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 | |||||
| Pages | 147-156 | ||||
| Publication size in sheets | 0.5 | ||||
| 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 English | Model predictive control Nonlinear control performance assessment Hurst exponent Entropy Fat tails | ||||
| DOI | DOI:10.1007/978-3-030-34747-5_15 | ||||
| URL | https://link.springer.com/chapter/10.1007/978-3-030-34747-5_15 | ||||
| Language | en angielski | ||||
| File |
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| Score (nominal) | 20 | ||||
| Score source | publisherList | ||||
| Score | = 20.0, 08-04-2020, MonographChapterAuthor | ||||
| Publication indicators | = 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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