2023). He was a lecturer at the Department of Electronics Engineering of UFMG (2022-2024).
His main topics of interest are robust and nonlinear control based on convex and quasi-convex optimization methods, cyber-physical systems, and real-time artificial intelligence for process monitoring.
Details on current projects and publications are available on CV Lattes.
My research program concentrates on the area of control systems. I am broadly interested in the study of robust and nonlinear control systems, usually in circumstances where interesting and relevant issues arise from sampled-data control implementations. I am also interested in evolving artificial intelligence methods for industrial process monitoring. More precisely, my main research topics include:
Convex and quasi-convex methods for robust and nonlinear control: in this context, the idea is to represent uncertain, time-varying, and/or nonlinear dynamical systems by linear differential inclusions that allow obtaining convex or quasi-convex conditions for analysis and synthesis.
Cyber-physical systems: the interest is designing resource-aware controllers for the stabilization of networked control systems considering network-induced phenomena and the presence of malicious cyber-attacks affecting communication.
Evolving systems for industrial process monitoring: the interest is employing evolving methods, such as evolving fuzzy systems and evolving neural networks, for monitoring industrial processes to achieve early detection of events. The main interest in evolving systems is to perform continual learning from data streams using models learned from scratch.