Model-free predictive control of nonlinear systems under False Data Injection attacks. (May 2022) - Zhang, Zeyu; Li, Hongran; Zhang, Heng; Zhang, Jian; Zhong, Zhaoman; Xu, Weiwei
Abstract: The control systems attacked by False Data Injection (FDI) force the equipment out of action. This paper presents a model-free predictive control framework based on polynomial regressors that attenuates adverse effects of FDI attacks on control systems modeled by the nonlinear systems. An FDI attacker targets at tampering the state estimation results, thereby destroy the security of control systems. In order to guarantee its stability, the polynomial regression vectors are considered. The novel point of this paper is the improvement of existing attack datasets by the polynomial regression which combines previous recorded datasets and attack datasets. The polynomial regression vectors can ensure the stable operation of the nonlinear systems guaranteed under FDI attacks. Finally, the simulation is employed to verify our points. Graphical abstract: Highlights: Autoregressive exogenous model can replace False Data Injection model. Volterra series expand few data to meet the demand of input data volume. Polynomial regressors ensure control systems run stably under False Data Injection attack.