Nonlinear Predictive Control of Temperature in Long Duct Using Specially Designed Neural Model

Maciej Ławryńczuk


This paper describes a nonlinear Model Predictive Control (MPC) algorithm for a distributed parameter thermal system (a long duct). For prediction a specially designed neural model of the process is used. The model consists of a set of local neural sub-models, which calculate temperatures for a number of predefined locations of sensors, and a neural interpolator, which calculates the temperature for any sensor location. In order to obtain a computationally simple MPC scheme, the predicted output trajectory of the process is linearised on-line which leads to a quadratic optimisation MPC problem. It is shown that due to nonlinearity of the process, the classical MPC algorithm based on linear models is unable to give satisfactory control quality whereas the described nonlinear MPC algorithm leads to good control performance. The paper also studies the effect of model pruning (removing some of the sub-models) on the performance of MPC
Author Maciej Ławryńczuk (FEIT / AK)
Maciej Ławryńczuk,,
- The Institute of Control and Computation Engineering
Publication size in sheets0.5
Book Sarrate Ramon (eds.): 3rd Conference on Control and Fault-Tolerant Systems. Proceedings (SysTol), 2016, IEEE, ISBN 978-1-5090-0658-8, 770 p.
ProjectDevelopment of methodology of control, decision support and production management. Project leader: Zieliński Cezary, , Phone: 5102, start date 19-05-2015, end date 31-12-2016, 504/02233/1031, Completed
WEiTI Działalność statutowa
Languageen angielski
LawrynczukM SysTol16.pdf 512.85 KB
Score (nominal)15
Score sourceconferenceIndex
ScoreMinisterial score = 15.0, 02-02-2020, BookChapterMatConfByConferenceseries
Ministerial score (2013-2016) = 15.0, 02-02-2020, BookChapterMatConfByConferenceseries
Publication indicators WoS Citations = 0
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