Adaptive security control for uncertain delayed semi-Markov jump systems subject to cyber attacks and actuator failures

This note investigates the adaptive security control issue for uncertain delayed semi-Markov jump systems (DSMJSs) within the framework of sliding mode control (SMC), in which the DSMJSs are affected by generally unknown transition rates (GUTRs), actuator failures (AFs) and cyber attacks. By the virtue of the strong approximation ability of neural network (NN), an adaptive NN-based SMC synthesis is carried out, which could not only force the state trajectories onto the proposed sliding surface but also ensure the DSMJSs operate as demanded in spite of the interference errors, structural uncertainty, hidden AFs, cyber attacks and GUTRs. Then, in view of the reachability of the proposed linear-type sliding mode surface (SMS), linear matrix inequalities (LMIs) and stochastic stability theory, a novel stochastically stable criterion for the resultant DSMJSs is obtained. At last, the single-link robot arm model is offered as an instance with simulation to illustrate the viability of the devised strategy.