Cyberattack Detection for a Class of Nonlinear Multiagent Systems Using Set-Membership Fuzzy Filtering

This article studies cyberattack detection in discrete-time leader-following nonlinear multiagent systems subject to unknown but bounded system noises. The Takagi–Sugeno fuzzy model is used to approximate the nonlinear systems over the true value of the state. A new method is developed for simultaneous distributed cyberattack detection and leader-following consensus control. The method is based on a fuzzy set-membership filtering consisting of two steps: a prediction and a measurement update. An estimation ellipsoid set is found by updating the prediction ellipsoid set with the current sensor measurement data. Two criteria are provided to detect cyberattacks that inject malicious signals into sensor data, communication channel signals, and control signals based on the intersection between the ellipsoid sets. If there is no intersection between the prediction set and the estimation set of an agent at the current time instant, then a cyberattack on its sensors is declared. Control or communication signals of an agent are under a cyberattack if their prediction sets have no intersection with the estimation sets updated at the previous time instant. Recursive algorithms are proposed for solving the consensus protocol and calculating the two ellipsoid sets. Two cyberattack recovery mechanisms are introduced.