Adaptive Neural replication and resilient control despite malicious attacks

摘要

In this paper, an Adaptive Neural Control (ANC) architecture is used for system replication and control within a Resilient Control framework. A dynamic model is chosen for our plant and a “maliciously attacked” plant. A Model Reference Adaptive Control (MRAC) architecture is used to replicate and control the plant to match an ideal reference system. At certain time, we replicate a malicious attack by changing plant parameters, injecting false data, or altering sensor data. This attacked plant is then replicated and controlled to match the reference system. Simulations were carried out to show that accurate system replication and resilient control is possible using adaptive neural networks.