Nonlinear predictive control of dynamic systems represented byWiener–Hammerstein models

Maciej Ławryńczuk


This paper is concerned with computationally efficient nonlinear model predictive control (MPC) of dynamic systems described by cascade Wiener– Hammerstein models. TheWiener–Hammerstein structure consists of a nonlinear steady-state block sandwiched by two linear dynamic ones. Two nonlinear MPC algorithms are discussed in details. In the first case the model is successively linearised on-line for the current operating conditions, whereas in the second case the predicted output trajectory of the system is linearised along the trajectory of the future control scenario. Linearisation makes it possible to obtain quadratic optimisationMPCproblems. In order to illustrate efficiency of the discussed nonlinear MPC algorithms, a heat exchanger represented by the Wiener– Hammerstein model is considered in simulations. The process is nonlinear, and a classical MPC strategy with linear process description does not lead to good control result. The discussedMPC algorithms with on-line linearisation are compared in terms of control quality and computational efficiency with the fully fledged nonlinearMPCapproach with on-line nonlinear optimisation.
Author Maciej Ławryńczuk (FEIT / AK)
Maciej Ławryńczuk,,
- The Institute of Control and Computation Engineering
Journal seriesNonlinear Dynamics, ISSN 0924-090X
Issue year2016
Publication size in sheets1.05
Keywords in EnglishProcess control · Model predictive control · Wiener–Hammerstein systems · Optimisation · Linearisation
ASJC Classification2208 Electrical and Electronic Engineering; 2604 Applied Mathematics; 2210 Mechanical Engineering; 2212 Ocean Engineering; 2202 Aerospace Engineering; 2207 Control and Systems Engineering
Languageen angielski
Larynczu NonlinearDyn2016.pdf 815.99 KB
Score (nominal)45
Score sourcejournalList
ScoreMinisterial score = 40.0, 08-01-2020, ArticleFromJournal
Ministerial score (2013-2016) = 45.0, 08-01-2020, ArticleFromJournal
Publication indicators Scopus Citations = 16; WoS Citations = 11; GS Citations = 22.0; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.523; WoS Impact Factor: 2016 = 3.464 (2) - 2016=3.313 (5)
Citation count*25 (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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