Adaptive performance control of switched nonlinear systems under false data injection attacks and input saturation constraints.

Considering the controller-actuator channel subjected to false data injection (FDI) attacks, this study proposes an adaptive performance control strategy for switched nonlinear systems under composite disturbances and input saturation. The strategy achieves disturbance rejection and a more flexible prescribed performance control. In addition, implicit disturbances that are coupled with the system states in switched nonlinear systems lie beyond the capability of existing disturbance observers. Moreover, conventional prescribed performance control methods become ineffective in the presence of input saturation. To overcome these challenges, a novel switched nonlinear disturbance observer is developed to reconstruct implicit mismatched disturbances. This observer employs dynamic high gain technique to address the unknown nonlinearity of disturbances. Additionally, an adaptive update law is designed to compensate for matched disturbances. Then, a modified fixed-time performance function is introduced to balance input saturation with tracking performance. To mitigate the adverse effects of FDI attacks, the neural network approximation technique is applied within the backstepping control process. Finally, the simulation results reveal that the proposed adaptive performance control strategy achieves effective tracking control of switched nonlinear systems.