A rate-based flow control mechanism for avoiding congestion

The rate-based flow control mechanisms for the Available Bit Rate(ABR)service are used to share the available badwidth of a bottleneck switch connected to a bottleneck link fairly and reasonably among many competitive users,and to maintain the buffer queue length of the witch at a desired level in order to avoid congestion in Asynchronous Transfer Mode(ATM)networks.In this Paper,a control theoretic approach that uses a Deadbeat-Response(DR) controller to the desing of a rate-based flow control mechanism is presented.The mehanism has a simple structure and is robust in the sense that its stability is not sensitive to the change of the number of active Virtual Connections(VCs),Simulation results show that this mechanism not only ensures fair share of the bandwidth for all active VCs regardless of the nmuber of hops they traverse but also has the advantages of fast convergence ,no oscillation,and high link bandwidth utilization.     

Deterministic learning from neural control for uncertain nonlinear pure‐feedback systems by output feedback

The essence of intelligence lies in the acquisition/learning and utilization of knowledge. However, how to implement learning in dynamical environments for nonlinear systems is a challenging issue. This article investigates the deterministic learning (DL) control problem for uncertain pure‐feedback systems by output feedback, which achieves the human‐like learning and control in a simple way. To reduce the complexity of control design and analysis, first, by combining an appropriate system transformation, the original pure‐feedback system is transformed into a simple normal nonaffine system. An observer is then introduced to estimate the transformed system states. Based on the backstepping and dynamic surface control techniques, a simple adaptive neural control scheme is first developed to guarantee the finite time convergence of the tracking error using only one neural network (NN) approximator. Second, through DL, the exponential convergence of the NN weights is obtained with the satisfaction of partial persistent excitation condition. Thus, locally accurate approximation/learning of the transformed unknown system dynamics is achieved and stored as constant NNs. Finally, by utilizing the stored knowledge, an experience‐based controller is constructed and a novel learning control scheme is further proposed to improve the control performance without any further adaptation online for the estimate neural weights. Simulation results have been given to illustrate that the proposed scheme not only can learn and memorize knowledge like humans but also can utilize experience to achieve superior control performance.     

Adaptive control with guaranteed transient and steady state tracking error bounds for strict feedback systems

Two robust adaptive control schemes for single-input single-output (SISO) strict feedback nonlinear systems possessing unknown nonlinearities, capable of guaranteeing prescribed performance bounds are presented in this paper. The first assumes knowledge of only the signs of the virtual control coefficients, while in the second we relax this assumption by incorporating Nussbaum-type gains, decoupled backstepping and non-integral-type Lyapunov functions. By prescribed performance bounds we mean that the tracking error should converge to an arbitrarily predefined small residual set, with convergence rate no less than a prespecified value, exhibiting a maximum overshoot less than a sufficiently small prespecified constant. A novel output error transformation is introduced to transform the original “constrained” (in the sense of the output error restrictions) system into an equivalent “unconstrained”one. It is proven that the stabilization of the “unconstrained” system is sufficient to solve the problem. Both controllers are smooth and successfully overcome the loss of controllability issue. The fact that we are only concerned with the stabilization of the “unconstrained” system, severely reduces the complexity of selecting both the control parameters and the regressors in the neural approximators. Simulation studies clarify and verify the approach.     

Adaptive backstepping output feedback control for SISO nonlinear system using fuzzy neural networks

In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output （SISO） nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzy- neural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.     

基于神经网络MIMO非线性系统自适应输出反馈控制

针对一类具有对象不确定和外部干扰的MIMO(多输入多输出)非线性系统提出了自适应鲁棒输出跟踪控制方案.使用了高斯径向基神经网络自适应补偿对象非线性,高增益观测器被用来估计不能直接测量的输出导数.此方法所设计的控制器不仅保证闭环系统稳定,而且所有状态有界以及跟踪误差一致终值有界.仿真结果充分表明了该方案的有效性和可行性.     

无线传感器网络的数据聚合时机分析

数据聚合是无线传感器网络实现节能的一种重要技术。数据聚合的时机直接影响到数据聚合的准确度和延时,是数据聚合的关键问题之一。建立基于泊松过程的数据聚合模型,分析数据聚合时机的概率特征,提出满足一定概率要求和信息数要求的条件下,数据聚合时机的求解方法。仿真表明,理论分析和模拟实验结果基本相符。该结论为数据聚合时机的分析提供新的思路。     

一类未知非线性系统的智能迭代学习控制

从自适应的角度设计迭代学习控制，将神经网络引入迭代学习控制中。学习控制与自适应控制相结合，使得对网络权值的学习和跟踪控制同时进行，克服 了经典迭代学习控制的一些缺陷。基于Lyapunov直接方法，证明了整个控制系统的稳定并实现了任意精度的跟踪。实例仿真结果说明了算法 的有效性及其所具有的优点。     

一类非线性系统的稳定自适应控制

将一类非线性系统等价表示为时变线性系统, 在此基础上设计了自适应控制器. 利用小波网络直接辨识控制器参数. 从理论上证明了闭环系统的稳定性. 仿真结果表明了所提算法的有效性.     

基于未知控制增益的非线性系统自适应迭代反馈控制

针对一类单输入单输出不确定非线性重复跟踪系统, 提出一种基于完全未知控制增益的自适应迭代反馈控制. 与普通迭代学习控制需要学习增益稳定性前提条件不同, 所提自适应迭代反馈控制律通过不断修改Nuss baum形式的反馈增益达到收敛. 证明当迭代次数i→δ时, 重复跟踪误差可一致收敛到任意小界δ. 仿真显示了所提控制方法的有效性.     

Output feedback stabilization for a general class of nonlinear systems via sampled‐data control

This paper considers global output feedback stabilization via sampled‐data control for a general class of nonlinear systems, which admit unknown control coefficients and nonderivable output function. A sector region of the output function is given by utilizing a technical lemma, and a sampled‐data controller is designed by combining a robust state stabilizer and a reduced‐order sampled‐data observer. By carefully choosing an appropriate sampling period, the proposed controller guarantees the globally asymptotical stability of the closed‐loop systems.     

具有负载和流量控制的软件分发系统设计与实现

通过分析软件分发过程中负载控制和流量控制的关键点,在原型系统的基础上设计并实现了一个基于可激活RMI框架的应用软件分发系统,系统可以实现B/S模式下的主动软件分发。实验结果表明,系统可以在较低负载下执行流量可控的大规模应用软件分发。     

一类非线性系统的输出反馈容错控制

针对一类非线性系统,研究了故障情况下基于输出反馈的容错控制问题．首先基于Zhang的自适应观测器和Lin的输出反馈控制律设计了正常系统的标称控制律,分析了该控制律的性能,并得到了故障发生后仍然采用该控制律时闭环系统状态有界的充分条件．在此基础上,设计了故障系统的容错控制律,证明了若发生的故障满足前述的充分条件,则故障系统在该容错控制律下稳定．数值仿真表明了该方法的有效性．     

Neural network‐based adaptive dynamic surface control of uncertain nonlinear pure‐feedback systems

In this paper, by incorporating the dynamic surface control technique into a neural network‐based adaptive control design framework, we have developed a backstepping‐based control design for a class of nonlinear systems in pure‐feedback form with arbitrary uncertainty. The circular design problem which may exist in pure‐feedback systems is overcome. In addition, our development is able to eliminate the problem of ‘explosion of complexity’ inherent in the existing backstepping‐based methods. A stability analysis is given, which shows that our control law can guarantee the semi‐global uniformly ultimate boundedness of the solution of the closed‐loop system, and makes the tracking error arbitrarily small. Moreover, the proposed control design scheme can also be directly applied to the strict‐feedback nonlinear systems with arbitrary uncertainty. Copyright © 2010 John Wiley & Sons, Ltd.     

Adaptive hierarchical control for output feedback systems

In this paper, we address the problem of adaptive hierarchical control for a class of so-called uncertain output feedback systems. The proposed approach is to design an adaptive output interface dynamic by estimating the uncertainties. With the interface connected to the uncertain nonlinear system and a linear abstract system, the system could track approximately the abstraction. Finally, two examples are presented to illustrate our approach.     

Adaptive neural output feedback tracking control for a class of nonlinear systems

In this paper, an adaptive neural output feedback control scheme based on backstepping technique and dynamic surface control (DSC) approach is developed to solve the tracking control problem for a class of nonlinear systems with unmeasurable states. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states. Secondly, in the controller design process, radial basis function neural networks (RBFNNs) are utilised to approximate the unknown nonlinear functions, and then a novel adaptive neural output feedback tracking control scheme is developed via backstepping technique and DSC approach. It is shown that the proposed controller ensures that all signals of the closed-loop system remain bounded and the tracking error converges to a small neighbourhood around the origin. Finally, two numerical examples and one realistic example are given to illustrate the effectiveness of the proposed design approach.     

一类非线性系统的自适应神经网络控制

针对一类具有非仿射函数和下三角结构的、受干扰未知的非线性系统,提出一种新的自适应神经网络控制方法．它是严格反馈不确定系统和纯反馈系统的更一般化表达．在Backstepping设计思想基础上,证明了闭环信号的半全局最终一致有界性,并很好地处理了控制方向和控制奇异问题．通过仿真验证了该方法的有效性．     

时延与丢包情形下的网络控制系统量化反馈镇定

网络控制系统是通过实时网络来形成闭环反馈的控制系统。网络的介入会带来许多不确定因素,包括网络诱导时延和数据包丢失以及量化误差,它们会影响系统的稳定性,严重时甚至会导致系统失稳。本文针对短时延网络控制系统,考虑传感器-控制器和控制器-执行器两个通道都存在数据包丢失,在传感器到控制器端设立量化器,将系统建模为一个异步动态系统,利用异步动态系统指数稳定性理论、线性矩阵不等式工具分析了闭环系统稳定性,并基于MATLAB软件的线性矩阵不等式(LMI)工具箱实验仿真进行有效性验证,结果证明了方案的可行性,最后给出控制器参数的设计方法。     

含正反馈振动主动控制系统的混合自适应控制

对于存在结构正反馈的振动主动控制系统,传统的基于有限冲击响应的自适应前馈控制器设计方法难以同时保证控制系统稳定与良好的控制性能.本文在分析正反馈对前馈控制系统影响的基础上,基于无限冲击响应控制器设计模式,提出一种结合前馈自适应控制器和反馈自适应控制器的混合自适应振动主动控制方法.其中前馈自适应控制器采用参考传感器采集到的扰动相关信号作为参考信号,反馈自适应控制器通过构建扰动的估计量作为参考信号,控制器参数更新采用Landau参数递推算法.以一典型的具有固有正反馈性质的机械振动系统为控制对象,给出了该混合自适应控制算法的详细推导过程以及稳定性和收敛性分析过程,得到了算法稳定与收敛的严格正实条件以及相应放松严格正实条件的要求.在此基础上,通过构建实时振动主动控制实验平台,针对多种振动扰动开展对比实验分析.相关实验结果验证了本文提出的混合自适应振动主动控制方法的可行性和有效性.