Distributed Containment Control of Networked Nonlinear Second-order Systems With Unknown Parameters

In this paper, we study the containment control problem for nonlinear second-order systems with unknown parameters and multiple stationary/dynamic leaders. The topologies that characterize the interaction among the leaders and the followers are directed graphs. Necessary and sufficient criteria which guarantee the control objectives are established for both stationary leaders(regulation case) and dynamic leaders(dynamic tracking case) based protocols. The final states of all the followers are exclusively determined by the initial values of the leaders and the topology structures. In the regulation case, all the followers converge into the convex hull spanned by the leaders,while in the dynamic tracking case, not only the positions of the followers converge into the convex hull but also the velocities of the followers converge into the velocity convex hull of the leaders.Finally, all the theoretical results are illustrated by numerical simulations.

Reliable and Load Balance-Aware Multi-Controller Deployment in SDN

Software Defined Networking(SDN) provides flexible network management by decoupling control plane and data plane. However, such separation introduces the issues regarding the reliability of the control plane and controller load imbalance in the distributed SDN network, which will cause the low network stability and the poor controller performance. This paper proposes Reliable and Load balance-aware Multi-controller Deployment(RLMD) strategy to address the above problems. Firstly, we establish a multiple-controller network model and define the relevant parameters for RLMD. Then, we design the corresponding algorithms to implement this strategy. By weighing node efficiency and path quality, Controller Placement Selection(CPS) algorithm is introduced to explore the reliable deployments of the controllers. On this basis, we design Multiple Domain Partition(MDP) algorithm to allocate switches for controllers according to node attractability and controller load balancing rate, which could realize the reasonable domain planning. Finally, the simulations show that, compared with the typical strategies, RLMD has the better performance in improving the reliability of the control plane and balancing the distribution of the controller loads.

Load Balancing Algorithm for Migrating Switches in Software-Dened Vehicular Networks

In Software-Dened Networks(SDN),the divergence of the control interface from the data plane provides a unique platform to develop a programmable and exible network.A single controller,due to heavy load trafc triggered by different intelligent devices can not handle due to it’s restricted capability.To manage this,it is necessary to implement multiple controllers on the control plane to achieve quality network performance and robustness.The ow of data through the multiple controllers also varies,resulting in an unequal distribution of load between different controllers.One major drawback of the multiple controllers is their constant conguration of the mapping of the switch-controller,quickly allowing unequal distribution of load between controllers.To overcome this drawback,Software-Dened Vehicular Networking(SDVN)has evolved as a congurable and scalable network,that has quickly achieved attraction in wireless communications from research groups,businesses,and industries administration.In this paper,we have proposed a load balancing algorithm based on latency for multiple SDN controllers.It acknowledges the evolving characteristics of real-time latency vs.controller loads.By choosing the required latency and resolving multiple overloads simultaneously,our proposed algorithm solves the loadbalancing problems with multiple overloaded controllers in the SDN control plane.In addition to the migration,our algorithm has improved 25%latency as compared to the existing algorithms.

Power control and channel allocation optimization game algorithm with low energy consumption for wireless sensor network

In a wireless sensor network （WSN）, the energy of nodes is limited and cannot be charged. Hence, it is necessary to reduce energy consumption. Both the transmission power of nodes and the interference among nodes influence energy consumption. In this paper, we design a power control and channel allocation game model with low energy consumption （PCCAGM）. This model contains transmission power, node interference, and residual energy. Besides, the interaction between power and channel is considered. The Nash equilibrium has been proved to exist. Based on this model, a power control and channel allocation optimization algorithm with low energy consumption （PCCAA） is proposed. Theoretical analysis shows that PCCAA can converge to the Pareto Optimal. Simulation results demonstrate that this algorithm can reduce transmission power and interference effectively. Therefore, this algorithm can reduce energy consumption and prolong the network lifetime.

Load balancing strategy of heterogeneous wireless networks based on multi-hop routing algorithm of ad hoc network

Because of different system capacities of base station （BS） or access point （AP） and ununiformity of traffic distribution in different cells, quantities of new call users may be blocked in overloaded cell in communication hot spots. Whereas in some neighboring under-loaded cells, bandwidth may be superfluous because there are only few users to request services. In order to raise resource utilization of the whole heterogeneous networks, several novel load balancing strategies are proposed, which combine the call ad- mission control policy and multi-hop routing protocol of ad-hoc network for load balancing. These loadbalancing strategies firstly make a decision whether to admit a new call or not by considering some parameters like load index and route cost, etc., and then transfer the denied users into neighboring under-loaded cell with surplus channel according to optimum multi-hop routing algorithm. Simulation results show that the proposed load balancing strategies can distribute traffics to the whole heterogeneous wireless netorks, improve the load balance index efficiently, and avoid the call block phenomenon almost absolutely.

Adaptive Server Load Balancing in SDN Using PID Neural Network Controller

Web service applications are increasing tremendously in support of high-level businesses.There must be a need of better server load balancing mechanism for improving the performance of web services in business.Though many load balancing methods exist,there is still a need for sophisticated load bal-ancing mechanism for not letting the clients to get frustrated.In this work,the ser-ver with minimum response time and the server having less traffic volume were selected for the aimed server to process the forthcoming requests.The Servers are probed with adaptive control of time with two thresholds L and U to indicate the status of server load in terms of response time difference as low,medium and high load by the load balancing application.Fetching the real time responses of entire servers in the server farm is a key component of this intelligent Load balancing system.Many Load Balancing schemes are based on the graded thresholds,because the exact information about the networkflux is difficult to obtain.Using two thresholds L and U,it is possible to indicate the load on particular server as low,medium or high depending on the Maximum response time difference of the servers present in the server farm which is below L,between L and U or above U respectively.However,the existing works of load balancing in the server farm incorporatefixed time to measure real time response time,which in general are not optimal for all traffic conditions.Therefore,an algorithm based on Propor-tional Integration and Derivative neural network controller was designed with two thresholds for tuning the timing to probe the server for near optimal perfor-mance.The emulation results has shown a significant gain in the performance by tuning the threshold time.In addition to that,tuning algorithm is implemented in conjunction with Load Balancing scheme which does not tune thefixed time slots.

基于云计算的舰船通信网络动态负载均衡控制方法

针对舰船通信网络通信环境复杂、用户需求不确定、影响通信均衡性等问题、提出基于云计算的舰船通信网络动态负载均衡控制方法。利用Ryu控制器获取监控舰船通信网络状态,获取网络负载。创建云计算平台虚拟机,实现执行舰船通信网络动态负载均衡任务调度任务。选取最高迁移概率的物理机结果作为云计算平台虚拟机迁移结果,通过虚拟机的迁移与关闭操作,更新虚拟机的综合权值,实现舰船通信网络动态负载均衡控制。实验结果表明,该方法可实现舰船通信网络的动态负载均衡控制,负载均衡度高于0.7,可有效提升舰船通信网络的资源利用率。

生成式大模型承载网络架构与关键技术探索

生成式大模型训练需要超大规模低时延、高带宽、高可用的网络承载底座。对生成式大模型下高性能网络基础设施的技术发展路线和实现方案进行了研究,认为商用部署时需针对不同训练阶段的工作负载和流量模式,开展定制化网络架构设计和传输协议优化。流控/拥塞控制技术、负载均衡技术、自动化运维技术和面向广域远程直接内存访问(RDMA)的确定性网络传输技术是未来的重点研究方向。

相依指挥控制网络耦合策略研究

指挥控制网络的层次关联复杂、通信链路交错等特点,使其表现出明显的多网相依特征。针对现有相依边的研究方法难以有效表达网络的耦合关系,通过研究基于节点介数的相依强度、基于桥接系数的链路均衡性指数,提出基于相依强度与链路均衡性的相依指挥控制网络耦合策略,仿真验证表明该策略提高了网络抗级联失效能力。

基于BP神经网络的集中供热二次网回水温度预测控制研究

针对集中供热系统二次管网存在的水力失调问题,设计了二次网水力平衡调节及回水温度预测模型,并实施智能控制策略,以实现二次网回水温度的精准控制。首先,构建BP神经网络预测模型,将此模型的输出视为二次网回水温度给定值;其次,在整个系统控制中,实施BP神经网络与PID控制器相结合的策略,进行二次网回水温度的控制。以高邑县某小区换热站数据为基础,通过阶跃响应曲线法建立二次网回水温度控制系统的数学模型,并通过BP-PID控制进行仿真实验。实验结果表明,与传统PID控制器相比,BP-PID控制器具有调节时间短、超调量小的优点,能够快速达到平稳状态。

基于CNN和LSTM混合网络的直流充电桩功率模块优化

直流充电桩内部功率变化动态性过强,不易捕捉及计算误差过大,提出基于CNN和LSTM混合网络的功率模块优化方法。根据充电桩电路工作原理,得到电路内部各项参数间存在的直接关联,根据该点采用三相电网平衡原理设计电力稳定控制函数,计算满足电压、电流以及功率最稳的调节比例系数,并作为调节点,建立CNN和LSTM混合网络多层次模型,计算有序、无序充放电过程的电力负荷变化,利用调节比例系数按照充电、放电所需功率需求完成优化。实验结果表明,所提方法的充电桩功率稳定,对充电桩的电压偏移量实现有效改善。

配电网多端柔性互联协调控制策略

随着社会经济的快速发展和新型电力系统建设的加快推进,柔性互联逐渐成为配电网网架升级和灵活调控能力提升的重要技术手段。针对多端柔性互联系统的功率控制需求,提出了一种面向工程应用的功率协调控制策略,包括在部分馈线重载时合理分配功率的重载限制控制,以及在所有馈线重载时优化潮流分布的功率均衡控制。基于所提策略开发了柔性互联协调控制装置并应用于实际工程。基于电网真实数据的案例分析和工程实测数据验证了所提策略能够应对不同负荷/电源特性的配电网柔性互联场景,有效解决配电网中馈线重载和光伏倒送的问题,提高配电网的供电效率和安全性。

基于哈希接入的跨网络负载均衡

无线接入网复杂化持续加剧,无线设备数量激增.为了提升未来网络容量和资源利用率,需要设计有效的拥塞控制手段和资源调配策略,平衡子网络之间负载,促进跨域资源协同共享.为此,本文在新近提出的哈希接入协议的基础上,设计了哈希接入动态优化策略,根据网络负载状况动态调整接入参数以缓解网络拥塞,并在跨网络场景下进一步提出了负载均衡方案与资源整合再调配方案,为无线接入网整合无线资源、均衡网络负载提供了有用的方法.仿真结果表明,本文提出的哈希接入动态优化策略在保证网络稳定的同时最大化网络吞吐量,资源调配方案能有效地调配失衡的网络负载,提升复杂融合网络性能和接入公平性.

基于二阶一致性算法的电池储能系统SOC均衡及功率分配策略

为延长电池储能系统的整体寿命,需保持储能系统中各单元的荷电状态(state of charge,SOC)均衡。为此,提出一种基于二阶一致性算法的改进下垂控制策略,通过指数函数嵌套变化系数,实现不同容量储能单元快速SOC均衡。在SOC均衡的基础上设计二次控制策略,在一定通信时延下实现频率、电压恢复和有功、无功功率合理分配。最后,以4台储能单元组成的电池储能系统为算例进行仿真,验证了所提控制策略的有效性,SOC能够快速收敛达到均衡状态,频率、电压能够恢复到额定值,有功、无功功率能够按照相应下垂系数比例进行分配。

基于线性化潮流模型的配电网电压无功控制策略

针对现有基于二阶锥松弛的电压无功控制模型无法以电压波动为目标且求解速度较慢等问题,文中提出了一种基于线性化潮流模型的配电网电压无功控制策略。基于支路潮流方程建立了包含有载调压分接头、电容器组和光伏逆变器等调控手段的电压无功控制模型,并将其离散变量松弛为连续变量,以形成二阶锥规划。进一步以二阶锥规划所得结果为基准点构造线性化潮流模型,并以电压波动最小为目标,将连续变量还原为离散变量。最后基于改进的IEEE-33节点测试系统进行了仿真,仿真结果表明所提出的线性化潮流模型具有较高的精度和更快的求解速度,能够适用于配电网实时电压无功控制。

基于文氏桥振荡器的CCCⅡ带通滤波器

为了得到一个最简单、最经济的电流模式高Q带通滤波器,根据传统的文氏桥振荡器和伴随网络理论,设计了一个电流模式/跨导模式的CCCⅡ带通滤波器。通过调整CCCⅡ的偏置电流,电路的极点频率和品质因数能够被独立地电调谐,当满足IB3=2IB4,电路还能产生正弦振荡。由于电路使用4个CCCⅡ和2个接地电容,因此电路适宜集成。计算机仿真结果表明设计方法正确。

基于神经网络实现带式输送机的功率平衡控制

为解决带式输送机传统功率平衡控制系统控制精度不高、响应速度慢等问题,在对带式输送机多电机功率平衡因素综合分析的基础上,对传统功率平衡控制系统的现状进行分析,掌握其薄弱点;然后,基于模糊神经网络算法设计了功率平衡控制系统原理框图,采用Simulink软件对其启动特性和突变工况下的功率平衡控制效果进行分析,达到了预期的效果。

基于冗余矢量的T型三电平双向变换器中点电位平衡模型预测控制

T型三电平双向变换器因其具有损耗小、输出电能质量高等优势适合应用于低压交直流混合配电网互联等场景。针对T型三电平双向变换器存在直流侧中点电位不平衡等问题,模型预测控制因其易于实现多目标优化的特点具有良好的应用价值。为了解决传统有限集模型预测控制(FCS-MPC)权重因子整定困难,中点电位控制效果不佳的问题,提出一种基于冗余矢量的中点电位平衡模型预测控制,利用冗余小矢量的特性实现对中点电位平衡的控制,避免了权重因子的选择。在此基础上,利用代价函数计算矢量的作用时间并预测输出最优开关序列,提升系统的稳态性能。最后,通过实验测试验证了所提策略的有效性。

引入自抗扰控制的MMC均压环流简化控制策略

针对模块化多电平换流器(MMC)正常工作时,子模块电容电压不平衡和三相桥臂间存在环流分量的问题,通过引入自抗扰控制,提出一种新的均压环流控制策略。分析子模块电容电压波动以及环流机理,提出基于自抗扰控制的单环子模块均压控制;结合二阶广义积分器滤波环节提取交流分量,使用准PR控制对环流二倍频分量进行抑制。在稳态、动态条件下进行实验,结果表明:在稳态条件下,基于ADRC的单环控制波动系数降低0.5%,环流抑制降低19%,输出电流畸变率降低0.46%;在动态条件下,可以快速达到新的稳定状态,对电容电压脉动有良好的抑制作用。

柔性互联装备中零序电压注入的相间功率均衡控制

级联全桥加双有源桥结构的多端柔性互联装备可连接交流电网与多个直流电网,直流电网瞬时功率差异、子模块故障等问题均会导致并网电流不均衡与直流电容电压不均分。针对三相星型互联结构,采用注入零序电压的控制策略均衡相间功率,推导出不同功率需求下零序电压分量具体表达式,并量化分析注入零序电压的限制范围。最后搭建仿真实验平台,验证了该方法的有效性,仿真结果表明:在现有控制架构的基础上加入零序电压注入环节能够调配相间的功率以保证并网电流均衡,将不平衡度从6%降为1%,同时维持直流电容电压均分。