Constrained robust model predictive control embedded with a new data-driven technique

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...

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Vydáno v:	IET control theory & applications Ročník 14; číslo 16; s. 2395 - 2405
Hlavní autoři:	Yang, L, Lu, J, Xu, Y, Li, D, Xi, Y
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 05.11.2020
Témata:	adaptive model predictive control AMPC closed loop systems constrained robust model predictive control control field control policy control system synthesis data‐driven concept data‐driven control methodology data‐driven model predictive control controller data‐driven RMPC algorithm data‐driven technique effective control actions historian data model estimation procedure model information model parameters predictive control Research Article robust control robust model predictive control architecture robust model predictive control framework time‐varying plant time‐varying systems uncertain systems unknown‐but‐bounded time‐independent utilising data time-varying systems AMPC adaptive model predictive control control policy constrained robust model predictive control data-driven technique control field effective control actions robust control model estimation procedure data-driven RMPC algorithm uncertain systems time-varying plant robust model predictive control architecture control system synthesis data-driven model predictive control controller historian data model information closed loop systems robust model predictive control framework data-driven concept model parameters data-driven control methodology utilising data predictive control unknown-but-bounded time-independent
ISSN:	1751-8644, 1751-8652
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Popis
Shrnutí:	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.
ISSN:	1751-8644 1751-8652
DOI:	10.1049/iet-cta.2019.1349