Multi-Sensor Fusion Model-Free Adaptive Control for Systems with False Data Injection Attacks

A control problem of unknown nonlinear networked systems with false data injection attacks is studied in this paper. A multi-sensor fusion model-free adaptive control method with subjected to attacks based on I/O data is proposed, where the random false data injection attacks are estimated to enhance the control performance. Then the weights are optimally taken using an intelligent algorithm, moth-flame optimization algorithm. Finally, simulation is used to confirm that the suggested algorithm is available.