Network Control Research Articles

Disturbance Observer–Based Adaptive Dynamic Event‐Triggered Control for a Class of Nonlinear Systems With Input Saturation

ABSTRACTFor the adaptive tracking control of a class of nonlinear systems with asymmetric input saturation and external disturbances, a novel dynamic event‐triggered neural network control scheme on the foundation of full‐state constraints is proposed. Firstly, by adding a compensation term into the traditional dynamic surface control framework, the filtering error ignored generally is eliminated. Secondly, a nonlinear disturbance observer is constructed to observe the compound interference united by external disturbances and approximation errors based on the approximation ability of neural network. Thirdly, an auxiliary variable is combined with a smooth function used to deal with the saturated input function to offset the influence of input saturation constraints. In addition, for ensuring that all states of the system are constrained by preset time‐varying ranges, the adaptive control method and dynamic event‐triggered condition are posed with the help of barrier Lyapunov function. Then, the tracking performance and the boundness of all signals in the closed‐loop system can be guaranteed by employing the presented adaptive control approach with dynamic event‐triggered mechanism, as well as the Zeno behavior does not appear. Finally, the validity of the designed control method can be demonstrated in simulation examples.

Hierarchical Resource Management for Mega-LEO Satellite Constellation

The mega-low Earth orbit (LEO) satellite constellation is pivotal for the future of satellite Internet and 6G networks. In the mega-LEO satellite constellation system (MLSCS), which is the spatial distribution of satellites, global users, and their services, along with the utilization of global spectrum resources, significantly impacts resource allocation and scheduling. This paper addresses the challenge of effectively allocating system resources based on service and resource distribution, particularly in hotspot areas where user demand is concentrated, to enhance resource utilization efficiency. We propose a novel three-layer management architecture designed to implement scheduling strategies and alleviate the processing burden on the terrestrial Network Control Center (NCC), while providing real-time scheduling capabilities to adapt to rapid changes in network topology, resource distribution, and service requirements. The three layers of the resource management architecture—NCC, space base station (SBS), and user terminal (UT)—are discussed in detail, along with the functions and responsibilities of each layer. Additionally, we explore various resource scheduling strategies, approaches, and algorithms, including spectrum cognition, interference coordination, beam scheduling, multi-satellite collaboration, and random access. Simulations demonstrate the effectiveness of the proposed approaches and algorithms, indicating significant improvements in resource management in the MLSCS.

Fault-tolerant synchronization control for time-varying delay complex networks based on aperiodic intermittent event triggering

Fault-tolerant synchronization control for time-varying delay complex networks based on aperiodic intermittent event triggering

Fast finite-time quantized control of multi-layer networks and its applications in secure communication

Fast finite-time quantized control of multi-layer networks and its applications in secure communication

Automation of 5G network slicing security using intent-based networking

Network slicing is a fundamental technological advancement that facilitates the provision of novel services and solutions within the realm of 5G and the forthcoming 6G communications. Numerous challenges emerge when implementing network slicing on a large-scale commercial level since it necessitates comprehensive control and automation of the entire network. Cyberattacks, such as distributed denial of service (DDoS) and address resolution protocol (ARP) spoofing, can significantly disrupt the performance and accessibility of slices inside a multi-tenant virtualized networking infrastructure due to the shared utilization of physical resources. This article employs intent-based networking (IBN) to identify and address diverse threats through automated methods. A conceptual framework is presented in which the IBN manager is integrated into the network-slicing architecture to facilitate the implementation of automated security controls. The proposed work is assessed using an experimental test bed. The study's findings indicate that the network slice's performance exhibits improvement when successful detection and mitigation measures are implemented. This improvement is observed in various metrics: availability, packet loss, response time, central processing unit (CPU) and memory utilization.

Adaptive global predefined-time control of robotic systems with output constraints via multiple multidimensional Taylor network.

Adaptive global predefined-time control is examined for the n-link robotic system, which involves output constraints. The primary challenge in designing the controller is not only to guarantee that the output constraints are never violated, but also to achieve global convergence of the tracking error within a predefined time. First, a barrier function is introduced to transform the output constrained system into an unconstrained system. Then, the command filtering technique is incorporated into the adaptive multiple multidimensional Taylor network (MMTN) control process. Furthermore, a compensation system is constructed to alleviate for errors arising from filtering. Notably, the designed multi-switching-based adaptive MMTN controller realizes the global stability of robotic systems. Finally, a two-link robotic system simulation is presented to demonstrate the feasibility of the proposed control strategy.

Impact of sleep disruptions on gray matter structural covariance networks across the Alzheimer's disease continuum.

This study explores the impact of sleep disturbances on gray matter structural covariance networks (SCNs) across the Alzheimer's disease (AD) continuum. Amyloid-negative participants served as controls, whereas amyloid positive (A+) individuals were categorized into six groups based on cognitive status and sleep quality. SCNsfor the default mode network (DMN), salience network (SN), and executive control network (ECN) were derived from T1-weighted magnetic resonance images. In the DMN, increased structural associations were observed in cognitive unimpaired (CU) A+ and mild cognitive impairment (MCI) groups regardless of sleep quality, whereas AD with poor sleep (PS) showed a decrease and AD with normal sleep (NS) an increase. For the ECN, AD-NS showed increased and AD-PS showed reduced associations. In the SN, reduced associations were observed in CU A+ NS and MCI-NS, whereas AD-NS displayed increased associations; only AD-PS had decreased associations. Distinct SCN damage patterns between normal and poor sleepers provide insights into sleep disturbances in AD. We delineated distinct patterns of structural covariance networks (SCN) impairment across the Alzheimer's disease (AD) continuum, uncovering significant disparities between individuals with normal sleep architecture and those afflicted by sleep disturbances.These observations underscore the pivotal importance of addressing sleep disruptions in AD therapeutics, providing a refined understanding of their detrimental impact on brain networks implicated in the disease.Our investigation epitomizes methodological precision by constructing an AD continuum using amyloid positron emission tomography (PET) and cerebrospinal fluid (CSF) biomarkers to minimize diagnostic heterogeneity, further enhanced by a substantial cohort size that bolsters the robustness and generalizability of our findings.

Driving brain state transitions via Adaptive Local Energy Control Model.

The brain, as a complex system, achieves state transitions through interactions among its regions and also performs various functions. An in-depth exploration of brain state transitions is crucial for revealing functional changes in both health and pathological states and realizing precise brain function intervention. Network control theory offers a novel framework for investigating the dynamic characteristics of brain state transitions. Existing studies have primarily focused on analyzing the energy required for brain state transitions, which are driven either by the single brain region or by all brain regions. However, they often neglect the critical question of how the whole brain responds to external control inputs that are driven by control energy from multiple brain regions, which limits their application value in guiding clinical neurostimulation. In this paper, we proposed the Adaptive Local Energy Control Model (ALECM) to explore brain state transitions, which considers the complex interactions of the whole brain along the white matter network when external control inputs are applied to multiple regions. It not only quantifies the energy required for state transitions but also predicts their outcomes based on local control. Our results indicated that patients with Schizophrenia (SZ) and Bipolar Disorder (BD) required more energy to drive the brain state transitions from the pathological state to the healthy baseline state, which is defined as Hetero-state transition. Importantly, we successfully induced Hetero-state transition in the patients' brains by using the ALECM, with subnetworks or specific brain regions serving as local control sets. Eventually, the network similarity between patients and healthy subjects reached baseline levels. These offer evidence that the ALECM can effectively quantify the cost characteristics of brain state transitions, providing a theoretical foundation for accurately predicting the efficacy of electromagnetic perturbation therapies in the future.

Modeling and adaptive NN control for SMA flexible actuating systems with modified GPI hysteresis model

In this study, a shape memory alloy (SMA) actuating system is constructed to achieve flexible actuation ability. To improve the actuating performance, an effective control scheme needs to be developed to address the negative effects caused by the nonlinear nature of the internal SMA material and accommodate complex operating conditions. A modified generalised Prandtl-Ishlinskii (MGPI) hysteresis model is proposed to accurately characterise the strong saturated asymmetric hysteresis nonlinearity in the SMA wires. The modification of the input shape function allows for greater flexibility in controller design. Using this hysteresis modelling approach, an adaptive neural network (NN) control with a command filter is designed to achieve superior control performance. Experimental results validate the effectiveness of the proposed controller strategy.

Association between the attentional network efficiency and change of direction speed ability in young male Indian footballers

IntroductionInteractions between cognitive functions and sports-specific motor actions are crucial for strategic sports performance. Change of direction speed (CODS) is an essential motor ability required for rapid positional maneuvering in football. Although CODS lacks perceptual judgment and anticipatory elements of higher-level cognition, its connection with fundamental cognitive abilities cannot be undermined. The attentional networks is the basis of the fundamental cognitive abilities controlling complex behavior. The present study aimed to investigate the association between CODS ability and the efficiency of alerting, orienting, and executive components of the attentional networks, and decision-making in footballers.MethodsSeventy-eight male footballers (age: 15.4 ± 0.87 years, BMI: 19.4 ± 1.98 kg/m2) during pre-season completed a battery of field tests comprising Illinois agility test (IAT), 30 m sprint, standing broad jump, and Yo-Yo test. Attentional network components and decision-making ability were tested in the participants with computerized Attentional Network Test-Interactions (ANT-I) and choice reaction time (CRT) tasks in the laboratory set-up. A 2(alerting) ×3 (orienting) ×2 (executive) repeated measures ANOVA tested interactions between the attentional network components. Partial correlation was conducted between the physical (field tests) and cognitive test scores adjusted for age and BMI.ResultsCODS ability measured with IAT was significantly correlated [r = +0.507 (large), p < 0.05] with the executive control network only, nor with alerting [r = −0.039 (trivial), p > 0.05] and orienting [r = + 0.051 (trivial), p > 0.05] networks and neither the CRT task performance [r = −0.011 (trivial), p > 0.05].DiscussionA strong positive association between executive control and preplanned CODS indicates better interference control by the attentional network. The later may be a factor for faster CODS execution in young footballers. Hence, it may be concluded that better CODS ability is possibly an outcome of innate competence in executive control of the attentional network in young male footballers. These findings attempted to fill the knowledge gap by highlighting the importance of the attentional network functions in modulating CODS ability. The outcomes can benefit football training by implementing ANT-I test in sports-specific settings and for screening purposes. However in the future, a large-scale study including female footballers is required to strengthen this claim further.

Synchronization control of complex networks with bit-rate constraint via dynamic event-triggered strategy

A sampling-based dynamic event-triggered strategy is proposed for the synchronization control of continuous complex networks with uncertainties and delayed coupling, taking into account a bit-rate constraint in node communication. First, a synchronization error system is established by integrating the dynamics of complex networks with those of an isolated node. To conserve limited network bandwidth resources, a sampling-based dynamic event-triggered strategy is designed. By introducing bit-rate constraints to characterize the communication limitations of the system, the available bit rate for each node is allocated using an average allocation protocol. Subsequently, a binary encoding–decoding method is developed to transmit the triggered data, and the decoded signals are utilized to design the controller. Then, a closed-loop synchronization error system is derived. Given that coding errors are inevitable, an exponentially ultimately bounded definition of synchronization error is introduced. The coupling and transmission delays within the system are addressed by constructing Lyapunov-Krasovskii (L-K) functions. Utilizing Lyapunov stability theory and other technical theorems, the exponentially ultimately bounded condition for the closed-loop system is obtained. Based on this condition, the co-design of the controller gain and triggering parameters is achieved. In addition, the explicit relationship between synchronization error and bit rate is presented. Finally, the effectiveness of the proposed method is validated through three numerical examples.

Dynamical models reveal anatomically reliable attractor landscapes embedded in resting-state brain networks

Abstract Analyses of functional connectivity (FC) in resting-state brain networks (RSNs) have generated many insights into cognition. However, the mechanistic underpinnings of FC and RSNs are still not well-understood. It remains debated whether resting-state activity is best characterized as reflecting noise-driven fluctuations around a single stable state, or instead, as a nonlinear dynamical system with nontrivial attractors embedded in the RSNs. Here, we provide evidence for the latter, by constructing whole-brain dynamical systems models from individual resting-state fMRI (rfMRI) recordings, using the Mesoscale Individualized NeuroDynamic (MINDy) framework. The MINDy models consist of hundreds of neural masses representing brain parcels, connected by fully trainable, individualized weights. We found that our models manifested a diverse taxonomy of nontrivial attractor landscapes including multiple equilibria and limit cycles. However, when projected into anatomical space, these attractors mapped onto a limited set of canonical RSNs, including the default mode network (DMN) and frontoparietal control network (FPN), which were reliable at the individual level. Further, by creating convex combinations of models, bifurcations were induced that recapitulated the full spectrum of dynamics found via fitting. These findings suggest that the resting brain traverses a diverse set of dynamics, which generate several distinct but anatomically overlapping attractor landscapes. Treating rfMRI as a unimodal stationary process (i.e., conventional FC) may miss critical attractor properties and structure within the resting brain. Instead, these may be better captured through neural dynamical modeling and analytic approaches. The results provide new insights into the generative mechanisms and intrinsic spatiotemporal organization of brain networks.

Topology Control of Low-Connection UAV Laser Network with Virtual Nodes

Space laser communication technology has advantages such as high bandwidth and low interference, but the limitation of the load capacity of an unmanned aerial vehicle (UAV) makes it difficult for UAV clusters to have sufficient communication links to build a stable communication network. Aiming to address the problem of the low upper limit of the node degree of the self-organized network after the introduction of space laser communication technology for UAVs, a virtual node topology control (VNTC) algorithm is proposed for constructing an efficient and stable communication network structure. The algorithm is based on the unit disc graph model and achieves a lower-node-degree upper bound while maintaining connectivity through node pairing and virtual graph construction. The theoretical analysis and experimental results of the algorithm show that the algorithm can effectively deal with the topology control problem in UAV laser networks. In contrast to the XTC algorithm (exceptional topology control), the VNTC algorithm maintains the connectivity and most of the planarizability of the network, and, at the same time, it performs better in reducing the node degree and number of edges, thus providing an effective solution for low-connectivity topology control.

Research on Energy-Saving Optimization Method and Intelligent Control of Refrigeration Station Equipment Based on Fuzzy Neural Network

Under the background of dual carbon, the retrofitting of the equipment operation system of a refrigeration station and the optimization combination of its control system are significant for its efficient operation and energy saving. The single-direction variable flow technology is often used in the chilled water system in refrigeration stations nowadays. However, the single-direction variable flow technology cannot achieve both thermal balance and flow balance for the chiller system, which is unfavorable for improving energy efficiency and reliability. To improve the reliability and energy efficiency of the refrigeration station equipment, the bidirectional variable flow technology of primary and secondary chilled water pumps was presented. Meanwhile, the feasibility of fuzzy neural networks in bidirectional variable flow systems and their energy-saving effect were studied. Before the energy saving retrofit, the refrigeration station used traditional PID (proportional-integral-derivative) controllers, and the chilled water system used single-direction variable flow technology; After the energy-saving retrofit, the refrigeration station adopted a fuzzy neural network control algorithm to optimize the PID controller parameters, and at the same time, the chilled water system used bidirectional variable flow technology. Through a large number of trial calculations of the established neural network model, it was found that 2 hidden layers and 25 hidden layer nodes can achieve higher accuracy. Specifically, the controller of the central refrigeration station consists of a training neural network and a predictive neural network working in parallel. The task of training neural networks is to learn the relationship between different input parameters and the whole energy consumption. Then it serves as the excitation function of the prediction network. The function of the predictive neural network is to find the control parameters that minimize energy consumption. The application results showed that before and after the retrofit annual power consumption and energy-saving effects were very Significant. After the energy-saving retrofit of the refrigeration station, the energy saving is 422,775 KWh every year, the energy-saving rate is 11.67%, and the annual saving cost is about 0.3382 million yuan. The results demonstrated that bidirectional variable flow technology and its control methods were feasible, reasonable, and worthy of promotion.

Optimal Risk Control of Complex Supply Chain Networks under Public Emergencies

Public emergency is an event that occurs suddenly, has a wide range of influence, and brings great impact to the society. It has the characteristics of suddenness, publicity, urgency and uncertainty. Frequent public emergencies pose a great threat to supply chain security, causing serious economic losses and social impacts. In order to improve the ability of supply chain network to cope with public emergencies, it is of great significance to study the law of supply chain risk transmission and risk control measures. Through the review of the relevant theories of supply chain sudden risk and infectious disease model, the existing problems and research prospects are pointed out. Then, based on the SEIR model, a complex supply chain network risk control model under government intervention is constructed to explore the transmission mechanism of supply chain risk and the optimal control rate of the government. Finally, the simulation verifies that by controlling the basic reproduction number, the government can effectively curb the further spread and diffusion of risks, and achieve the goal of controlling risks and balancing cost control.

Risk Control in Complex Supply Chain Networks Under Sudden Public Emergencies

After the occurrence of sudden public emergencies, risks propagate throughout the entire supply chain network, triggering a domino effect. Therefore, determining appropriate strategies to intervene in and control risks, thereby blocking or mitigating their spread, becomes critically important. This study constructs a risk control model for complex supply chain networks under sudden public emergencies based on the SEIR system dynamics propagation model, incorporating risk immunization strategies and government participation in risk control. The proposed model adopts a target control approach to manage risk dissemination, and the effectiveness of this approach is verified through simulation experiments. The findings reveal that target control can effectively contain risk propagation, and enhancing the risk immunity of supply chain networks during sudden public emergencies significantly reduces the extent of risk diffusion while lowering the cost of government intervention.

Frequency coordination and economic dispatch of microgrids using fuzzy neural network control

Frequency coordination and economic dispatch of microgrids using fuzzy neural network control

Linear Quadratic Regulator-Based Coordinated Voltage and Power Control for Flexible Distribution Networks

Linear Quadratic Regulator-Based Coordinated Voltage and Power Control for Flexible Distribution Networks

Causal evidence for increased theta and gamma phase consistency in a parieto-frontal network during the maintenance of visual attention.

Endogenous visuo-spatial attention is under the control of a fronto-parietal network of brain regions. One key node in this network, the intra-parietal sulcus (IPS), plays a crucial role in maintaining endogenous attention, but little is known about its ongoing physiology and network dynamics during different attentional states. Here, we investigated the reactivity of the left IPS in response to brain stimulation under different states of selective attention. We recorded electroencephalography (EEG) in response to single pulses of transcranial magnetic stimulation (TMS) of the IPS, while participants (N=44) viewed bilateral random-dot motion displays. Individual MRI-guided TMS pulses targeted the left IPS, while the left primary somatosensory cortex (S1) served as an active control site. In separate blocks of trials, participants were cued to attend covertly to the motion display in one hemifield (left or right) and to report brief coherent motion targets. The perceptual load of the task was manipulated by varying the degree of motion coherence of the targets. Excitability, variability and information content of the neural responses to TMS were assessed by analysing TMS-evoked potential (TEP) amplitude and inter-trial phase clustering (ITPC), and by performing multivariate decoding of attentional state. Results revealed that a left posterior region displayed reduced variability in the phase of theta and gamma oscillations following TMS of the IPS, but not of S1, when attention was directed contralaterally, rather than ipsilaterally to the stimulation site. A right frontal cluster also displayed reduced theta variability and increased amplitude of TEPs when attention was directed contralaterally rather than ipsilaterally, after TMS of the IPS but not S1. Reliable decoding of attentional state was achieved after TMS pulses of both S1 and IPS. Taken together, our findings suggest that endogenous control of visuo-spatial attention leads to changes in the intrinsic oscillatory properties of the IPS and its associated fronto-parietal network.

ZT-SDN: An ML-Powered Zero-Trust Architecture for Software-Defined Networks

Zero Trust (ZT) is a security paradigm aiming to curtail an attacker’s lateral movements within a network by implementing least-privilege and per-request access control policies. However, its widespread adoption is hindered by the difficulty of generating proper rules due to the lack of detailed knowledge of communication requirements and the characteristic behaviors of communicating entities under benign conditions. Consequently, manual rule generation becomes cumbersome and error-prone. To address these problems, we propose ZT-SDN , an automated framework for learning and enforcing network access control in Software-Defined Networks. ZT-SDN collects data from the underlying network and models the network “transactions’’ performed by communicating entities as graphs. The nodes represent entities, while the directed edges represent transactions identified by different protocol stacks observed. It uses novel unsupervised learning approaches to extract transaction patterns directly from the network data, such as the allowed protocol stacks and port numbers and data transmission behavior. Finally, ZT-SDN uses an innovative approach to generate correct access control rules and infer strong associations between them, allowing proactive rule deployment in forwarding devices. We show the framework’s efficacy in detecting abnormal network accesses and abuses of permitted flows in changing network conditions with real network datasets. Additionally, we showcase ZT-SDN’s scalability and the network’s performance when applied in an SDN environment.