CDMA2000 1xEV-DO系统前向链路调度算法

CDMA2000 1xEV-DO作为第三代移动通信技术CDMA2000的演进,主要用来提供高速数据传输服务。系统前向时分复用,并采用“机会主义”调度算法实现前向数据吞吐量的提高。在介绍了1xEV-DO系统的基本原理与前向调度模型的基础上,详细讨论了近年来提出的各种前向链路调度算法,并对算法的优缺点做了比较,指出了部分算法研究过程中的不足之处。最后概述了前向调度算法下一步的研究方向。     

基于速率控制的反垃圾邮件模型设计

在对垃圾邮件及其防范技术进行全面分析与比较的基础上,针对传统过滤技术的防范和能力不足问题,提出了基于速率控制的空间向量邮件过滤系统设计。该系统改善了传统的单IP和单门限的简单设置,采用多方位多层次的空间过滤系统,再结合客户端的邮件过滤系统,形成了更为有效的垃圾邮件防范能力。     

一种单速率组播速率控制与修剪算法

组播网络的异构性问题导致了组播内部公平性问题。本文在分析了相关工作的局限性之后,提出了一种基于效用的单速率组播内部公平性标准,其优化目标是最大化组播会话的净收益。相应地设计了一种基于动态规划的速率控制与修剪算法,该算法在计算开销与通信开销两方面都是可扩展的。     

基于Stackbelberg博弈的认知无线网络速率控制模型

提出了基于Stackelberg博弈的认知无线单跳网络流量速率控制模型。应用反向归纳法对提出的流量速率Stackelberg博弈模型纳什均衡进行了分析,证明了提出的模型纳什均衡存在性及唯一性,并给出了Stackelberg博弈模型纳什均衡解的具体形式。仿真验证了提出的模型正确性,仿真结果表明在模型的纳什均衡处网络总体效用是最优的,且网络效用最大时认知结点可获得最优数据传输速率。     

松弛算法在TCP友好速率控制中的应用

由于TCP友好速率控制（TFRC）机制在实时多媒体应用中,TFRC流的发送速率波动性明显,不利于实时多媒体流的传输。采用松弛算法对TFRC流的发送速率进行自适应约束,使TFRC流发送速率在与TCP流竞争中变得更加平缓、收敛。实验结果表明,松弛算法能够改善TFRC的性能,提高实时流媒体的传输质量。     

一种面向对象速率控制方法

该文提出了一种面向对象的速率控制算法,对重要对象区域根据宏块运动剧烈程度分配量化步长,在控制输出速率为常数的同时,该算法能让人类视觉更敏感的运动宏块分配到更多的比特。另外该算法比以率失真模型为基础的速率控制算法具有更低的计算复杂度。从模拟实验的结果可以看出该算法能有效提高指定区域的视觉质量。     

流媒体网络传输中基于速率的控制方法

介绍了一种新的基于速率的拥塞控制方法,重点讨论了速率控制方法在流媒体网络传输中的具体应用。该方法通过监测网络状态变化和客户端缓冲区状态,动态调整服务器端的发送速率,以提高服务质量。研究表明,相对于传统的基于窗口型拥塞控制方法,速率控制方法在流媒体网络传输中可取得更好的效果。     

固定翼飞机控制律设计与飞行模拟系统的实现

为了提高机动性能,现代飞机的设计开始向放宽静不稳定乃至静不稳定的方向发展.因此飞行控制律的设计尤为重要.对于控制律的设计.经典控制理论法简单、直观.而且是相当成熟、可靠的控制方法并且广泛应用于实际型号中.提出了某型固定翼飞机飞行控制系统设计方法,并以纵向通道的俯仰角保持控制律设计为例介绍了经典控制理论中根轨迹方法的设计思想及步骤.给出实现某型固定翼飞机飞行模拟仿真系统的实现方案,为验证所设计控制律的正确性.在仿真系统中使用了虚拟视景技术,直观且实时地显示飞行姿态.     

一种动态优化的无线信道速率控制方法

速率控制是无线视频传输过程中重要的控制方法,在无线网络中采用了基于TCP友好速率控制TFRC(TCP-Friendly Rate Control)的速率控制方法,提出了基于动态优化的无线视频传输多用户TFRC的速率控制方法。该方法利用了自适应编码调制特点,根据用户的信道状况,动态优化物理层发送速率;经过理论分析和仿真证明可以在总的链路速率不增加的情况下,动态地根据各个用户的信道状况变化情况,公平地分配和调整各个用户带宽,从而最大效率地使用现有链路及提高整体的用户满意度。     

定量限制血压下降速率的多模控制方法

定量限制血压下降速率的多模控制方法１）郑连清（空军电讯工程学院自动化室西安７１００７７）刘建平（西京医院心外科西安７１００４０）张忠惠（陕西电子职工大学西安７１００４３）程敬之（西安交通大学医电系西安７１００４９）关键词多模控制,速率,血压．１）国家...     

动态矩阵控制系统稳定域的根轨迹分析

本文使用根轨迹技术从理论上分析了纯滞后工业过程参数变化对系统稳定性的影响,得到了一个与设计参数有关的稳定域,从而为设计参数自动整定提供了参考。     

A computational model of time for stiff hybrid systems applied to control synthesis

Computation has quickly become of paramount importance in the design of engineered systems, both to support their features as well as their design. Tool support for high-level modeling formalisms has endowed design specifications with executable semantics. Such specifications typically include not only discrete-time and discrete-event behavior, but also continuous-time behavior that is stiff from a numerical integration perspective. The resulting stiff hybrid dynamic systems necessitate variable-step solvers to simulate the continuous-time behavior as well as solver algorithms for the simulation of discrete-time and discrete-event behavior. The combined solvers rely on complex computer code which makes it difficult to directly solve design tasks with the executable specifications. To further leverage the executable specifications in design, this work aims to formalize the semantics of stiff hybrid dynamic systems at a declarative level by removing implementation detail and only retaining ‘what’ the computer code does and not ‘how’ it does it. A stream-based approach is adopted to formalize variable-step solver semantics and to establish a computational model of time that supports discrete-time and discrete-event behavior. The corresponding declarative formalization is amenable to computational methods and it is shown how model checking can automatically generate, or synthesize, a feedforward control strategy for a stiff hybrid dynamic system. Specifically, a stamper in a surface mount device is controlled to maintain a low acceleration of the stamped component for a prescribed minimum duration of time.     

Networked control design for linear systems

In this paper we study a systematic networked control method designed specifically to handle the constraints of the networked realization of a linear time invariant control system. The general structure of the proposed controller requires switching between the open loop and closed loop subsystems of the controller which is dictated by the behaviour of the communication network.     

Distributed model predictive control for constrained linear systems

We address the distributed model predictive control (MPC) for a set of linear local systems with decoupled dynamics and a coupled global cost function. By the decomposition of the global cost function, the distributed control problem is converted to the MPC for each local system associated with a cost involving neighboring system states and inputs. For each local controller, the infinite horizon control moves are parameterized as N free control moves followed by a single state feedback law. An interacting compatibility condition is derived, disassembled and incorporated into the design of each local control so as to achieve the stability of the global closed‐loop system. Each local system exchanges with its neighbors the current states and the previous optimal control strategies. The global closed‐loop system is shown to be exponentially stable provided that all the local optimizers are feasible at the initial time. Copyright © 2009 John Wiley & Sons, Ltd.     

Adaptive control Lyapunov function based model predictive control for continuous nonlinear systems

A design of adaptive model predictive control (MPC) based on adaptive control Lyapunov function (aCLF) is proposed in this article for nonlinear continuous systems with part of its dynamics being unknown at the starting time. Specifically, to guarantee the convergence of the closed-loop system with online predictive model updating, a stability constraint is designed. It limits the aCLF of the system under the MPC to be less than that under an online updated auxiliary adaptive control. The auxiliary adaptive control which implements in a sampling-hold fashion can guarantee the convergence of the controlled system. The sufficient conditions that guarantee the states to be steered to a small region near the equilibrium by the proposed MPC are provided. The calculation of the proposed algorithm does not depend on the model mismatch at the starting time. And it does not require the Lyapunov function of the state of the real system always to be reduced at each time. These provide the potential to improve the performance of the closed-loop system. The effectiveness of the proposed method is illustrated through a chemical process example.     

一种模糊控制的组播速率调节机制

目前多数组播拥塞控制机制采用模拟TCP窗口机制传输流媒体业务,尽管保证了TCP友好性,但是速率不够平滑,不能很好地满足流媒体组播业务服务质量的要求。针对这一问题,提出了一种模糊控制的组播速率调节算法（FC-MRAA）。该算法基于模糊控制理论设计了两个模糊控制器,一个根据接收端的反馈信息计算速率增量,保证TCP友好性;另一个根据路由器缓冲区占有率计算控制增益,平滑发送速率。仿真结果表明,该算法具有良好的速率平滑性和TCP友好性。     

Designing of non-fragile robust model predictive control for constrained uncertain systems and its application in process control

A non-fragile robust model predictive control (RMPC) is designed in the uncertain systems under bounded control signals. To this aim, a class of the nonlinear systems with additive uncertainty is considered in its general form. The RMPC synthesis could lead to the proper selection of the controller’s gains. Thus, the non-fragile RMPC design is translated into a minimization problem subjected to some constraints in terms of linear matrix inequality (LMI). Hence, the controller’s gains are computed by solving such a minimization problem. In some numerical examples, the suggested non-fragile RMPC is compared with the other methods. The simulation results demonstrate the effectiveness of the proposed RMPC in comparison with similar techniques.     

Guaranteed cost control for networked control systems

The guaranteed cost control problem for networked control systems (NCSs) is addressed under conmmnication constraints and varying sampling rate. First of all, a simple inFormation-scheduling scheme is presented to describe the scheduling approach of system signals in NCSs. Then, based on such a scheme and given sampling method, the design procedure in dynarmic output feedback manner is also derived which renders the closed loop system to be asymptotically stable and guarantees an upper bound of the LQ pefformance cost function.