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Optimal PID controller tuning using stochastic programming techniques
Author(s) -
Renteria Jose A.,
Cao Yankai,
Dowling Alexander W.,
Zavala Victor M.
Publication year - 2018
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.16030
Subject(s) - computer science , benchmarking , stochastic programming , pid controller , set (abstract data type) , construct (python library) , dynamic programming , inference , controller (irrigation) , control engineering , mathematical optimization , engineering , artificial intelligence , algorithm , mathematics , agronomy , marketing , business , biology , programming language , temperature control
We argue that stochastic programming provides a powerful framework to tune and analyze the performance limits of controllers. In particular, stochastic programming formulations can be used to identify controller settings that remain robust across diverse scenarios (disturbances, set‐points, and modeling errors) observed in real‐time operations. We also discuss how to use historical data and sampling techniques to construct operational scenarios and inference analysis techniques to provide statistical guarantees on limiting controller performance. Under the proposed framework, it is also possible to use risk metrics to handle extreme (rare) events and stochastic dominance concepts to conduct systematic benchmarking studies. We provide numerical studies to illustrate the concepts and to demonstrate that modern modeling and local/global optimization tools can tackle large‐scale applications. The proposed work also opens the door to data‐based controller tuning strategies that can be implemented in real‐time operations. © 2017 American Institute of Chemical Engineers AIChE J , 64: 2997–3010, 2018