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The design of the state observer for a kind of uncertain symmetric circulant composite systems based on regional pole assignment is studied. The "decentralized observer gain matrices" have been obtained by taking use of the special structure of the system. The central observer gain matrix for such a system has the same block symmetric circulant structure as the original system and can be constructed by the "decentralized observer gain matrices." Thus, the problem of designing state observer for such an uncertain system with n·N dimensions based on regional pole assignment can be transformed into the subproblems for N/2+1 or (N + 1)/2 uncertain systems with n dimensions. The central D-stabilizable state observer is also a symmetric circulant composite system. And the design of the observer makes best use of the advantage of interconnections of the system.

We investigate the performances of collective task-solving capabilities and the robustness of complex networks of automata using the density and synchronization problems as typical cases. We show by computer simulations that evolved Watts–Strogatz small-world networks have superior performance with respect to several kinds of scale-free graphs. In addition, we show that Watts–Strogatz networks are as robust in the face of random perturbations, both transient and permanent, as configuration scale-free networks, while being widely superior to Barabási–Albert networks. This result differs from information diffusion on scale-free networks, where random faults are highly tolerated by similar topologies.

Many real networks have a common topological structure called scale-free (SF) that follows a power law degree distribution, and are embedded on an almost planar space which is suitable for wireless communication. However, the geographical constraints on local cycles cause more vulnerable connectivity against node removals, whose tolerance is reduced from the theoretical prediction under the assumption of uncorrelated locally tree-like structure. We consider a realistic generation of geographical networks with the SF property, and show the significant improvement of the robustness by adding a small fraction of shortcuts between randomly chosen nodes. Moreover, we quantitatively investigate the contribution of shortcuts to transport many packets on the shortest path for the spatially different amount of communication requests. Such a shortcut strategy preserves topological properties and a backbone naturally emerges bridging isolated clusters.

The growth of world population, limitation of resources, economic problems, and environmental issues force engineers to develop increasingly efficient solutions for logistic systems. Pure optimization for efficiency, however, has often led to technical solutions that are vulnerable to variations in supply and demand, and to perturbations. In contrast, nature already provides a large variety of efficient, flexible, and robust logistic solutions. Can we utilize biological principles to design systems, which can flexibly adapt to hardly predictable, fluctuating conditions? We propose a bio-inspired "BioLogistics" approach to deduce dynamic organization processes and principles of adaptive self-control from biological systems, and to transfer them to man-made logistics (including nanologistics), using principles of modularity, self-assembly, self-organization, and decentralized coordination. Conversely, logistic models can help revealing the logic of biological processes at the systems level.

We consider a third party logistics service provider (LSP), who faces the problem of distributing different products from suppliers to consumers having no control on supply and demand. In a third party set-up, the operations of transport and storage are run as a black box for a fixed price. Thus the incentive for an LSP is to reduce its operational costs. The objective of this paper is to find an efficient network topology on a tactical level, which still satisfies the service level agreements on the operational level. We develop an optimization method, which constructs a tactical network topology based on the operational decisions resulting from a given model predictive control (MPC) policy. Experiments suggest that such a topology typically requires only a small fraction of all possible links. As expected, the found topology is sensitive to changes in supply and demand averages. Interestingly, the found topology appears to be robust to changes in second order moments of supply and demand distributions.

Self-organizing processes are crucial for the development of living beings. Practical applications in robots may benefit from the self-organization of behavior, e.g., to increase fault tolerance and enhance flexibility, provided that external goals can also be achieved. We present results on the guidance of self-organizing control by visual target stimuli and show a remarkable robustness to sensorimotor disruptions. In a proof of concept study an autonomous wheeled robot is learning an object finding and ball-pushing task from scratch within a few minutes in continuous domains. The robustness is demonstrated by the rapid recovery of the performance after severe changes of the sensor configuration.

The research on efficient routing strategies holds paramount importance in mitigating network congestion and enhancing the transmission capacity of complex networks. A Hierarchical Routing Optimization (HRO) strategy is proposed for dual-layer networks featuring both logical and physical layer structures. This strategy employs distinct methods for packet transmission at the logical and physical layers to optimize the utilization of network resources. Comparison with the improved static-weighted routing strategy (ISWR) and the improved effective routing strategy (IER) involves analyzing the relationship between the transmission capacity of the dual-layer network and the coupling method under these three routing strategies. This analysis aims to determine the optimal coupling method for the routing strategy, thereby enhancing overall network capacity. The simulation results reveal that the random coupling method proves most effective when employing the ISWR strategy, the assortative coupling method excels with the IER strategy, and the dissortative coupling method stands out when implementing the HRO strategy. In evaluating the ISWR strategy, IER strategy, and HRO strategy, it becomes evident that the HRO strategy substantially amplifies the transmission capacity of the dual-layer network. Furthermore, the HRO strategy exhibits heightened robustness against both random and deliberate attacks compared to the IER strategy.

Network robustness, which includes controllability robustness and connectivity robustness, reflects the ability of a network system to withstand attacks. In this paper, a Graph Convolutional Network (GCN) approach is proposed for predicting network robustness. In contrast to the existing Convolutional Neural Network (CNN) approach, the network topology and the node characteristics are directly used as GCN input without being converted into a grayscale image. Due to the reduction in the number of feature maps, the model size of a GCN is greatly reduced to only 1% of a CNN. Extensive experimental studies on four representative networks and six real networks have proven that the proposed approach can achieve better predictive performance with less training and running time.