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.

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.

The paper investigates containment control for multi-agent systems under Markov switching topologies. By using graph theory and the tools of stochastic analysis, sufficient conditions of mean square containment control problems are derived for the second-order multi-agent systems. Then the obtained results are further extended to high-order multi-agent systems.

The paper investigates $H_{\infty }$ consensus problem of heterogeneous multi-agent systems including agents with first- and second-order integrators in the presence of disturbance and communication time delays under Markov switching topologies. Based on current messages, outdated information stored in memory and communication time delay information from neighbors, a more general kind of distributed consensus algorithm is proposed, which is faster consensus convergence. By applying stochastic stability analysis, model transformation techniques and graph theory, sufficient conditions of mean square consensus and $H_{\infty }$ consensus are obtained, respectively. Finally, simulation examples are given to illustrate the effectiveness of obtained theoretical results.