Output‐Feedback Adaptive Fuzzy Inverse Optimal Tracking Control for Hybrid‐Attack‐Affected Nonlinear Cyber‐Physical Systems

This work investigates adaptive inverse optimal tracking control for strict‐feedback nonlinear cyber‐physical systems (CPS) under hybrid cyber attacks, including false data injection (FDI) and denial of service (DoS). Initially, assumptions about the attack model are made and a new observer based on measurable signals is created to reconstruct the unmeasurable state signals. And fuzzy logic system (FLS) estimates nonlinear uncertainties. Second, during backstepping design, measurable observer state is strategically introduced, thereby mitigating impact arising from hybrid network attacks. Moreover, to ensure the semi‐global uniform ultimate boundedness of all signals in the closed‐loop system, an advanced adaptive inverse optimal tracking control strategy is developed through the minimization of the cost function. Finally, simulations validate the effectiveness and practicality of this approach.