Finite-time synchronization of multi-layer nonlinear coupled complex networks via intermittent feedback control

Topology analysis of Lanzhou public transport network based on double-layer complex network theory

Finite-time synchronization of drive-response systems via periodically intermittent adaptive control

Urban traffic congestion is a major nuisance for residents' daily commute, but few studies have focused on the effective combination between urban traffic condition visualization and hybrid traffic travel route recommendation. In this paper, the visualization exploration of urban transportation patterns is realized by multilayer complex transportation networks, which are constructed by taxi transportation network, bike-sharing transportation network, and urban transportation community network. Based on multilayer complex traffic networks, a genetic algorithm modified by A * search algorithm is used to generate multi-modal travel routes. The case studies prove that this method can effectively reduce the time cost of commuting between congested areas by generating hybrid traffic routes.

This paper considers finite-time synchronization of complex multi-links dynamical networks with or without internal time delays via intermittent controls. Two simple intermittent feedback controllers are designed to achieve finite-time synchronization between the drive and response system. Some novel and effective finite-time synchronization criteria are derived based on finite-time stability analysis techniques. By constructing suitable Lyapunov functions, we theoretically prove its correctness. Finally, two numerical simulation examples are given to show the effectiveness of proposed method in this paper.

This paper studies the stability and stabilization of a class of evolutionary games (EGs) with time delays via the semi‐tensor product (STP) method. First, the matrix expression of the EGs with time delays is given, and then it is converted into the form without delay equivalently. By designing Lyapunov function, a necessary and sufficient condition for the global stability of delayed EGs is obtained. To deal with the delayed EGs with multiple equilibriums, the controlled EGs with time delays is investigated. An intermittent state feedback control is proposed, in which the controller works only at certain strategy profiles. Based on the intermittent control, two necessary and sufficient conditions are provided to assure the stabilization of the controlled EGs with time delays. Some examples are worked out to illustrate the effectiveness of the theoretical results.

In the field of complex networks, understanding the spreading dynamics in multi-layer networks is crucial for real-world applications such as epidemic control and the analysis of social phenomenon. In this study, a two-layer interconnected network is established, with each layer modeled as a small-world network. To investigate the spreading dynamics on this two-layer network, the classic susceptible-infected-susceptible (SIS) model is applied to it. The governing equations for the Infection proportions on each layer are developed first, followed by the proof of the existence of steady solutions (the proportion of finally infected nodes in the network) and their analytical derivation. Then, the computational model is developed accurately track the time evolution of these solutions using the 4th-order Runge–Kutta method. Finally, a numerical investigation on the influence of parameters is carried out. Three categories of parameters are considered, including inter-layer parameters, intra-layer parameters, and recovery-related parameters. Their effects on the steady solution and the infection velocity of the SIS model on the two-layer networks are summarized. It is found that both inter-layer and intra-layer parameters significantly impact the infection dynamics, an increase in these parameters leads to an increase in the final Infection proportions and a decrease in the time to reach this state. For recovery-related parameters, there exists a maximum value due to the balance of contributions from different aspects. Besides, when the scales of the two layers are unequal, the influence of intra-layer parameters is more obvious for the layer with a smaller scale. The study may contribute to the understanding of spreading dynamics on multi-layer complex networks. It provides practical guidance for managing and controlling the spread of infections in real-world scenarios. The insights gained are valuable for related research and applications in areas like epidemiology and social network analysis.

Finite-time synchronization of delayed fuzzy inertial neural networks via intermittent control

Fixed-time output synchronization of multi-layer complex networks under dynamic event-triggering control

In this paper, finite-time global synchronization control problem for a class of nonlinear coupling Markovian switched complex networks (NCMSCNs) is investigated. Furthermore, according to differentiability of nonlinear coupling function g(x,y), g(x,y) how to affect synchronization dynamics of the class of NCMSCNs is analyzed by two viewpoints. The first is that if g(x,y) satisfies the Lipschitz condition and is derivable, the above question is discussed by taking g(x,y) = L1x+L2y, g(x,y) =–L1x+L2y, g(x,y) = L1x–L2y and g(x,y) =–L1x–L2y, where L1 > 0, L2 > 0. The second is that if nonlinear coupling function g(x,y) only satisfies the Lipschitz condition, by analyzing the differences of synchronization control rules for the class of NCMSCNs and a class of linear coupling Markovian switched complex networks (LCMSCNs), the problem is explored. Comparing the previous works [12,21,22,26,33,34], the main improvement of this paper is that the works of this paper extend the existed analyzing ideas of the finite-time global synchronization for nonlinear coupling complex networks, including NCMSCNs.

The synchronization in complex network model with time-delay nodes is investigated. The model is applicable for two sorts of complex networks. With linear feedback controller and adaptive feedback controller, positive definite functions are designed respectively. The sufficient conditions of synchronization are given by Lyapunov stability theory. Finally, numerical simulations show the effectiveness of the synchronization in complex networks.

Based on the characteristics of drone swarm such as low cost, strong integrity, and frequent information exchange, as well as the high cost of timely maintenance of traditional units. This paper proposes a swarm maintenance method based on the reliability assessment of multi-layer complex network missions, which combines the multi-layer complex network system evaluation method with the group maintenance method. On the basis of considering the problem of maintenance grouping cost, the failure mechanism of drones in different modes and the impact of drone maintenance on the system are studied. According to the failure model of single node, the complex network method is used to establish the swarm system’s topology model and evaluate the system mission reliability. The maintenance grouping strategy is optimized by using the multi-objective planning of cost and system mission reliability. Compared with the existing just in time maintenance methods, this method can greatly reduce the total maintenance cost of the swarm system maintenance under the condition of ensuring the high mission robustness of the swarm. In addition, a universal drone swarm mission scenario is used to illustrate the method, and the results verify the feasibility and effectiveness of the method.

Finite-time synchronization of inertial memristive neural networks with time delay via delay-dependent control

In this paper, we investigate the finite-time cluster synchronization problem for networks with nonlinearly coupled delayed networks of nonidentical delayed dynamic systems. We derive sufficient conditions for finite-time cluster synchronization through a proper controller based on finite-time stability theory. Some numerical examples are then given to illustrate the theoretical results.

Event-driven intermittent feedback control is a form of feedback control in which the corrective control action is only initiated intermittently when the variables of interest exceed certain threshold criteria. It has been reported in the literature that the CNS uses an event-driven intermittent control strategy to stabilize the human upright posture. However, whether the threshold criteria may change under different postural task conditions is not yet well understood. We employ a numerical study with inverted pendulum models and an experimental study with 51 young healthy individuals (13 females and 38 males; age: 27.8 ± 6.5 years) with stabilogram-diffusion, temporal and spectral analysis applied to COP (Center of Pressure) trajectories measured from these experiments to examine this aspect. The present study provides compelling evidence that inducing a natural arm swing during quiet stance appears to lead to higher sensory dead zone in neuronal control reflecting higher intermittency thresholds in active feedback control and a corresponding lower sensory dependence. Beyond the obvious scientific interest in understanding this aspect of how CNS controls the standing posture, an investigation of the said control strategy may subsequently help uncover insights about how control of quiet stance degrades with age and in diseased conditions. Additionally, such an understanding will also be of interest to the humanoid robotics community as it may lead to insights leading to improving control strategies for posture control in robots.

Finite-time and fixed-time bipartite synchronization of complex networks with signed graphs