STOCHASTIC ADAPTIVE SWITCHING CONTROL BASED ON MULTIPLE MODELS

1 IntroductionIn an uncertain and complex environment, the approach of "optimal" switching is often usedfor making decisions through predicting and comparing the effects of multiple schemes. In thearea of control, the mu1tipIe model approach, which has been used to improve estimations andcontrol accuracies, can be traced back at least to 1960s--1970s(see, e.g., [1l--[3l). Some practicalapplications have also been reported[4,']. In adaptive control, switching cootroller based onmultiple models…     

一种新型的非线性随机大系统自适应镇定方法

研究了一类具有严反馈形式并含有Wiener噪声扰动的非线性随机大系统,其互联项满足线性增长约束。利用微分几何的理论,应用Backstepping算法,基于Lyapunov稳定性理论,选择了适合这类组合非线性系统的状态观测器,设计了自适应输出反馈分散控制律和自适应律,使得闭环,互联的非线性随机大系统实现了概率意义下的全局稳定,仿真结果表明了控制算法的有效性。     

ADAPTIVE SYSTEMS THEORY： SOME BASIC CONCEPTS, METHODS AND RESULTS

The adaptive systems theory to be presented in this paper consists of two closely related parts: adaptive estimation (or filtering, prediction) and adaptive control of dynamical systems. Both adaptive estimation and control are nonlinear mappings of the on-line observed signals of dynamical systems, where the main features are the uncertain-ties in both the system‘s structure and external disturbances, and the non-stationarity and dependency of the system signals. Thus, a key difficulty in establishing a mathematical theory of adaptive systems lies in how to deal with complicated nonlinear stochastic dynamical systems which describe the adaptation processes. In this paper, we will illustrate some of the basic concepts, methods and results through some simple examples. The following fundamental questions will be discussed: How much information is needed for estimation? How to deal with uncertainty by adaptation? How to analyze an adaptive system? What are the convergence or tracking performances of adaptation? How to find the proper rate of adaptation in some sense? We will also explore the following more fundamental questions: How much uncertainty can be dealt with by adaptation ? What are the limitations of adaptation ? How does the performance of adaptation depend on the prior information ? We will partially answer these questions by finding some “critical values“ and establishing some “Impossibility Theorems“ for the capability of adaptation, for several basic classes of nonlinear dynamical control systems with either parametric or nonparametric uncertainties.     

基于Simulink的空调系统先进自校正控制仿真研究

空调系统可以被看作是一阶惯性加延迟的过程,系统具有时延和参数时变的特性.提出了一种先进的自校正控制策略并将其应用在空调系统中.该控制策略采用了一种混合的参数辨识算法,在控制系统闭环运行条件下,空调系统的过程参数包括延迟时间可以被在线辨识.辨识出的延迟时间被Smith预估器用来补偿控制回路中的时延,同时该控制策略的控制信号可由内环回路中的PI控制器根据ITAE整定规则计算获得.Simulink仿真结果表明该自校正控制策略较自适应PI控制的控制性能更为优越.     

Self-tuning measurement fusion white noise deconvolution estimator with correlated noises

For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics, an on-line noise statistics estimator is presented by using the correlation method. Substituting it into the steady-state Riccati equation, the self-tuning Riccati equation is obtained. Using the Kalman filtering method, based on the self-tuning Riccati equation, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented. By the dynamic error system analysis (DESA) method, it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization, so that it has the asymptotic global optimality. A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.     

一类非线性系统的直接自适应模糊滑模控制

研究了一类非线性系统的一种直接适应模糊滑模控制（AFSMC）的问题。首先，在系统的非线性动态函数满足可估计和有界两个基本假设的条件下，给无了此类系统基于非线性动态函数估计的一般滑模控制律设计。然后基于目标函数梯度校正的方法，通过自适应机构调解模糊逻辑系统（FLS）的后作参数，所设计的自适应模糊逻辑系统（AFLS）能够逼近系统基于非线性动态函数估计的一般滑模控制律。这样，系统的自适应模糊控制律具有一般滑模控制律的控制效果，同时由于AFLS的滤波作用，具有消除滑模控制高频抖振的特性。数值仿真结果证明了所设计控制律的有效性。     

Dynamic Power Saving via Least-Square Self-Tuning Regulator in the Virtualized Computing Systems

In recent years,power saving problem has become more and more important in many fields and attracted a lot of research interests.In this paper,the authors consider the power saving problem in the virtualized computing system.Since there are multiple objectives in the system as well as many factors influencing the objectives,the problem is complex and hard.The authors will formulate the problem as an optimization problem of power consumption with a prior requirement on performance,which is taken as the response time in the paper.To solve the problem,the authors design the adaptive controller based on least-square self-tuning regulator to dynamically regulate the computing resource so as to track a given reasonable reference performance and then minimize the power consumption using the tracking result supplied by the controller at each time.Simulation is implemented based on the data collected from real machines and the time delay of turning on/off the machine is included in the process.The results show that this method based on adaptive control theory can save power consumption greatly with satisfying the performance requirement at the same time,thus it is suitable and effective to solve the problem.     

基于限制容差关系的加权信息熵

信息论观点是利用粗集理论研究信息系统性质的一种重要途径和方法。从信息论角度研究不完备信息系统的属性约简问题。在仔细分析完备信息系统的信息熵概念的本质之后,通过引入权数,研究了基于限制容差关系的不完备信息系统加权信息熵和条件熵问题。还提出了基于一般的集值函数的信息系统加权信息熵和条件熵概念。所得结果为在一般二元关系下的信息和决策表的知识获取提供了理论依据。     

Adaptive tracking of a class of first-order systems with binary-valued observations and fixed thresholds

This paper considers the adaptive tracking problem for a class of first-order systems with binary-valued observations generated via fixed thresholds.A recursive projection algorithm is proposed for parameter estimation based on the statistical properties of the system noise.Then,an adaptive control law is designed via the certainty equivalence principle.By use of the conditional expectations of the innovation and output prediction with respect to the estimates,the closed-loop system is shown to be stable and asymptotically optimal.Meanwhile,the parameter estimate is proved to be both almost surely and mean square convergent,and the convergence rate of the estimation error is also obtained. A numerical example is given to demonstrate the efficiency of the adaptive control law.     

多变量自校正控制计算机辅助设计软件系统

本文介绍了一个可帮助用户进行多变量自校正控制系统设计,而使用户不必了解算法细节的计算机辅助设计(CAD)软件系统;它既包括一些经典的自校正控制算法,又引入了一些近年来新研究的成果。使用情况表明,该系统算法先进,使用方便,控制效果较好,是一个实用的软件工具。     

用于船舶噪声分类的局域自适应子波神经网络分类器

提出了一种用于船舶噪声分类的局域自适应子波神经网络分类方法。首先利用傅里叶变换对三类船舶噪声进行预处理,然后利用网络局域化构造局域自适应子波神经网络分类器。通过对实际的三类处理后的船舶噪声谱进行自动特征提取并分类,分类结果令人满意,证明了该方法的优越性和工程应用前景。     

推广的多传感器数据融合算法

本文对工程实际中多传感器系统线性化后存在未知的系统误差，测量噪声具有指数衰减相关，且与状态噪声相关的问题，提出了推广的多传感器数据的分层融合算法和多传感器自适应数据融合算法，给出了计算流程图，可以对目标的状态进行实时估计，这两种算法对防空导弹体系制导雷达组网数据融合具有理论意义与实用价值。     

基于径向基函数网络或模糊系统的非线性连续时间系统的自适应调节

对于一类连续时间的非线性动态系统x=f(x)+Bu+d,当系统中的非线性函数f(x)满足有界或线性增长条件(具有未知的增长系数)时,首先证明了f(x)中的x落入一紧集中,然后根据径向基函数网络或模糊系统的逼近性质,给出了两种自适应调节器的设计方法.利用李亚普诺夫稳定性理论,证明了控制算法是全局稳定的,闭环系统的状态是一致最终有界的,且控制律是稳定的.     

具有可变终端代价项的非线性模型预测控制

针对离散时间非线性系统的模型预测控制问题,提出了一种具有可变自调节终端代价项的模型预测控制方案.此方案通过引入自调节因子,并对其设定不同的自调节规则,使终端代价函数项具备了可变自调节性能.证明了带有可变自调节终端代价项的模型预测控制方案可保证闭环系统的性能.为自调节因子提出了若干自调节规则,并分别分析了它们的性质.用实例证明了研究结果的有效性.     

船舶航线保持自适应控制器的设计与仿真试验研究

主要介绍适用于船舶航线保持自动控制系统的自校正控制算法，由于选择了具有偏航距离加权约束的广义最小差性指标，使所设计的控制器不仅适合于航向保持自动操舵，而且适合于航线保持自动控制，实验表明，该控制器具有良好的性能。     

基于异步估计自适应Kalman滤波系统设计

一般的Kalman滤波器时系统噪声和测量噪声统计特性要求较为严格,当统计特性存在不确定性时,估计会造成较大的估计误差,甚至使滤波器发散.针对此问题,在基于新息的自适应估计和极大后验估计的基础上对Q和R进行异步估计;以某型号光纤惯性测量装置和GPS系统为背景的仿真实验结果表明,该方法能够有效地对Q和R存在不确定的组合导航系统的误差状态变量进行估计,并能较好地保证自适应滤波器的收敛性,进一步提高了估计精度.     

一种带未知时变系统噪声水平的目标跟踪滤波器

在机动目标跟踪中，为了保证Kalman滤波器的数值稳定性和最优性，未知的时变系统噪声水平需要在线估计，但已有方法主要针对平稳或统计特性缓变的噪声过程。在Sage-Husa系统噪声水平自适应估计算法的基础上，通过引入基于新息的滤波器发散检测判据和利用强跟踪滤波器的思想，提出了一种系统噪声水平估计值的时变调节因子阵来抑制因系统噪声水平突变而引起的滤波器可能出现的发散问题。Monte-Carlo仿真结果表明，该算法不仅数值稳定性好，同时目标的跟踪精度也得到明显改善。     

基于四阶累积量的自适应参数型多径时延估计

在存在多通道信号干扰及背景噪声的情况下，本文提出了一种基于四阶累积量的自适应参数型时间延时估计方法。该方法准确地进行多径传输情况下的时间延时估计，有效地抑制相关高斯噪声的影响，仿真结果说明了方法的有效性。该时间延时估计方法可用于声纳及雷达系统的目标定位。     

非匹配不确定时滞系统的自校正滑模控制

针对一类不匹配、不确定并具有状态时滞的多输入多输出线性系统,基于线性矩阵不等式理论提出一种新的自校正滑模控制方法。该方法利用线性矩阵不等式给出滑动模态存在的充分条件,使系统在滑动模态运动下对于存在的不匹配不确定性扰动以及状态时滞具有完全不变性。引入双极性Sigmoid函数代替符号函数并根据Lyapunov稳定性理论设计了具有自适应能力的滑模控制器,在自适应律的作用下Sigmoid函数的边界层厚度以及切换增益可根据系统状态进行自适应调节,从而改善了滑模控制器输出量过大以及抖振等现象。基于Lyapunov理论证明了该方法的稳定性,最后通过仿真实验进一步验证了该方法的可行性及有效性。     

基于支持向量机的混沌时间序列非线性预测

提出一种新的应用支持向量机回归原理的混沌时间序列非线性预测方法,同时利用自适应的方法对支持向量机的参数进行优化.仿真结果显示支持向量机具有比传统的回归方法更好的泛化能力,预测方法具有很高的预测精度,同时还讨论了支持向量机中参数以及嵌入维数的变化对泛化误差的影响,得出的结论与统计学习理论中的VC维理论相一致.