| author | = | {K. Ki\v{s} and M. Klau\v{c}o}, |
| title | = | {Neural network based explicit MPC for chemical reactor control}, |
| journal | = | {Acta Chimica Slovaca}, |
| year | = | {2019}, |
| keyword | = | {model predictive control, artifi cial neural networks, process control, continuous stirred tank reactor}, |
| volume | = | {12}, |
| number | = | {2}, |
| pages | = | {218-223}, |
| annote | = | {In this paper, implementation of deep neural networks applied in process control is presented. In
our approach, training of the neural network is based on model predictive control, which is popular for its
ability to be tuned by the weighting matrices and for it respecting the system constraints. A neural network that
can approximate the MPC behavior by mimicking the control input trajectory while the constraints on states
and control input remain unimpaired by the weighting matrices is introduced. This approach is demonstrated
in a simulation case study involving a continuous stirred tank reactor where a multi-component chemical
reaction takes place.}, |
| doi | = | {10.2478/acs-2019-0030}, |
| url | = | {https://www.uiam.sk/assets/publication_info.php?id_pub=2115} |
}