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Abstract Here, the issue of distributed adaptive event‐triggered fault‐tolerant cooperative control (FTCC) is studied for multiple unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) in the presence of actuator faults under denial‐of‐service attacks. To save the limited communication network resources, the distributed adaptive event‐triggered FTCC scheme is investigated for the multiple UAVs and UGVs which does not require continuous information relating to its neighbours. It is proven that the tracking errors are uniformly ultimately bounded by utilizing the Lyapunov function approach. Furthermore, the Zeno behaviour is excluded with the proposed scheme. Finally, simulation studies are provided to demonstrate the efficiency of the proposed scheme.

This paper investigates the adaptive fault-tolerant formation control scheme for heterogeneous multi-agent systems consisting of unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) with actuator faults, parameter uncertainties and external disturbances under directed communication topology. Firstly, the dynamic models of UAVs and USVs are introduced, and a unified heterogeneous multi-agent system model with actuator faults is established. Then, a distributed fault-tolerant formation controller is proposed for the unified model of UAVs and USVs in the XY plane by using adaptive updating laws and radial basis function neural network. After that, a decentralized formation-tracking controller is designed for the altitude control system of UAVs. Based on the Lyapunov stability theory, it can be proved that the formation errors and tracking errors are uniformly ultimately bounded which means that the expected time-varying formation is achieved. Finally, a simulation study is given to demonstrate the effectiveness of the proposed scheme.

This article deals with incipient fault of insulated-gate bipolar transistors to improve the safety of traction systems of China Railway High-speed 5. Combining with the pulse width modulated strategy which makes signals variate periodically, the multi-mode kernel principal component analysis algorithm is proposed. It can effectively not only capture the tiny changes caused by incipient faults but also detect short-faults of insulated-gate bipolar transistors in electrical systems. In feature space of every mode, different thresholds will be formed corresponding to defined modes. The proposed scheme is tested in experimental setup of traction system of China Railway High-speed 5 with incipient fault and short-circuit fault, and experimental results show that the multi-mode kernel principal component analysis has superior monitoring performance compared to other five methods.