  With the growing share of renewable energy sources, the uncertainty in power
supply is increasing. In addition to the inherent fluctuations in the
renewables, this is due to the threat of deliberate malicious attacks, which
may become more revalent with a growing number of distributed generation units.
Also in other safety-critical technology sectors, control systems are becoming
more and more decentralized, causing the targets for attackers and thus the
risk of attacks to increase. It is thus essential that distributed controllers
are robust toward these uncertainties and able to react quickly to disturbances
of any kind. To this end, we present novel methods for model-based
identification of attacks and combine them with distributed model predictive
control to obtain a resilient framework for adaptively robust control. The
methodology is specially designed for distributed setups with limited local
information due to privacy and security reasons. To demonstrate the efficiency
of the method, we introduce a mathematical model for physically coupled
microgrids under the uncertain influence of renewable generation and
adversarial attacks, and perform numerical experiments, applying the proposed
method for microgrid control.
