Adaptively robust nonlinear model predictive control based on attack identification

Autor:	Sarah Braun, Sebastian Albrecht, Sergio Lucia
Rok vydání:	2022
Předmět:	Control and Systems Engineering Electrical and Electronic Engineering Computer Science Applications
Zdroj:	at - Automatisierungstechnik. 70:367-377
ISSN:	2196-677X 0178-2312
Popis:	Robust model predictive control (MPC) is an essential tool for control systems under uncertainty as it allows for constraint satisfaction even if disturbances occur. When a system suffers malicious attacks, in contrast to parametric uncertainties or known systems faults, it is difficult to specify tight uncertainty ranges within which possible disturbances lie. In this case, very conservative solutions or even infeasible problems are obtained. To address this issue, we propose an adaptively robust MPC scheme that adjusts the uncertainty ranges to available knowledge about the attackers. To this end, we combine a recently proposed method identifying unknown attacks on nonlinear systems with a multi-stage approach to robust MPC. We illustrate the potential of the method in a numerical case study with a distributed nonlinear system.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c884b40fb20e93aa9784f9a8aefea959 https://doi.org/10.1515/auto-2021-0109 Zobrazit plný text záznamu