Adaptive Neural Network-Based Security Asynchronous Control for
Uncertain Markov Jump Power Systems with Dead Zone under DoS Attack
Abstract
The paper deals with the security control stabilization problem of
uncertain Markov jump power systems with input dead zone under
stochastic denial-of-service (DoS) attack. DoS attack is modeled as a
discrete-time Markov process. Dual hidden Markov models are respectively
used to detect the modes of the original power systems and the one under
DoS attack. Based on the detected modes and neural networks (NNs),
adaptive NN-based security asynchronous control strategies are proposed,
where both state feedback and output feedback are studied
simultaneously. With the developed control laws, all trajectories of the
closed-loop systems are bounded stable in the stochastic setting.
Simulation results demonstrate the correctness and usefulness of the
proposed techniques.