Current research

[2016-Present]
KhronoSim - System for Simulation and Test of Complex Systems
Role: Team Member.
Host institutions: University of Coimbra (UC); Critical Software, SA; Institute of Engineering of Porto.
Financing: co-financing by “Portugal 2020” (PT2020), in the framework of the “Competitiveness and Internationalization Operational Program” (COMPETE 2020), and by the European Union through the European Structural and Investment Funds (ESIF); Reference number: KhronoSim201617611.

Abstract: Concepts such as “Fourth Industrial Revolution (Industry 4.0)” and “Internet of the Things (IoT)” boasted into the technology speech like a blizzard, touching those who use and interest themselves of technology almost as much as those developing it. Such concepts are not surprisingly more used than understood; more are those using the concepts than those actually understanding their implications. Surprisingly enough, the increase of use of technology by the population at large, makes that security is not the least well-known aspect, though still not fully grasped however. Less well-known are the implications of complex systems working tightly coupled, with little or no human intervention, or possibility of human intervention, whatsoever. In such a scenario, testing components individually, one-by-one, is not sufficient to assert the correct functioning of the overall system. KhronoSim aims at developing a platform for testing cyber-physical systems in closed-loop. A platform that is modular, extensible and usable in multiple application domains. A platform featuring hard-real-time control, enabling the integration of simulation models to build a closed loop test environment and allowing the use of physical and virtual systems alike. The application case of the project is the simulation, control, and test of a sun-synchronous satellite.


[2015-Present]
Self-Learning Fuzzy Logic Control for Industrial Processes
Role: Post-Doctoral Researcher
Host institutions: University of Coimbra (UC); Institute of Systems and Robotics (ISR).
Financing: Foundation of Science and Technology (FCT); Grant reference - SFRH/BPD/99708/2014.

Abstract: The main objective is to research and contribute for the automatic learning of a Fuzzy Logic Controller (FLC) from data obtained from a given process while it is being manually or automatically controlled, in order to control nonlinear industrial processes. Additionally, the methodologies may also be used to understand a process for which there is little or no information available, since the FLCs are able to gather a knowledge-base about the process control. A current challenge in FLC research is to determine the most suitable fuzzy rules and membership functions of a FLC using data obtained from a given process while it is being manually or automatically controlled. Even, after the learning of the FLC, it is crucial that it can work properly over time, controlling as accurately as possible output variable of the plant, even under operating areas where the train dataset may be not sufficiently representative of the plant, and “unknown changes” of the process. To address these problems, iterative rule learning techniques will be a starting point, where for the unknown operating areaschanges the learning process may create a new fuzzy rule, modify the parameters of an existing one, or merge similar rules. Know issuesproblems with data collection, such as sampling time, missing data, and outliers will be studied due to their influence in the iterative rule learning techniques. Thus, the methodology to be developed should be efficient in terms of performance, adaptivity and robustness.