Control Quality Assessment of Nonlinear Model Predictive Control Using Fractal and Entropy Measures

Paweł Domański , Maciej Ławryńczuk

Abstract

Industry faces the winds of change with the new era of Industry 4.0 paradigm. Systems require flexible and stringent operation on the edge of technological limitations. Process and control quality are closely coupled affecting simultaneously the overall plant performance within such an environment. Nonlinear model predictive control (MPC) is considered as the top quality control strategy used in the most challenging tasks. Control quality assessment of nonlinear MPC is required to supervise and maintain its operation. This work discusses efficiency of control quality non-Gaussian and nonlinear measures applied to nonlinear MPC of a polymerization reactor benchmark.
Author Paweł Domański (FEIT / AK)
Paweł Domański,,
- The Institute of Control and Computation Engineering
, Maciej Ławryńczuk (FEIT / AK)
Maciej Ławryńczuk,,
- The Institute of Control and Computation Engineering
Pages147-156
Publication size in sheets0.5
Book Walter Lacarbonara, Balakumar Balachandran, Ma Jun, Machado J.A.Tenreiro , Gabor Stepan (eds.): Nonlinear Dynamics and Control Proceedings of the First International Nonlinear Dynamics Conference (NODYCON 2019), Volume II, 2020, Springer, ISBN 978-3-030-34746-8, 349 p., DOI:10.1007/978-3-030-34747-5
Keywords in EnglishModel predictive control Nonlinear control performance assessment Hurst exponent Entropy Fat tails
DOIDOI:10.1007/978-3-030-34747-5_15
URL https://link.springer.com/chapter/10.1007/978-3-030-34747-5_15
Languageen angielski
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Domański Lawrynczuk NDC2020.pdf 760.78 KB
Score (nominal)20
Score sourcepublisherList
ScoreMinisterial score = 20.0, 08-04-2020, MonographChapterAuthor
Publication indicators GS Citations = 3.0
Citation count*5 (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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