Uncertainty Analysis of a Fault Detection and Isolation Scheme for Multi-Agent Systems

Abstract: Diagnostic techniques in model-based fault detection and isolation approaches are often based on residuals. If the residuals become greater than a certain threshold then an alarm can be triggered. However, disturbances, such as those caused by model uncertainty, affect the behavior of the residuals and therefore the performance of the diagnostic system. Fault detection becomes a matter of security when applied in multi-agent systems, since their distributed nature offers adversaries possibilities to attack the system. This thesis considers disturbances caused by model uncertainty which is often encountered during implementation. Their influence on a model-based fault detection and isolation scheme in multi-agent systems is analyzed and an evaluation technique for the residuals is proposed. Different attack scenarios are considered and their influence on the residuals will be discussed. Finally, experimental results circumstantiate the proposed approaches.