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Realtime l1-fault-and-state estimation for multi-agent systems

In this paper, we propose a state and fault estimation scheme for multi-agent systems. The proposed estimator is based on an l1 -norm optimization problem, which is inspired by sparse signal recovery in the field of compressive sampling. Moreover, we provide a necessary and sufficient condition such that state and fault signals are correctly estimated. The result presents a fundamental limitation of the algorithm, which shows how many faulty nodes are allowed to ensure a correct estimation. An illustrative example of a vehicle platoon is given to validate the effectiveness of the proposed approach.