Fuzzy Unknown Input Observer for Estimating Sensor and Actuator Cyber-Attacks in Intelligent Connected Vehicles

The detection and mitigation of cyber-attacks in connected vehicle systems (CVSs) are critical for ensuring the security of intelligent connected vehicles. This paper presents a solution to estimate sensor and actuator cyber-attacks in CVSs. A novel method is proposed that utilizes an augmented system representation technique and a nonlinear unknown input observer (UIO) to achieve asymptotic estimation of both CVS dynamics and cyber-attacks. The nonlinear CVS dynamics is represented in a Takagi-Sugeno (TS) fuzzy form with nonlinear consequents, which allows for the effective use of the differential mean value theorem to handle unmeasured premise variables. Furthermore, via Lyapunov stability theory sufficient conditions are proposed, expressed in terms of linear matrix inequalities, to design TS fuzzy UIO. Several test scenarios are performed with high-fidelity Simulink-CarSim co-simulations to show the effectiveness of the proposed cyber-attack estimation method.