3 research outputs found
Data-driven Linear Quadratic Tracking based Temperature Control of a Big Area Additive Manufacturing System
Designing efficient closed-loop control algorithms is a key issue in Additive
Manufacturing (AM), as various aspects of the AM process require continuous
monitoring and regulation, with temperature being a particularly significant
factor. Here we study closed-loop control of a state space temperature model
with a focus on both model-based and data-driven methods. We demonstrate these
approaches using a simulator of the temperature evolution in the extruder of a
Big Area Additive Manufacturing system (BAAM). We perform an in-depth
comparison of the performance of these methods using the simulator. We find
that we can learn an effective controller using solely simulated process data.
Our approach achieves parity in performance compared to model-based controllers
and so lessens the need for estimating a large number of parameters of the
intricate and complicated process model. We believe this result is an important
step towards autonomous intelligent manufacturing
Optimal age-specific vaccination control for COVID-19
The outbreak of a novel coronavirus causing severe acute respiratory syndrome
in December 2019 has escalated into a worldwide pandemic. In this work, we
propose a compartmental model to describe the dynamics of transmission of
infection and use it to obtain the optimal vaccination control. The model
accounts for the various stages of the vaccination and the optimisation is
focused on minimising the infections to protect the population and relieve the
healthcare system. As a case study we selected the Republic of Ireland. We use
data provided by Ireland's COVID-19 Data-Hub and simulate the evolution of the
pandemic with and without the vaccination in place for two different scenarios,
one representative of a national lockdown situation and the other indicating
looser restrictions in place. One of the main findings of our work is that the
optimal approach would involve a vaccination programme where the older
population is vaccinated in larger numbers earlier while simultaneously part of
the younger population also gets vaccinated to lower the risk of transmission
between groups
Optimal age-specific vaccination control for COVID-19 : An Irish case study
The outbreak of a novel coronavirus causing severe acute respiratory syndrome in December 2019 has escalated into a worldwide pandemic. In this work, we propose a compartmental model to describe the dynamics of transmission of infection and use it to obtain the optimal vaccination control. The model accounts for the various stages of the vaccination, and the optimisation is focused on minimising the infections to protect the population and relieve the healthcare system. As a case study, we selected the Republic of Ireland. We use data provided by Ireland's COVID-19 Data-Hub and simulate the evolution of the pandemic with and without the vaccination in place for two different scenarios, one representative of a national lockdown situation and the other indicating looser restrictions in place. One of the main findings of our work is that the optimal approach would involve a vaccination programme where the older population is vaccinated in larger numbers earlier while simultaneously part of the younger population also gets vaccinated to lower the risk of transmission between groups. We compare our simulated results with those of the vaccination policy taken by the Irish government to explore the advantages of our optimisation method. Our comparison suggests that a similar reduction in cases may have been possible even with a reduced set of vaccinations available for use