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

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

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

Adaptive output synchronization of complex delayed dynamical networks with output coupling

This article is concerned with the problem of fixed-time (FXT) and preassigned-time (PAT) synchronization for discontinuous dynamic networks by improving FXT stability and developing simple control schemes. First, some more relaxed conditions for FXT stability are established and several more accurate estimates for the settling time (ST) are obtained by means of some special functions. Based on the improved FXT stability, FXT synchronization for discontinuous networks is discussed by designing a simple controller without a linear feedback term. Besides, the PAT synchronization is also explored by developing several nontrivial control protocols with finite control gains, where the synchronized time can be prespecified according to actual needs and is irrelevant with any initial value and any parameter. Finally, the improved FXT stability and the synchronization for complex networks are confirmed by two numerical examples.

This paper investigates the cooperative output regulation problem for heterogeneous linear multiagent systems under fixed communication graphs via event-triggered control. A fully distributed event-triggered dynamic output feedback control law is proposed based on the feedforward design approach. At the same time, a fully distributed dynamic event-triggering mechanism is designed so that each agent can determine when to broadcast its information to its neighbors. Compared with existing related results, both the control law and the event-triggering mechanism in this paper are independent of any global information. It is shown that with the proposed dynamic event-triggered control strategy, the cooperative output regulation problem can be solved in a fully distributed manner by intermittent communication. Moreover, Zeno behavior can be strictly ruled out for each agent. Finally, the effectiveness of the proposed dynamic event-triggered control strategy is validated by a numerical example.

Fixed-time synchronization of multilayered complex dynamic networks via quantized variable-gain saturated control

Synchronization in complex networks is influenced by higher-order interactions and non-Gaussian perturbations, yet their mechanisms remain unclear. We investigate the synchronization and spike dynamics in a higher-order Kuramoto model subjected to Lévy noise. Using the mean order parameter, mean first-passage time, and basin stability, we identify boundaries distinguishing synchronization and incoherence. The stability index governs the tail heaviness of the probability density function for Lévy noise, while the scale parameter affects the magnitude. Synchronization weakens as the stability index decreases, and even completely disappears when the scale parameter exceeds a critical threshold. By varying coupling, we find bifurcations and hysteresis. Lévy noise smooths the synchronization transitions and requires stronger coupling compared to Gaussian white noise. We then define spikes as extreme excursions of the order parameter and study their statistical and spectral properties. The maximum number of spikes is observed at small-scale parameters. A generalized spectral analysis based on an edit distance algorithm measures the similarity between spike sequences and identifies spike patterns. These findings deepen the understanding of synchronization and extreme events in complex networks driven by non-Gaussian noise. Synchronization in complex networks, crucial for understanding phenomena in systems like neuronal networks and power grids, is affected by higher-order interactions and non-Gaussian perturbations. The authors explore a higher-order Kuramoto model with Lévy noise, revealing how stability and scale parameters influence synchronization, offering insights into network dynamics and extreme events.

Fixed/Prescribed-time synchronization of multi-layer complex networks under switching control

This paper focuses on event‐triggered sampled‐data synchronization of uncertain complex networks with time‐varying coupled delays. First of all, a discrete event‐triggered sampled‐data control scheme is adopted, which not only makes the state of the system be monitored in discrete time, but also the sampling information is effectively transmitted. The proposed event‐triggered mechanism effectively prevents Zeno behavior. In addition, we also use some novel piecewise time‐dependent Lyapunov–Krasovskii and Wirtinger inequality to handle the time‐varying delays and parameter uncertainties of complex networks. Then, synchronization criteria are given for uncertain complex networks. Finally, the simulation results show that the control scheme can significantly reduce the number of transmitted signals while maintaining the uncertain complex networks synchronization.

This paper proposes a reinforcement learning approach to the output synchronization problem for heterogeneous leader-follower multi-agent systems, where the system dynamics of all agents are completely unknown. First, to solve the challenge caused by unknown dynamics of the leader, we develop an experience-replay learning method to estimate the leader's dynamics, which only uses the leader's past state and output information as training data. Second, based on the newly estimated leader's dynamics, we design an event-triggered observer for each follower to estimate the leader's state and output. Furthermore, the experience-replay learning method and the event-triggered leader observer are co-designed, which ensures the convergence and Zeno behavior exclusion. Subsequently, to free the followers from reliance on system dynamics, a data-driven adaptive dynamic programming (ADP) method is presented to iteratively derive the optimal control gains, based on which we design a policy iteration (PI) algorithm for output synchronization. Finally, the proposed algorithm's performance is validated through a simulation.

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.

Fixed-time synchronization of discontinuous complex networks with time-varying delay and disturbances via aperiodically semi-intermittent control is investigated in this paper. Firstly, a novel lemma is introduced to prove the fixed-time stability. Then, based on the new lemma, we design a controller with less terms than that in existing literature in work time (control time) and rest time, deriving some sufficient conditions to realize synchronization of a discontinuous complex network with disturbances within a settling time. Finally, a numerical example is provided to illustrate the correctness and effectiveness of our main result.

The primary focus of this study is to investigate the control strategies of a hybrid system used in hydraulic excavators. First, the structure and evaluation target of hybrid hydraulic excavators are analyzed. Then the dynamic system model including batteries, motor and engine is built as the simulation environment to obtain control results. A so-called multi-work-point dynamic control strategy, which has both closed-loop speed PI (proportion integral) control and direct torque control, is proposed and studied in the simulation model. Simulation results indicate that the hybrid system with this strategy can meet the power demand and achieve better system stability and higher fuel efficiency.

In this study, the output synchronization problem in dynamical networks consisting of nodes that have nonlinear dynamic systems with relative degree one has been explored and solved. The system property of passivity has been found to be a useful tool in solving the output synchronization problem in complex dynamical networks. It is well known, however, that not all dynamic systems possess the passivity property. Nonetheless, it has been shown here how to use passivity in order to output synchronize complex dynamical networks. It is also known that if a nonlinear system is weakly minimum phase and has relative degree one, then it is feedback equivalent to a passive system. Thus, the feedback passivation result has been exploited to investigate and solve the output synchronization problem in dynamical networks. The conditions are found, which do not require the assumption of the negative definiteness property of the outer coupling matrix under which output synchronization of dynamical networks having nodes with relative degree one systems is achieved. Furthermore, it has been found that, when all nodes are feedback equivalent to a strictly passive system, the output synchronization criterion is accomplished with less conservative conditions. An illustrative example along with numerical and simulation results is given to demonstrate the effectiveness of the theoretical findings in this study.

In this paper, a reinforcement learning (RL) based dynamic inverse attitude control scheme is proposed for near-space vehicle (NSV). Firstly, the conventional dynamic inverse control is employed to ensure the basic capability of NSV attitude tracking. Subsequently, RL is employed to tackle the system uncertainties. Actor-critic RL method is adopted to generate a compensation control signal in order to track attitude command better. Finally, simulation results illustrate that the proposed RL based dynamic inverse control scheme can obtain a better performance compared with the conventional dynamic inverse control scheme.