This article investigates the adaptive neural network (NN) output-feedback event-triggered consensus secure control problem for a class of nonlinear multi-agent systems (MASs) under mixed sensor attacks and actuator faults. Since the considered nonlinear MASs contain unknown nonlinear dynamics, the NNs are first adopted to model unknown agents. Then, a nove NN learning secure state observer is proposed to estimate the sensor attacks and unmeasured states. To reduce unnecessary updating times of the actuator, an event-triggered mechanism is constructed. By using the backstepping control design technique and the design NN state observer, a NN adaptive output-feedback event-triggered consensus secure control scheme is formulated. It is proved that the developed consensus secure control scheme can guarantee the controlled nonlinear MASs are stable and consensus tracking errors converge even under mixed sensor attacks and actuator faults. Simulation and comparative results illustrate the effectiveness of the proposed scheme.

This article investigates the fuzzy adaptive output-feedback consensus control problem for nonlinear multiagent systems with network communication encountering deception attacks. Fuzzy logic systems are used to approximate unknown nonlinear functions, and a novel fuzzy state observer is established to estimate the states of encountered deception attacks. For the case of unknown time-varying gains caused by deception attacks, a projection operator-based compensation mechanism is designed. Consequently, under the framework of adaptive backstepping, a novel adaptive fuzzy output-feedback consensus control method is proposed. The proposed control method can not only ensure that all signals remain bounded, but also make all followers maintain consensus with the trajectory of the leader. Finally, the simulation results are given to confirm the effectiveness of the proposed control scheme.

This article proposes secure state estimation for cyber-physical systems against sensor attacks. The attack and defense strategies are established via additional historical data, and the defender aims to reduce the estimation error maximally while the attacker aims to degrade the system performance maximally. The algorithm is implemented in the Nash equilibrium framework where the defender first designs the defense strategy and then the attacker designs corresponding attack parameters to launch attacks. Then, a robust optimization problem is formulated using Wasserstein ambiguity sets, which turn out to be equivalent to a convex program. A novel secure observer is proposed, where the attack estimation is used to mitigate attacks. Moreover, the detector is to monitor system behavior and detects the existence of sensor attacks. Finally, simulation results and comparative results illustrate the effectiveness of the defense strategy. The attack and defense strategies are established via additional historical data, and the defender aims to reduce the estimation error maximally while the attacker aims to degrade the system performance maximally.Then, a robust optimization problem is formulated in the Nash equilibrium framework using Wasserstein ambiguity sets. Then, a novel secure observer is proposed, where the attack estimation is used to mitigate attacks.image

This article investigates the adaptive control problem for nonlinear cyber‐physical systems with network communication encountered false data injection (FDI) attacks. To address such attacks, the attack estimate method is designed whose objective is to minimize the vulnerability of FDI attacks. This article aim to find, using the historical FDI attack, a solution with guaranteed out‐of‐sample forecasting, so as for the attacker to plan its attacks such that the worst possible action on the system measurement. The approach is to formulate a robust optimization problem using the box‐like sets, and then transform it into a linear programming model for solving problems. Consequently, under the framework of backstepping, a robust adaptive state‐feedback control method is proposed. By using Lyapunov stability theory, the proposed control scheme can guarantee that all the closed‐loop signals are globally bounded and the stabilization error converges to the origin. Finally, simulation results illustrate the effectiveness of the proposed control scheme.

This paper investigates the neural network (NN) adaptive consensus output-feedback control problem for a class of nonlinear multi-agent systems (MASs) encountered sensor attacks. To overcome the impact of unknown sensor attacks, a NN estimation algorithm is adopted to estimate the unknown sensor attacks. Subsequently, a novel NN observer is established to estimate the states of encountered sensor attacks. Consequently, under the framework of backstepping control design, an adaptive NN consensus control method is proposed. By using the Lyapunov stability theory, the proposed consensus control method can not only ensure that all the signals of controlled MASs remain bounded, but also make all followers maintain consensus with the trajectory of the leader. Simulation results and comparative results illustrate the effectiveness of the proposed consensus control scheme.