A Multi-Observer Based Estimation Framework for Nonlinear Systems under Sensor Attacks
It addresses the problem of secure state estimation for nonlinear systems under sensor attacks, which is important for safety-critical applications, but the contribution is incremental as it generalizes existing linear methods.
This paper proposes a state estimation and attack isolation framework for general discrete-time nonlinear systems under sensor attacks, extending prior work on linear systems to a broader class of nonlinear plants and observers. Simulations demonstrate the effectiveness of the approach.
We address the problem of state estimation and attack isolation for general discrete-time nonlinear systems when sensors are corrupted by (potentially unbounded) attack signals. For a large class of nonlinear plants and observers, we provide a general estimation scheme, built around the idea of sensor redundancy and multi-observer, capable of reconstructing the system state in spite of sensor attacks and noise. This scheme has been proposed by others for linear systems/observers and here we propose a unifying framework for a much larger class of nonlinear systems/observers. Using the proposed estimator, we provide an isolation algorithm to pinpoint attacks on sensors during sliding time windows. Simulation results are presented to illustrate the performance of our tools.