Nonlinear model predictive control for processes with complex dynamics: A parameterisation approach using Laguerre functions

Maciej Ławryńczuk

Abstract

Classical model predictive control (MPC) algorithms need very long horizons when the controlled process has complex dynamics. In particular, the control horizon, which determines the number of decision variables optimised on-line at each sampling instant, is crucial since it significantly affects computational complexity. This work discusses a nonlinear MPC algorithm with on-line trajectory linearisation, which makes it possible to formulate a quadratic optimisation problem, as well as parameterisation using Laguerre functions, which reduces the number of decision variables. Simulation results of classical (not parameterised) MPC algorithms and some strategies with parameterisation are thoroughly compared. It is shown that for a benchmark system the MPC algorithm with on-line linearisation and parameterisation gives very good quality of control, comparable with that possible in classical MPC with long horizons and nonlinear optimisation.
Author Maciej Ławryńczuk (FEIT / AK)
Maciej Ławryńczuk,,
- The Institute of Control and Computation Engineering
Journal seriesInternational Journal of Applied Mathematics & Computer Science, ISSN 1641-876X
Issue year2020
Vol30
No1
Pages35-46
Publication size in sheets0.55
Keywords in Englishprocess control, nonlinear model predictive control, Laguerre functions, linearisation
ASJC Classification1701 Computer Science (miscellaneous); 2201 Engineering (miscellaneous); 2604 Applied Mathematics
Abstract in original languageClassical model predictive control (MPC) algorithms need very long horizons when the controlled process has complex dynamics. In particular, the control horizon, which determines the number of decision variables optimised on-line at each sampling instant, is crucial since it significantly affects computational complexity. This work discusses a nonlinear MPC algorithm with on-line trajectory linearisation, which makes it possible to formulate a quadratic optimisation problem, as well as parameterisation using Laguerre functions, which reduces the number of decision variables. Simulation results of classical (not parameterised) MPC algorithms and some strategies with parameterisation are thoroughly compared. It is shown that for a benchmark system the MPC algorithm with on-line linearisation and parameterisation gives very good quality of control, comparable with that possible in classical MPC with long horizons and nonlinear optimisation.
DOIDOI:10.34768/amcs-2020-0003
URL https://www.amcs.uz.zgora.pl/?action=paper&paper=1533
Languageen angielski
File
Lawrynczuk AMCS_2020.pdf 391.23 KB
Score (nominal)100
Score sourcejournalList
ScoreMinisterial score = 100.0, 17-06-2020, ArticleFromJournal
Publication indicators WoS Citations = 0; Scopus Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2018 = 1.361; WoS Impact Factor: 2018 = 1.504 (2) - 2018=1.553 (5)
Citation count*2 (2020-09-09)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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