In this article, a data-driven design scheme of undetectable false data-injection attacks against cyber-physical systems is proposed first, with the aid of the subspace identification technique. Then, the impacts of undetectable false data-injection attacks are evaluated by solving a constrained optimization problem, with the constraints of undetectability and energy limitation considered. Moreover, the detection of designed data-driven false data-injection attacks is investigated via the coding theory. Finally, the simulations on the model of a flight vehicle are illustrated to verify the effectiveness of the proposed methods.

This paper studies the carrier landing control problem for aircraft in presence of model uncertainty, deck motion and airwake disturbances. An automatic carrier landing system (ACLS) is designed. An adaptive super-twisting control (ASTC) scheme is proposed for a class of nonlinear multivariable system by combining parameter adaptions and sliding mode differentiators, and is applied to ACLS to ensure the tracking precision, rapidity and robustness of carrier auto-landing. Simulation results show that the ASTC has better performance than the super-twisting control and PID control.

This paper studies the issues of data-driven attack detection and identification for cyber-physical systems under sparse sensor attacks. First, based on the available input and output data sets, a data-driven monitor is formulated with two objectives: attack detection and attack identification. Then, with the subspace approach, a data-driven attack detection policy is presented, wherein the attack detector is designed directly by the process data. A subspace projection-based attack identification scheme is proposed via designing a bank of projection filters to determine the locations of attacked sensors. Moreover, the sparse recovery technique is adopted to decrease the combinatorial complexity of the subspace projection based identification method. The attack identification is recast into a block-sparse recovery problem. Finally, the proposed methods are verified by the simulations on a flight vehicle system.

The effectiveness of multiple UAVs at attacking the enemy target and defending against the attacking UAVs simultaneously gained researchers’ attention due to their cost efficiency and high mission success rates. This paper explores the UAV swarm vs. swarm offense/defense confrontation decision-making while attacking the aircraft carriers on open seas. The system is developed as a multi-agent system. Precisely, every UAV is modeled as an independent agent, which is free to make decisions based on the behavioral rules of the swarm, detection radius, and enemy location. A distributed auction-based algorithm with a limited data-sharing rate ability is formulated among swarm UAVs. A critical feature of the proposed strategy is that it empowers each UAV to make decisions in real-time during the mission in the presence of relatively limited communication hardware in terms of the update rate within the UAV communication system. The algorithm parameters are optimized to obtain an improved target allocation and elimination. Simulation results are presented to show the effectiveness of the proposed Offence-Defense UAV-based swarm Strategy.

In this article, a distributed cooperative strategy based on Saturation Attack Mission (SAM) with Unmanned Aerial Vehicle (UAV) swarm is investigated. Little research has been conducted in this area, especially in relation to aerial moving targets. Firstly, the mathematical model of UAV swarm and the problem of distributed fencing under SAM are established, respectively. Secondly, aimed at target's defense system, a calculation model of assigned UAVs is designed to ensure that the minimum number of UAVs is required to carry out the mission. Then, considering one situation that entire swarm is not loaded speed measurement and at least one UAV can detect moving-target's position, a distributed cooperative control scheme without a preset formation is proposed. It includes distributed position observer and fencing controller, which inspired by swarm behaviors and consensus theory. Furthermore, the speed information of each UAV is replaced by introducing an auxiliary state. This controller realizes target-fencing without collision between UAVs. Finally, the effectiveness of the developed strategy is demonstrated through numerical simulations.