Efficient MPC algorithms with variable trajectories of parameters weighting predicted control errors

Robert Nebeluk , Piotr Marusak


Model predictive control (MPC) algorithms brought increase of the control system performance in many applications thanks to relatively easily solving issues that are hard to solve without these algorithms. The paper is focused on investigating how to further improve the control system performance using a trajectory of parameters weighting predicted control errors in the performance function of the optimization problem. Different shapes of trajectories are proposed and their influence on control systems is tested. Additionally, experiments checking the influence of disturbances and of modeling uncertainty on control system performance are conducted. The case studies were done in control systems of three control plants: a linear non- minimumphase plant, a nonlinear polymerization reactor and a nonlinear thin film evaporator. Three types of MPC algorithms were used during research: linear DMC, nonlinear DMC with successive linearization (NDMC–SL), nonlinear DMC with nonlinear prediction and linearization (NDMC–NPL). Results of conducted experiments are presented in greater detail for the control system of the polymerization reactor, whereas for the other two control systems only the most interesting results are presented, for the sake of brevity. The experiments in the control system of the linear plant were done as preliminary experiments with the modified optimization problem. In the case of control system of the thin film evaporator the researched mechanisms were used in the control system of a MIMO plant showing possibilities of improving the control system performance.

Author Robert Nebeluk (FEIT / AK)
Robert Nebeluk,,
- The Institute of Control and Computation Engineering
, Piotr Marusak (FEIT / AK)
Piotr Marusak,,
- The Institute of Control and Computation Engineering
Journal seriesArchives of Control Sciences, ISSN 1230-2384, e-ISSN 2300-2611
Issue year2020
Volvol. 30
NoNo 2
Publication size in sheets1.9
Keywords in Englishmodel predictive control, nonlinear systems, nonlinear models, nonlinear control, simulation, optimization
ASJC Classification2207 Control and Systems Engineering; 2606 Control and Optimization; 2611 Modelling and Simulation
URL http://journals.pan.pl/dlibra/publication/133502/edition/116649/content
Languageen angielski
Nebeluk Marusak art06.pdf 1.13 MB
Score (nominal)100
Score sourcejournalList
ScoreMinisterial score = 100.0, 03-09-2020, ArticleFromJournal
Publication indicators Scopus Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 0.908; WoS Impact Factor: 2018 = 1.559 (2)
Citation count*1 (2020-09-12)
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