Observer-Based Adaptive Neural Sliding Mode Control of Fuzzy Systems With Sojourn-Probability-Based Multimode Attacks

This article addresses the challenges posed by multimode denial of service (DoS) and deception attacks in observer-based sliding mode control (SMC) for fuzzy nonlinear systems. A novel multimode DoS attack model is introduced, incorporating time-varying sojourn probabilities to provide a more accurate and computationally efficient representation of the stochastic nature of these attacks. This model overcomes the limitations of traditional Markov-based models by capturing dynamic attack behaviors. Deception attacks are modeled as unbounded nonlinear functions, and adaptive neural networks (NNs) are employed to approximate their complex behaviors, significantly reducing their detrimental impact on system stability. A fuzzy sliding surface is designed based on the switching rule for sojourn probabilities, and an observer-based SMC law is proposed to ensure the mean square estimation upper bound of the fuzzy nonlinear systems, guaranteeing stability despite the presence of cyberattacks. Finally, the validity and superiority of the proposed control strategy are demonstrated through a tunnel diode circuit model.