Open AccessConstrained robust model predictive control embedded with a new data‐driven technique
Author(s) -
Yang L.,
Lu J.,
Xu Y.,
Li D.,
Xi Y.
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2019.1349
Subject(s) - model predictive control , control theory (sociology) , computer science , controller (irrigation) , bounded function , stability (learning theory) , data driven , novelty , control (management) , adaptive control , set (abstract data type) , robust control , online model , robustness (evolution) , field (mathematics) , control engineering , control system , artificial intelligence , engineering , machine learning , mathematics , philosophy , mathematical analysis , chemistry , theology , biology , biochemistry , agronomy , programming language , statistics , pure mathematics , electrical engineering , gene
In the control field, the adaptive model predictive control (AMPC) has the capability of taking effective control actions on unknown‐but‐bounded time‐independent or slowly time‐varying systems coupling with constraints. In essence, AMPC estimates the uncertain parameters or uncertainty set online by utilising historian data to extract model information. The model estimation procedure imposes some specific conditions on data and these extra conditions have restricted its practical use. To overcome these problems, a new data‐driven control methodology is presented that integrates the data‐driven concept into robust model predictive control (RMPC) architecture for unknown‐but‐bounded time‐independent or slowly time‐varying plant. The key novelty is to employ historian data to derive control policy and make a prediction in replacement with the complicated procedure of utilising data to estimate model parameters. A data‐driven RMPC algorithm is developed within the robust model predictive control framework with the fulfilment of recursive feasibility and stability. The authors display the highlights of the data‐driven model predictive control controller through two simulation examples. The resulting controller is verified to reduce conservativeness and increase the closed‐loop performance of the system.