Guoying Miao

This paper investigates disturbance-resistant intermittent event-triggered optimal formation control problems of second-order multi-agent systems by using the reinforcement learning method, which takes into account the influence of network damage including denial-of-service (DoS) and deception attacks, stochastic noises, and unknown external disturbances. Firstly, we propose a novel disturbance observer based on adaptive control to estimate unknown external disturbances under an event-triggered mechanism. Secondly, by use of estimation of disturbances, an innovative intermittent event-triggered optimal formation algorithm is given. By applying theories such as Lyapunov stability and stochastic stability, sufficient conditions are derived to guarantee that all agents achieve the desired formation in mean square sense. Additionally, in the model-free case, the optimal controller is solved using the least squares method, which is computationally less complex than some existing approaches. Finally, the theoretical results are effectively validated through simulation examples.

The classification of land cover types is an important task for monitoring land use. Moreover, with the continuous application of high resolution remote sensing images, the time and space span is becoming larger and larger, which greatly increases the difficulty of classification of target types. And there are few results that can effectively deal with deep land category information. Furthermore, the extraction and fusion of deep features still need to be improved. In this paper, a multi-angle attention fusion network (MAFNet) is proposed for the classification of land cover types. The network uses a 50-layer residual network as a feature extraction network, and an adaptive special-shaped window attention module (ASWA) is added to the deep layer of the network to extract deep semantic information, building the connection between global information. In addition, the multi-angle interactive attention fusion module (MIAF) is used to fuse feature maps at different levels, and an interactive attention mechanism is established at different angles. Finally, a new decoder module (De) is proposed for the decoder to adjust the fused feature map again. Through experiments on three datasets, the results show that the method proposed in this paper is more accurate than the previous network for the segmentation results of different categories. It can realize the accurate division of land types in remote sensing images, and has good generalization ability.

This article presents the inverse of the kernel operator associated with the complete quadratic Lyapunov-Krasovskii functional for coupled differential-functional equations when the kernel operator is separable. Similar to the case of time-delay systems of retarded type, the inverse operator is instrumental in control synthesis. Unlike the power series expansion approach used in the previous literature, a direct algebraic method is used here. It is shown that the domain of definition of the infinitesimal generator is an invariant subspace of the inverse operator if it is an invariant subspace of the kernel operator. The process of control synthesis using the inverse operator is described, and a numerical example is presented using the sum-of-square formulation.

This paper investigates containment control problem for multi-agent systems with input saturation. Firstly, containment control algorithms are designed for the first-order and second-order cases with input saturation. In this framework, some sufficient conditions for the existence of such containment control are proposed, which guarantees that the position states of the followers converge to the convex hull formed by leaders. Secondly, the obtained results are further extended to the high-order case. Finally, simulation examples are given to illustrate the effectiveness of our proposed method.