Morphing aircraft are able to keep optimal performance in diverse flight conditions. However, the change in geometry always leads to challenges in the design of flight controllers. In this paper, a new method for designing a flight controller for variable-sweep morphing aircraft is presented—dynamic inversion combined with ${\mathcal{L}}_1$ adaptive control. Firstly, the dynamics of the vehicle is analyzed and a six degrees of freedom (6DOF) nonlinear dynamics model based on multibody dynamics theory is established. Secondly, nonlinear dynamic inversion (NDI) and incremental nonlinear dynamic inversion (INDI) are then employed to realize decoupling control. Thirdly, linear quadratic regulator (LQR) technique and ${\mathcal{L}}_1$ adaptive control are adopted to design the adaptive controller in order to improve robustness to uncertainties and ensure the control accuracy. Finally, extensive simulation experiments are performed, wherein the demonstrated results indicate that the proposed method overcomes the drawbacks of conventional methods and realizes an improvement in control performance.

In the past few decades, in-flight icing has become a common problem for many missions, potentially leading to a reduction in control effectiveness and flight stability, which would threaten flight safety. One of the most popular methods to address this problem is adaptive control. This paper establishes a dynamic model of an iced high-altitude long-endurance unmanned aerial vehicle (HALE-UAV) with disturbance and measurement noise. Then, by combining multilayer perceptrons (MLP) with a nonlinear dynamic inversion (NDI) controller, we propose an MLP-NDI controller to compensate for online inversion errors and provide a brief proof of control stability. Two experiments were conducted: on one hand, we compared the MLP-NDI controller with other typical controllers; on the other hand, we evaluated its robustness and adaptiveness under different icing conditions. Results indicate that the MLP-NDI controller outperforms other typical controllers with higher tracking accuracy and exhibits strong robustness in the presence of icing errors and measurement noise, which has huge potential to ensure flight safety.

The development of unmanned aerial vehicle (UAV) formation systems has brought significant advantages across various fields. However, formation change and obstacle avoidance control have long been fundamental challenges in formation flight research, with the majority of studies concentrating primarily on quadrotor formations. This paper introduces a novel approach, proposing a new method for designing a formation adaptive factor fuzzy controller (AFFC) and an artificial potential field (APF) method based on an enhanced repulsive potential function. These methods aim to ensure the smooth completion of fixed-wing formation flight tasks in three-dimensional (3D) dynamic environments. Compared to the traditional fuzzy controller (FC), this approach introduces a fuzzy adaptive factor and establishes fuzzy rules to address parameter-tuning uncertainties. Simultaneously, improvements to the obstacle avoidance algorithm mitigate the issue of local optimal values. Finally, multiple simulation experiments were conducted. The findings show that the suggested method outperforms the proportional–integral–derivative (PID) control and fuzzy control methods in achieving formation transformation tasks, resolving formation obstacle avoidance challenges, enabling formation reconstruction, and enhancing formation safety and robustness.

Network embedding has attracted a lot of attention in different fields recently. It represents nodes in a network into a low-dimensional and dense space while preserving the structural properties of the network. Some methods (e.g. motif2Vec, RUM, and MODEL) have been proposed to preserve the higher-order structures, i.e., motifs in embedding space, and they have obtained better results in some downstream network analysis tasks. However, there still exists a significant challenge because original motifs may include redundant noise edges, and embedding entire motifs into embedding space may adversely affect the performance in downstream tasks. To overcome this problem, we propose a motifs enhancement framework for network embedding, based on edge reweighting. Through edge reweighting, the weight of redundant noise edges between motifs is decreased. Therefore, the effect of redundant noise edges will be reduced in the embedding space. We apply the edge reweighting as a preprocessing phase in network embedding, and construct the motifs enhanced network by incorporating enhanced motifs structures with the original network. By doing this, the embedding vectors from the motifs enhanced network can achieve better performance in downstream network analysis tasks. Extensive experiments are performed on two network analysis tasks (community detection and node classification) with synthetic and real-world datasets. The results show that our framework outperforms state-of-the-art network embedding methods.