Teaching Control during the COVID-19 Pandemic.

This paper aims to analyze some different solutions that were adopted in control education activities during the pandemic. The authors of this paper are educators in the control education field from different countries on all the continents, who have developed a questionnaire with the idea of collecting data about the COVID-19 pandemic impact on the control education activities. The main objective is to study the diverse alternatives that were used worldwide to perform the online educational activities during that period, such as methodologies, tools, learning management systems (LMS), theoretical exercises, laboratory experiments, types of exams, simulators, software for online lecturing, etc. As a result, comparisons between pre-and during-pandemic educational resources and methods are performed, where useful ideas and discussions are given for the control education community.

Optimal control analysis and Practical NMPC applied to refrigeration systems.

This work is focused on optimal control of mechanical compression refrigeration systems. A reduced-order state-space model based on the moving boundary approach is proposed for the canonical cycle, which eases the controller design. The optimal cycle (that satisfying the cooling demand while maximizing efficiency) is defined by three variables, but only two inputs are available, therefore the controllability of the proposed model is studied. It is shown through optimization simulations how optimal cycles for a range of the cooling demand turn out not to be achieved by keeping the degree of superheating to a minimum. The Practical NMPC and a well-known feedback-plus-feedforward strategy from the literature are compared in simulation, both showing trouble in reaching the optimal cycle, which agrees with the controllability study.