Journal article
Model Predictive Control Tuning Methods: A Review
Industrial & engineering chemistry research, v 49(8), pp 3505-3515
21 Apr 2010
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
This paper provides a review of the available tuning guidelines for model predictive control, from theoretical and practical perspectives. It covers both popular dynamic matrix control and generalized predictive control implementations, along with the more general state-space representation of model predictive control and other more specialized types, such as max-plus-linear model predictive control. Additionally, a section on state estimation and Kalman filtering is included along with auto (self) tuning. Tuning methods covered range from equations derived from simulation/approximation of the process dynamics to bounds on the region of acceptable tuning parameter values.
Metrics
Details
- Title
- Model Predictive Control Tuning Methods: A Review
- Creators
- Jorge L. Garriga - Drexel UniversityMasoud Soroush - Drexel University
- Publication Details
- Industrial & engineering chemistry research, v 49(8), pp 3505-3515
- Publisher
- American Chemical Society; Washington, DC
- Number of pages
- 11
- Grant note
- CBET-0651706 / National Science Foundation (NSF)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Chemical and Biological Engineering
- Web of Science ID
- WOS:000276554200001
- Scopus ID
- 2-s2.0-77951158435
- Other Identifier
- 991019169101804721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Web of Science research areas
- Engineering, Chemical