This article proposes a distributed neuroadaptive monitoring fault-tolerant consensus control scheme for a class of uncertain, nonlinear, strict feedback multi-agent systems which have actuator faults and all the control coefficients in them are unknown. This scheme provides each agent with a local monitor combined with the actuator switching to solve actuator failure. Simultaneously, it guarantees the tracking error satisfies the prescribed transient and steady-state performance, even if there exist actuators switching. Furthermore, the time varying asymmetric Barrier Lyapunov function (BLF) and the auxiliary system are used to analyze input and output constraints' influence. Under the action of aforementioned control scheme, closed-loop systems can be stable and semiglobal uniform boundedness. Additionally, its efficacy can be proved in numerical simulation.

Multi-agent system (MAS) is a common cyber-physical system (CPS). Due to it often uses a relatively open network platform, it is vulnerable to malicious cyber attacks in the process of system operation, so the control scheme design is critical to ensure the system operation security. In this article, we propose a new resilient neuroadaptive dynamic surface control scheme for non-linear MASs with potential cyber attacks (false data injection and denial-of-service), system uncertainty, unknown control gain, and output constraints which are usually seen on CPSs.In this scheme, the neuroadaptive controller design ensures that all signals of the closed loop system are semi-globally uniform ultimately bounded when the MAS even exists time-varying cyber attacks on data links among agents, and the links between controllers, actuators, and sensors. Using Gaussian radial basis function neural network, the system uncertainty, non-strict feedback terms, unknown control gain, and some cyber attacks are effectively solved and the process of controller design is extremely simplified. Finally, we provide a simulation result to verify the effectiveness and superiority of the proposed control scheme.

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Considering that the actual operating environment of UAV is complex and easily disturbed by the space environment of urban buildings, the RoutE Planning Algorithm of Resilience Enhancement (REPARE) for UAV 3D route planning based on the A* algorithm and artificial potential fields algorithm is carried out in a targeted manner. First of all, in order to ensure the safety of the UAV design, we focus on the capabilities of the UAV body and build a risk identification, assessment, and modeling method such that the mission control parameters of the UAV can be determined. Then, the three-dimensional route planning algorithm based on the artificial potential fields algorithm is used to ensure the safe operation of the UAV online and in real time. At the same time, by adjusting the discriminant coefficient of potential risks in real time to deal with time-varying random disturbance encountered by the UAV, the resilience of the UAV 3D flight route planning can be improved. Finally, the effectiveness of the algorithm is verified by the simulation. The simulation results show that the REPARE algorithm can effectively solve the traditional route planning algorithm’s insufficiency in anti-disturbance. It is safer than a traditional A* route planning algorithm, and its running time is shorter than that of the traditional artificial potential field route planning algorithm. It solves the problems of local optimization, enhances the UAV’s ability to tolerate general uncertain disturbances, and eventually improves resilience of the system.