This paper develops an observer-based neural integral sliding mode control scheme for singular Markovian jump systems subject to malicious actuator spoofing attacks. Vulnerabilities in network transmissions enable adversaries to tamper with the control signals, which substantially degrades the system's operational performance and may even lead to closed-loop instability. A three-layer feedforward neural network is adopted to approximate unknown nonlinear attack signals, where prior knowledge of the attack's upper bounds is not required. To reconstruct immeasurable system states, a mode-dependent sliding mode observer is established. An integral sliding surface with continuous value inheritance at Markovian switching instants is designed to eliminate jumps of the sliding variables induced by mode transitions. A novel neural sliding mode control law is formulated to guarantee the finite-time reachability of the sliding manifold. By virtue of the weak infinitesimal generator and linear matrix inequality techniques, tractable sufficient conditions are derived to ensure the stochastic admissibility of the closed-loop system. Finally, a practical engineering example of a direct current motor is presented to validate the state estimation, anti-attack, and steady-state tracking performance of the developed control strategy.
Citation: Zhihao Wang, Xuetong Zhang, Zihan Zhao, Guangming Zhuang. Observer-based neural integral sliding mode control for singular Markovian jump systems against actuator spoofing attacks[J]. AIMS Mathematics, 2026, 11(9): 30280-30303. doi: 10.3934/math.20261200
This paper develops an observer-based neural integral sliding mode control scheme for singular Markovian jump systems subject to malicious actuator spoofing attacks. Vulnerabilities in network transmissions enable adversaries to tamper with the control signals, which substantially degrades the system's operational performance and may even lead to closed-loop instability. A three-layer feedforward neural network is adopted to approximate unknown nonlinear attack signals, where prior knowledge of the attack's upper bounds is not required. To reconstruct immeasurable system states, a mode-dependent sliding mode observer is established. An integral sliding surface with continuous value inheritance at Markovian switching instants is designed to eliminate jumps of the sliding variables induced by mode transitions. A novel neural sliding mode control law is formulated to guarantee the finite-time reachability of the sliding manifold. By virtue of the weak infinitesimal generator and linear matrix inequality techniques, tractable sufficient conditions are derived to ensure the stochastic admissibility of the closed-loop system. Finally, a practical engineering example of a direct current motor is presented to validate the state estimation, anti-attack, and steady-state tracking performance of the developed control strategy.
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Zhihao Wang, Xuetong Zhang, Zihan Zhao, Guangming Zhuang. Observer-based neural integral sliding mode control for singular Markovian jump systems against actuator spoofing attacks[J]. AIMS Mathematics, 2026, 11(9): 30280-30303. doi: 10.3934/math.20261200
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