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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Vydané v:IET control theory & applications Ročník 14; číslo 16; s. 2395 - 2405
Hlavní autori: Yang, L, Lu, J, Xu, Y, Li, D, Xi, Y
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: The Institution of Engineering and Technology 05.11.2020
Predmet:
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