Data-Driven-Based Event-Triggered Prescribed Performance Tracking of Nonlinear System With FDI Attacks

This paper considers a prescribed performance-based event-triggered model-free adaptive control (ET-MFAC) problem for nonlinear system without and with false data injection (FDI) attacks. First, the nonlinear system is converted into an equivalent linear data model by utilizing the dynamic linearization technique. Then, the event-triggered constrained tracking control schemes for nonlinear system without and with FDI attacks under the data-driven framework are designed, which include the following three parts: 1) a prescribed performance function and converted output signal are directly employed in the cost function, and the constraint control schemes are derived by the cost function to realize the prescribed performance requirement; 2) an event-triggered mechanism is designed on the basis of the relationship between measurement error and tracking error, which can decrease the communication burden; 3) in order to reduce the negative effect of FDI attacks on nonlinear system, a resilient security control scheme is presented. Finally, a numerical example shows the efficiency of the developed control methods without and with FDI attacks. Note to Practitioners—This paper investigates an event-triggered constrained tracking error control for the nonlinear system without and with FDI attacks. To avoid utilizing accurate model information, MFAC is introduced into the design process of presented strategies. Incorporating prescribed performance control with an event-triggered mechanism, the developed constrained tracking strategy is designed for the first time to lessen the computational burden while improving the transient and steady-state performance on system. Moreover, based on the energy constraint of FDI attackers, a novel resilient security constrained tracking strategy is developed to decrease the impact of FDI attacks on considered nonlinear system. The validity of theoretical results is carried out by a simulation.