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This paper presents a novel approach for resilient event-triggered control for linear parameter-varying (LPV) systems under denial-of-service (DoS) attacks and stochastic deception attacks. The proposed networked control system comprises a gain-scheduled controller and an event-triggering mechanism. Besides that, a scenario where DoS attacks restrict the data rate is investigated. This is done by considering a deterministic DoS attack model that limits the frequency and duration of the DoS attack sequence on average. On the other hand, in the absence of DoS attacks the network may be subject to the occurrence of stochastic deception attacks that follow a given Bernoulli distribution. Furthermore, a switching approach provides new co-design conditions based on linear matrix inequalities (LMIs) to guarantee the mean square exponential stability of the closed-loop system even in the presence of hybrid attacks. It is also proposed an LMI-based optimization strategy to enhance resilience against the considered class of DoS attacks while minimizing the number of transmissions. Finally, a numerical example is provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This article handles the problem of periodic event‐triggered control co‐design for the stabilization of Takagi‐Sugeno fuzzy models subject to stochastic deception attacks whose occurrence follows a given Bernoulli distribution. A novel delay‐dependent condition is presented to simultaneously design the event‐triggering mechanism and the state‐feedback controller to ensure the local mean‐square asymptotic stability of the closed‐loop system. The co‐design condition is derived as linear matrix inequalities by considering Lyapunov–Krasovskii stability arguments. In addition, an optimization procedure is provided to estimate an enlarged set of admissible initial conditions and the corresponding ultimate bound within the validity domain of the fuzzy model. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]