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With the growing share of renewable energy sources, the uncertainty in powersupply is increasing. In addition to the inherent fluctuations in therenewables, this is due to the threat of deliberate malicious attacks, whichmay become more revalent with a growing number of distributed generation units.Also in other safety-critical technology sectors, control systems are becomingmore and more decentralized, causing the targets for attackers and thus therisk of attacks to increase. It is thus essential that distributed controllersare robust toward these uncertainties and able to react quickly to disturbancesof any kind. To this end, we present novel methods for model-basedidentification of attacks and combine them with distributed model predictivecontrol to obtain a resilient framework for adaptively robust control. Themethodology is specially designed for distributed setups with limited localinformation due to privacy and security reasons. To demonstrate the efficiencyof the method, we introduce a mathematical model for physically coupledmicrogrids under the uncertain influence of renewable generation andadversarial attacks, and perform numerical experiments, applying the proposedmethod for microgrid control.

Resilient Model Predictive Control of Distributed Systems Under Attack Using Local Attack Identification