In this paper, a specified-time event-triggered fuzzy adaptive control algorithm is developed to solve the consensus tracking control problem for stochastic high-order nonlinear multi-agent networks, which is intrinsically challenging due to the existence of stochasticity and high-order (positive odd integers greater than one) terms. More precisely, a novel specified-time performance function (STPF) is incorporated into time-varying high-order tan-type barrier Lyapunov function to guarantee that the tracking errors remain under time-varying constraints within specified time. Combining fuzzy logic systems with the adding on power integrator technique, an adaptive approximation policy is introduced to handle the system uncertainties. Moreover, a new switching threshold event-triggered mechanism is devised to determine the control signals updating instants, which reduces the transmission and computation burden, while resizing the triggering threshold in real-time. The Zeno phenomenon is excluded by guaranteeing that the triggering intervals is lower bounded by a positive constant. Two simulation examples are provided to demonstrate the effectiveness of the designed algorithm.

With the widespread application of unmanned aerial vehicles (UAVs) in various fields, anomaly detection in flight data has become increasingly important. However, temporal variations and temporal correlations in flight data challenging to achieve accurate anomaly detection. To address these issues, the multiscale Transformers with contrastive learning for UAV anomaly detection (MTCL-UAV) is proposed to achieve adaptive multiscale modeling. The model is based on the mixture-of-experts (MoEs) architecture, and each MoE block includes a router, expert networks (ENs), and an aggregator. The router performs a temporal decomposition to select optimal scales for each sample, and the aggregator fuses the outputs of the ENs into an integrated representation. To learn the temporal correlations of flight data and improve the model’s representation, a dual attention mechanism enhanced by contrastive learning (CL-DAM) is introduced, which captures not only intrapatch and interpatch correlation relationships but also neighborhood relationships among patches. Experiments on a real flight dataset demonstrate that MTCL-UAV not only achieves superior performance compared to other methods but also exhibits strong robustness. The code is available at https://github.com/SteelHu/MTCL-UAV

In this paper, a novel event-triggered optimizing consensus tracking control scheme is proposed for multiple unmanned aerial vehicles (UAVs) formation. First, the formation consensus tracking is transformed to an optimal control issue based on the feedforward- feedback control design framework. Subsequently, a policy iteration algorithm is developed to solve the input-constrained Hamilton-Jacobi-Isaacs (HJI) equation based on the two-player zero-sum differential game. The critic-only architecture is adopted to approximate the optimal cost function. A novel weight vector tuning law is designed to relax the common initial admissible control (IAC) condition and persistence of excitation (PE) condition by supplementing appropriate compensation terms. Moreover, a novel input-oriented relative threshold dynamic event-triggered mechanism is proposed to alleviate the control transmission burdens. Finally, the simulation results demonstrate the effectiveness of proposed consensus tracking control strategy.

This paper proposes a novel specifified-time event-triggered fuzzy adaptive consensus control methodology for stochastic high-order nonlinear multi-agent networks, which is intrinsically challenging as the existence of stochasticity and high-order (positive odd integers greater than one) terms. In contrast with the state-of-the-art methodologies, the distinguishing contributions of this work lie in that: (a) a novel specifified-time performance function (STPF) is introduced and a new STPF-based high-order asymmetric time-varying barrier Lyapunov function (BLF) is designed to guarantee the local consensus tracking errors of entire network remain within a predefifined arbitrarily small residual set in specifified-time; (b) the communication effificiency of network is proved signifificantly owing to a new high-order switching threshold event-triggered mechanism, which is capable of resizing triggering threshold in real-time; (c) the fuzzy adaptive control algorithm and adaption laws are devised by combining adding one power integrator technique and fuzzy approximators. Comparative simulations are carried out to validate the effectiveness of the proposed scheme.