基于电力企业文化感知结构时滞系统控制研究

针对电力企业文化结构感知时滞系统未知与稳定性问题,提出一种基于改进企业文化感知结构数学模型的电力企业文化感知结构网络控制,优化了传统的电力企业文化结构感知系统,同时有效改进电力企业文化感知结构网络的最优控制。仿真结果证明,该模型对电力企业文化结构感知时滞系统具有较好的实用效果。     

一类时滞不确定非线性系统的自适应鲁棒H∞控制

本文针对一类同时具有未知上界时滞不确定性和外部扰动的非线性系统，在非线性H∞控制的理论框架内讨论了系统的自适应鲁棒H∞控制问题，通过自适应律估计时滞不确定性的上界，并且基于Lyapunov-Krasovskii函数和Hamilton-Jacobi不等式获得使鲁棒控制问题可解的自适应鲁棒控制器。     

虚假消息注入攻击下的多车队列纵向稳定性控制研究

针对虚假消息注入攻击下多车队列系统纵向稳定性控制问题,采用加性不确定方法量化攻击对队列系统的影响,采用等效时滞、网络诱导时滞等方法,将通信时滞与数据丢包、车载传感器数据离散化特性等因素整合到队列中,构建了网络攻击与信息延迟下的队列模型。基于李雅普诺夫-克拉索夫斯基稳定性理论和H_∞鲁棒控制,提出了一种弱保守性分布式鲁棒抗干扰控制方法,并给出了队列系统保持稳定性的条件。仿真结果表明,所提出的鲁棒H_∞控制方法在保证队列内稳定需求的同时,能兼顾队列稳定性。与常规队列干扰抑制控制方法相比,对于队列内相邻车辆之间的间距误差控制以及跟随车辆与领航车之间的速度跟踪控制效果更好。     

基于改进鲁棒DEA模型的电力系统评价研究

针对传统DEA模型在不确定环境下评价结果不稳定的缺陷,提出了一种改进的鲁棒DEA评价方法,为电网建设和投资规划提供有效的经济指导。该方法采用鲁棒模糊核主成分分析,确定电力系统评价的投入产出指标;考虑投入产出不确定性,利用不确定信息一般化思想和对偶理论构建鲁棒DEA模型。为验证模型效果,采用我国部分省级电力系统的投入产出数据,研究不确定环境下电力系统评价的稳定性,实验表明,鲁棒DEA模型会在效率最优性和求解可靠性之间权衡,当投入产出数据同时面临不确定扰动时,模型仍能保证评价的准确稳定性。     

一类非线性不确定时滞系统鲁棒容错控制

研究一类不确定非线性时滞系统的鲁棒容错控制问题.针对不确定非线性时滞系统,基于执行器连续型增益故障模式,利用Lyapunov-Krasovskii泛函方法和线性矩阵不等式方法,推导了当一类非线性不确定系统满足一定范数有界条件时,闭环系统时滞无关鲁棒容错控制器存在的充分条件,并给出了状态反馈鲁棒容错控制器的设计方法.将所设计的状态反馈控制方法应用于某一非线性不确定时滞系统,仿真结果表明设计的控制器不仅使得该故障系统对于执行器故障具有完整性,并且能达到给定的H∞性能指标,从而验证了所提出方法的可行性和有效性.     

基于业务感知的认知网络QoS自适应控制技术

文章研究并提出了基于业务感知的认知网络服务质量(QoS)自适应控制架构。该架构在智能业务感知和分类模型的基础上对数据包进行分类和识别,并借鉴控制理论通过基于端路协同的认知网络业务流QoS自适应控制机制实现对网络流量的控制。在认知网络环境下,该架构可以构建QoS的自动感知、分析、关联、反馈、决策、配置和实施机制,进行资源的优化调整分配,适应网络环境的变化,优化网络端到端的性能,保证用户的服务质量。     

基于速率的网络拥塞控制H∞反馈控制器设计

为了解决现代网络中常见的网络拥塞控制问题,采用频域设计的方法,把不确定时滞转化为系统已知未建模动态幅值界限的乘性不确定性;根据系统鲁棒稳定性及性能指标的要求,把高速网络基于速率的鲁棒H∞拥塞控制反馈控制器的设计问题转化为工程应用中常见的混合灵敏度优化问题,然后采用解析法求取满足要求的H∞控制器.结果证明采用此方法设计的拥塞控制H∞反馈控制器较为简单,且能有效达到防止拥塞及使网络利用效率最大化的目的.     

基于自组织网络的智能电网配电方案设计

为了在不需要电网集中控制的前提下,实现自主式点对点电力传输,提出一种基于自组织网络的智能电网配电技术。利用网络信号强度的光学非线性,设计一个光电混合双稳态电路,同时,在每个网络节点处使用一个简单的局部自适应传输权重,以支持网络的自组织功能。实验结果表明,所提出的智能电网模型可以在电力分配系统中进行用电均衡调整。另外,自组织网络节点与太阳能光伏设备和电能存储设备等个体供电设施相关联,实现了电力潮流的自主式控制。     

输入饱和约束下一类多智能体编队系统鲁棒一致性分析

针对输入饱和约束奈件下具有不对称时滞的二阶多智能体编队系统的鲁棒一致性问题,本文综合利用Lya-punov-Krasovskii泛函方法和线性矩阵不等式的方法对其进行了研究.首先在n维欧氏空间中建立了二阶多智能体所组成的编队系统的数学模型;然后设计了分布式的基于一致性的具有饱和约束和不对称时滞的编队控制律;进一步,利用非线性扇区法处理了饱和项,将其转化为一种简单的非线性项,从而建立了Lyapunov-Krasovskii泛函,并利用LMI方法对编队系统进行了鲁棒一致性分析,得到了系统达到鲁棒一致时的线性矩阵不等式条件,并通过仿真分析验证了所得条件的正确性.     

基于物联网公共云平台的远程家庭感知与控制系统

针对智能家居系统普遍存在标准不统一、购买及使用成本高、对外不可扩展等问题,提出了一种基于物联网公共云平台的智能家居系统解决方案。设计和实现了一种基于公共云平台的智能家居系统整体架构,提出了家居环境舒适度、安全性、能耗三种智能终端,通过语音指令方便切换,实现了家居环境舒适度、安全性、能耗三个方面的感知与控制。然后,完成了智能终端设备与云平台服务器间的通信流程,实现了终端设备向云平台服务器上传数据以及云平台下发控制指令到终端设备的功能。最后,搭建了房屋模型,设计了智能家居系统可视化的展示平台,实现了设备通过Internet或移动互联网在任何时间、任何地点远程查看和控制家中智能设备。     

异构网络中用户关联和功率控制的协同优化

针对正交频分多址(Orthogonal Frequency Division Multiplexing Access,OFDMA)异构网络中用户关联和功率控制协同优化不佳的问题,提出了一种多智能体深度Q学习网络(Deep Q-learning Network,DQN)方法.首先,基于用户关联和功率控制最优化问题,构建了正交频分多址的双层异构网络系统模型,以实现智能决策;其次,根据应用场景和多智能体DQN框架的动作空间,对状态空间和奖励函数进行重构;最后,通过选取具有宏基站(Base Station,BS)和小型BS的两层异构网络,对多智能体DQN算法的性能进行仿真实验.仿真结果表明,相较于传统学习算法,多智能体DQN算法具有更好的收敛性,且能够有效提升用户设备(User Equipment,UE)的服务质量与能效,并可获得最大的长期总体网络实用性.     

考虑恶意节点的CRN合作频谱感知方法

基于带有恶意节点的更为实际的频谱感知环境,研究了基于合作感知的频谱共享网络模型,次级用户将会根据合作感知结果动态地调整其发射功率。为了防止恶意节点对感知系统的感知性能造成严重影响,研究了如何进行合作感知以提高感知性能。在一定的检测概率和相关功率约束下,建立了一个以最大化次级网络的吞吐量为目标函数的优化问题。仿真实验首先突出说明了恶意节点数目对频谱感知影响重大,同时还表明无论是否存在恶意节点,提出的算法均可有效地计算出最优的感知时间和发射功率,且在降低最大干扰功率限制和最大发射功率限制时,网络的吞吐量是增大的。     

基于传输中断概率的认知无线电系统性能分析

如何设计最优的频谱感知与传输框架是认知无线电技术的重要环节。考虑频谱接入过程中数据传输中断对认知网络性能影响的问题,提出了一种新的基于传输中断概率的频谱感知与传输模型,联合优化频谱感知和数据传输两个阶段,将问题建模为对主用户的干扰量约束条件下的非凸优问题,以最大化认知网络吞吐量为目标联合优化感知时间、传输速率,并通过数值计算方法对其进行求解与仿真。数值分析表明,引入传输中断概率后,在满足上述约束条件的同时,在保护主用户和认知网络吞吐量、传输时延之间有了更好的权衡。     

基于通信云和承载网协同的算力网络编排技术

面向未来网络中计算与网络紧密结合、"算网一体"的技术发展趋势,提出了基于集中式和分布式两种控制方案的算力网络编排模型,并分别介绍了实现过程的关键技术。从方案与技术分析来看,基于电信运营商通信云和承载网协同的算力网络编排方案可以较好地适应未来移动边缘计算(MEC)站点成网后边边协同与云边协同的业务需求,增强了网络对业务的感知与调度能力,而集中式或分布式控制方案的具体选择与运营商通信云能力和承载网的演进阶段密切相关。     

An analytical framework for remote sensing satellite networks based on the model predictive control with convex optimization

In this paper, we consider the problem of maximizing the throughput of remote sensing satellite networks. In such networks, the link capacities and routing matrices are varying over time. We propose a convex optimization‐based analytical framework for the problem. To maximize the network throughput under the premise of satisfying the delay constraint, we formulate the data transmission schedule into an optimization problem aiming at maximizing the delay‐constrained throughput. Considering the fact that the future link capacities cannot be accurately known in the actual situation, we propose a heuristic and distributed framework on the basis of model predictive control for approximately solving the problem. This framework can be used to design remote sensing data transmission schedules under various scenarios. We adopt a generic example to simulate and analyze the framework. The simulation results show that the proposed analytic framework can obtain the approximate solution that is very close to the optimal solution by solving the convex optimization problem step‐by‐step. The heuristic algorithm based on model predictive control can obtain the approximate solution, which is very close to the optimal solution in distributed scenario.     

Worst-case performance optimization for robust power control in downlink beamforming

In this paper,¹ we examine the problem of robust power control in a downlink beamforming environment under uncertain channel state information (CSI). We suggest that the method of power control using the lower bounds of signal-to-interference-and-noise ratio (SINR) is too pessimistic and will require significantly higher power in transmission than is necessary in practice. Here, a new robust downlink power control solution based on worst-case performance optimization is developed. Our approach employs the explicit modeling of uncertainties in the downlink channel correlation (DCC) matrices and optimizes the amount of transmission power while guaranteeing the worst-case performance to satisfy the quality of service (QoS) constraints for all users. This optimization problem is non-convex and intractable. In order to arrive at an optimal solution to the problem, we propose an iterative algorithm to find the optimum power allocation and worst-case uncertainty matrices. The iterative algorithm is based on the efficient solving of the worst-case uncertainty matrices once the transmission power is given. This can be done by finding the solutions for two cases: (a) when the uncertainty on the DCC matrices is small, for which a closed-form optimum solution can be obtained and (b) when the uncertainty is substantial, for which the intractable problem is transformed into a convex optimization problem readily solvable by an interior point method. Simulation results show that the proposed robust downlink power control using the approach of worst-case performance optimization converges in a few iterations and reduces the transmission power effectively under imperfect knowledge of the channel condition.     

Spectrum Sensing Based on Deep Learning Classification for Cognitive Radios

Spectrum sensing is a key technology for cognitive radios.We present spectrum sensing as a classification problem and propose a sensing method based on deep learning classification.We normalize the received signal power to overcome the effects of noise power uncertainty.We train the model with as many types of signals as possible as well as noise data to enable the trained network model to adapt to untrained new signals.We also use transfer learning strategies to improve the performance for real-world signals.Extensive experiments are conducted to evaluate the performance of this method.The simulation results show that the proposed method performs better than two traditional spectrum sensing methods,i.e.,maximum-minimum eigenvalue ratio-based method and frequency domain entropy-based method.In addition,the experimental results of the new untrained signal types show that our method can adapt to the detection of these new signals.Furthermore,the real-world signal detection experiment results show that the detection performance can be further improved by transfer learning.Finally,experiments under colored noise show that our proposed method has superior detection performance under colored noise,while the traditional methods have a significant performance degradation,which further validate the superiority of our method.     

Throughput and resource optimization for adaptive coding‐based random access networks with correlated sources

Modern wireless communication networks frequently have lower application throughput due to higher number of collisions and subsequent retransmission of data packets. Moreover, these networks are characterized by restricted computational capacity due to limited node‐battery power. These challenges can be assessed for deploying fast, reliable network design with resource‐restrained operation by means of concurrent optimization of multiple performance parameters across different layers of the conventional protocol stack. This optimization can be efficiently accomplished via cross‐layer design with the aid of network coding technique and optimal allocation of limited resources to wireless links. In this paper, we evaluate and analyze intersession coding across several source–destination pairs in random access ad hoc networks with inherent power scarcity and variable capacity links. The proposed work addresses the problem of joint optimal coding, rate control, power control, contention, and flow control schemes for multi‐hop heterogeneous networks with correlated sources. For this, we employ cross‐layer design for multiple unicast sessions in the system with network coding and bandwidth constraints. This model is elucidated for global optimal solution using CVX software through disciplined convex programming technique to find the improved throughput and power allocation. Simulation results show that the proposed model effectively incorporates throughput and link power management while satisfying flow conservation, bit error rate, data compression, power outage, and capacity constraints of the challenged wireless networks. Finally, we compare our model with three previous algorithms to demonstrate its efficacy and superiority in terms of various performance metrics such as transmission success probability, throughput, power efficiency, and delay.     

基于功率控制和信道分配的网络优化算法

WLAN已经在各种场所进行了大规模的布设,但是用户网络质量感知较差,甚至出现掉线、不能接入网络等问题。针对这种情况,设计了一种基于功率控制和信道分配的网络优化算法,该方案采用功率控制及信道分配的方法对特定范围内的无线局域网进行优化,最大化无线局域网的有效带宽,同时保证网络负载的均衡。仿真实验结果表明,提出的功率控制及信道分配的网络优化方案提高了网络的性能。