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Dynamic event‐triggered sliding mode security control for Markovian jump systems: Learning‐based iteration optimization method

This work investigates the sliding mode control (SMC) of Markovian jump systems under communication constraints subject to unavailable states and deception attack. To reduce the information communication, a dynamic event‐triggered (DET) scheme is employed to regulate the transmission of measured outputs, in which the transmitted signals may be maliciously attacked. Meanwhile, a mode estimator is utilized to estimate the unavailable system mode. Then, an asynchronous static output‐feedback SMC law is co‐designed with the DET strategy, by which the influence of deception attacks can be completely accommodated and the closed‐loop systems are ensured to be stochastically ultimate bounded. Moreover, a learning‐based iteration optimization algorithm is developed to minimize the convergence domain. Finally, the numerical simulations are given to illustrate the proposed control scheme.