并联有源滤波器网侧谐波电流反馈控制

有源滤波器采用电源端电流检测的谐波抑制效果优于负载端电流检测法,但因其稳定性较差而应用较少。电源端检测方法因电源电流的反馈而构成闭环控制系统,它把产生谐振的传递函数包括在闭环内,选择适当的校正环节可以抑制谐振,通过相位超前校正可以提升系统稳定裕度和增大环路增益,但加入相位超前校正环节在提高系统的相位稳定裕度的同时系统低频段增益较低,限制了实际补偿效果。为提高低频段增益,本文提出应用选择性滤波器复合比例环节提高特定频率增益,该环节同时提高了系统相位稳定裕度和低频段的增益,保证系统稳定性并提升了补偿效果。实验结果表明了其有效性。

论“无缝之网”的“编织”机理——一种对技术与社会关系的解读

在规律所提供的"可能性空间"中,社会因素为技术形成"编织"了一张"无缝之网"。由社会客体性因素为技术形成划定"边界群"的约束机理、社会主体性因素为技术形成拓展"空间集"的扩展机理和社会控制性因素为技术形成设置"反馈网"的调节机理以及它们之间的整体联动,构成了"无缝之网""编织"的一般机理。认识这一机理对于我们具有重要的启示意义。

低开关频率下并网变换器的控制及稳定性分析

分析LCL型并网变换器网侧电流反馈控制和变换器侧电流反馈控制阻尼系统的稳定性。从主导极点的角度分析低开关频率下2个共轭复根的位置决定了根轨迹的走向,进而影响系统的稳定。已有的有源阻尼方法都是各自独立提出的,缺乏系统的研究以便于提出新的有源阻尼控制方法,通过对并网变换器稳定控制的分析可得到阻尼控制方法的本质。数字延时本质上是一种串联校正,揭示了延时改变系统相位特性。数字延时的相位滞后在某些情况下有利于系统稳定,但这取决于测量反馈量的传感器位置和谐振频率。该文在离散域下对电流控制器参数进行设计,从工程实用的角度给出快速确定控制器参数的方法。最后通过仿真和试验验证理论分析的正确性。

基于LCL滤波器的双馈风电网侧变换器不同控制结构性能研究

为分析基于LCL滤波器的双馈风电网侧变换器在不同电流反馈控制结构情况下的工作性能,采取PI控制器对网侧变换器网侧电流反馈控制结构和变换器侧电流反馈控制结构的电流闭环根轨迹进行分析,对其在理想电网无阻尼电阻和有阻尼电阻、非理想电网无阻尼电阻3种情况下的特性进行了比较。分析及仿真结果表明变换器侧电流反馈控制结构控制算法相对较复杂,但是系统稳定性好,电网电流的谐波畸变率较低;而电网侧电流反馈控制结构较易实现网侧单位功率因数控制,但稳定性较差。

Exponential stability and existence of periodic solutions for a class of recurrent neural networks with delays

Both the global exponential stability and the existence of periodic solutions for a class of recurrent neural networks with continuously distributed delays （RNNs） are studied. By employing the inequality α∏k=1^m bk^qk≤1/r ∑qkbk^r＋1/rα^r（α≥0,bk≥0,qk〉0,with ∑k=1^m qk=r-1,r≥1, constructing suitable Lyapunov r k=l k=l functions and applying the homeomorphism theory, a family of simple and new sufficient conditions are given ensuring the global exponential stability and the existence of periodic solutions of RNNs. The results extend and improve the results of earlier publications.

基于LCL滤波器的电压型PWM整流器新型电流控制策略研究

在对基于LCL滤波器的三相电压型PWM整流器研究基础上提出了一种新型的电流控制策略。该策略采取了电网侧电流反馈控制,能够方便对功率因数进行控制。为了保证系统的稳定性,在电流环内引入了电网电流二次微分环节,根据二相静止坐标系下实轴和虚轴正弦函数的特性,在算法中避免了微分环节可能造成的高频噪声问题。对所提出的控制策略进行了分析和仿真,在此基础上搭建了以TMS320LF2407为控制核心的基于LCL滤波器的三相电压型PWM整流器系统。仿真和实验结果验证了该算法的可行性。

Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints

Abstract--In this paper, an adaptive neural network （NN） control approach is proposed for nonlinear pure-feedback sys- tems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions （BLFs） with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closed- loop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach. Index TermsmAdaptive control, neural networks （NNs）, non- linear pure-feedback systems, time-varying constraints.

Control of Halo-Chaos in Beam Transport Network via Neural Network Adaptation with Time-Delayed Feedback

Subject of the halo-chaos control in beam transport networks （channels） has become a key concerned issue for many important applications of high-current proton beam since 1990＇. In this paper, the magnetic field adaptive control based on the neural network with time-delayed feedback is proposed for suppressing beam halo-chaos in the beam transport network with periodic focusing channels. The envelope radius of high-current proton beam is controlled to reach the matched beam radius by suitably selecting the control structure and parameter of the neural network, adjusting the delayed-time and control coefficient of the neural network.

Design and Implementation of an Adaptive Feedback Queue Algorithm over Open Flow Networks

The concurrent presence of different types of traffic in multimedia applications might aggravate a burden on the underlying data network, which is bound to affect the transmission quality of the specified traffic. Recently, several proposals for fulfilling the quality of service(QoS) guarantees have been presented. However, they can only support coarse-grained QoS with no guarantee of throughput, jitter, delay or loss rate for different applications. To address these more challenging problems, an adaptive scheduling algorithm for Parallel data Processing with Multiple Feedback(PPMF) queues based on software defined networks(SDN) is proposed in this paper, which can guarantee the quality of service of high priority traffic in multimedia applications. PPMF combines the queue bandwidth feedback mechanism to realise the automatic adjustment of the queue bandwidth according to the priority of the packet and network conditions, which can effectively solve the problem of network congestion that has been experienced by some queues for a long time. Experimental results show PPMF significantly outperforms other existing scheduling approaches in achieving 35--80% improvement on average time delay by adjusting the bandwidth adaptively, thus ensuring the transmission quality of the specified traffic and avoiding effectively network congestion.

Impacts of feedback delay and estimation error on secrecy performance of MISO single eavesdropper cognitive radio networks

Due to the broadcast nature of wireless transmission medium,security threats may hinder propagation of cognitive radio systems for commercial and military data application. This paper sets a channel error analytical framework and studies the joint impact of estimation errors and feedback delay on secrecy performance in cognitive radio networks. Under the assumption that system applies beamforming and jamming scheme,a multi-antenna cognitive base station( CBS) sends confidential signals to a secondary user( SU) in the presence of M primary users( PUs) and an eavesdropper. Assuming only imperfect channel state information( CSI) about the receivers is available,secrecy rate,outage probability,secrecy throughput are deduced to obtain a closed-form expression. It is shown that while the transmit power increases,secrecy throughput would reach to a constant. Simulation results show that feedback delay adversely impacts on secrecy rate,connection outage probability and secrecy throughput,while estimation error causes more impact on secrecy outage probability. Furthermore,the secrecy rate could increase progressively with the transmit power only if there exists no feedback delay.

Robust adaptive control for a class of uncertain non-affine nonlinear systems using neural state feedback compensation

A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.

A dynamic knowledge base based search engine

Search engines have greatly helped us to find the desired information from the Internet. Most search engines use keywords matching technique. This paper discusses a Dynamic Knowledge Base based Search Engine （DKBSE）, which can expand the user＇s query using the keywords＇ concept or meaning. To do this, the DKBSE needs to construct and maintain the knowledge base dynamically via the system＇s searching results and the user＇s feedback information. The DKBSE expands the user＇s initial query using the knowledge base, and returns the searched information after the expanded query.

A Trust Management Scheme Based on Behavior Feedback for Opportunistic Networks

In the harsh environment where n ode density is sparse, the slow-moving nodes cannot effectively utilize the encountering opportunities to realize the self-organized identity authentications, and do not have the chance to join the network routing. However, considering m ost of the communications in opportunistic networks are caused by forwarding operations, there is no need to establish the complete mutual authentications for each conversation. Accordingly, a novel trust management scheme is presented based on the information of behavior feedback, in order to complement the insufficiency of identity authentications. By utilizing the certificate chains based on social attributes, the mobile nodes build the local certificate graphs gradually to realize the web of "Identity Trust" relationship. Meanwhile, the successors generate Verified Feedback Packets for each positive behavior, and consequently the "Behavior Trust" relationship is formed for slow-moving nodes. Simulation result shows that, by implementing our trust scheme, the d elivery probability and trust reconstruction ratio can be effectively improved when there are large numbers of compromised nodes, and it means that our trust management scheme can efficiently explore and filter the trust nodes for secure forwarding in opportunistic networks.

NEW ARCHITECTURE OF IP QOS MANAGEMENT SYSTEM

Based on the analysis of current Quality of Service (QoS) management on IP network, new genera-tion of QoS architecture QoSAF is proposed. QoSAF is divided into three layers: resource control layer, net-work service layer and business layer. QoS management is accomplished by interactivity of layers. In this ar-chitecture, mechanism of feedback control enhances the functions of resource management and system moni-toring. The principle of design and logical architecture of system is discussed in detail.

Joint Dynamic Clustering and User Scheduling for Downlink Cloud Radio Access Network with Limited Feedback

In limited feedback-based CloudRAN(C-RAN) systems,the inter-cluster and intra-cluster interference together with the quantification error can seriously deteriorates the system spectral efficiency.We,in this paper,propose an efficient three-phase framework and corresponding algorithms for dealing with this problem.Firstly,a greedy scheduling algorithm based on the lower bound of the ergodic rate is performed for generating an elementary cluster in the first phase.And then the elementary cluster is divided into many small clusters according to the following proposed algorithms based on the short term instantaneous information in the second phase.In the end,based on the limited feedback two zero-forcing(ZF) precoding strategies are adopted for reducing the intra-cluster interference in the third phase.The provided Monte Carlo simulations show the effectiveness of our proposed algorithms in the respect of system spectral efficiency and average user rate.

Hybrid control based on inverse Prandtl-Ishlinskii model for magnetic shape memory alloy actuator

The hysteresis characteristic is the major deficiency in the positioning control of magnetic shape memory alloy actuator. A Prandtl-Ishlinskii model was developed to characterize the hysteresis of magnetic shape memory alloy actuator. Based on the proposed Prandtl-Ishlinskii model, the inverse Prandtl-Ishlinskii model was established as a feedforward controller to compensate the hysteresis of the magnetic shape memory alloy actuator. For further improving of the positioning precision of the magnetic shape memory alloy actuator, a hybrid control method with hysteresis nonlinear model in feedforward loop was proposed. The control method is separated into two parts: a feedforward loop with inverse Prandtl-Ishlinskii model and a feedback loop with neural network controller. To validate the validity of the proposed control method, a series of simulations and experiments were researched. The simulation and experimental results demonstrate that the maximum error rate of open loop controller based on inverse PI model is 1.72%, the maximum error rate of the hybrid controller based on inverse PI model is 1.37%.

State feedback control for Lurie networked control systems

The problem of the stability analysis and controller design which the network-induced delays and data dropout problems network-induced delays are assumed to be time-varying and bounded, for Lurie networked control systems （NCSs） is investigated, in are simultaneously considered. By considering that the and analyzing the relationship between the delay and its upper bound, employing a Lyapunov-Krasovskii function and an integral inequality approach, an improved stability criterion for NCSs is proposed. Furthermore, the resulting condition is extended to design a less conservative state feedback controller by employing an improved cone complementary linearization （ICCL） algorithm. Numerical examples are provided to show the effectiveness of the method.

Research on the O2O Mode and Corresponding Influences on the Online and Offline Internet Marketing

In this paper, we conduct research on the O2O mode and corresponding influence on the online and offline Internet marketing. Precision marketing is on the basis of fully understanding of consumer information, according to its characteristics and primary preferences targeted to carry out the one-to-one marketing. Mobile Internet users are mostly fixed which can be the new network technology in-depth insight into consumer interest and demand, and to establish for each specific customer database. Based on the analysis of customer data, then according to the characteristics of the different customers and the preference information such as the precision marketing, at the same time which can also according to customer＇ s feedback information targeted adjustment of products and the marketing, in order to better meet the needs of customers. Our research combines the O2O concepts and the technique to propose the novel marketing paradigm which is of great importance.

Effective Context Inconsistency Elimination Algorithm Based on Feedback and Reliability Distribution for loV

In recent years,context aware technology has been widely used in many fields,such as internet of vehicles(IoV).Consistent context information plays a vital role in adapting a system to rapidly changing situations.However,sensor's precision variance,equipment heterogeneity,network delay and the difference of statistical algorithms can lead to inconsistency context and inappropriate services.In this paper,we present an effective algorithm of context inconsistent elimination which is based on feedback and adjusted basic reliability distribution.Through feedback,each sensor's perception precision can be obtained,and with the adjusted basic reliability distribution scheme,we can make full use of all context information by adjusting the influence of every context on whole judgment based on sensor's perception precision and threshold of sensor's perception precision,and then eliminate context inconsistency.In order to evaluate the performance of the proposed context inconsistency elimination algorithm,context aware rate is defined.The simulation results show that the proposed context inconsistency elimination algorithm can obtain the best context aware rate in most cases for the varied error rates of sensors.

无电解电容永磁同步电机驱动系统谐振抑制

提出了一种基于网侧输入电流比例反馈的无电解电容驱动系统谐振抑制策略,通过对电感电容谐振产生机理的分析,建立了驱动系统小信号模型.在分析不同反馈控制变量、不同控制律下的谐振抑制效果的基础上,选择电网电流比例作为最优反馈控制方式.直接从电网电流中提取谐振分量,计算阻尼电流,采用电压注入方式实现有源阻尼控制.实验结果表明:所提出的方法能有效抑制电网电流中的电感电容谐振现象,驱动系统网侧输入功率因数高达0.989,输入电流谐波满足EN61000-3-2谐波标准.