Event‐triggered model‐free adaptive control for nonlinear cyber‐physical systems with false data injection attacks

Abstract

AbstractThis article studies the event‐triggered model‐free adaptive control (ET‐MFAC) problem for a class of nonlinear cyber‐physical systems (CPSs) in the case of false data injection (FDI) attacks. The nonlinear CPSs are transformed into an equivalent linear data model, in which the nonlinear characteristics of the system are compressed into a pseudo partial derivative parameter. Then, a novel ET‐MFAC framework under FDI attacks is established and the event‐triggered mechanism is designed to save the communication resources. Based on the designed ET‐MFAC framework with FDI attacks, a new ET‐MFAC algorithm is developed to track the reference trajectory under FDI attacks. The developed control algorithm only depends on the input and output data of the system and has nothing to do with the structure of the system, which makes the result with good robustness. Finally, a simulation is given to illustrate the validity of the developed ET‐MFAC algorithm.