基于两级参数算法的对偶控制

针对具有未知参数的随机系统，从随机次优的角度出发，将原不可解的动态规划问题转化为优化一个效用函数，该效用函数考虑了输出调节要求和参数学习要求及两者之间的折衷，充分利用了对偶控制较常规自适应控制的优越性．提出利用两级参数算法来最小化效用函数，从而获得控制信号．仿真结果表明，这种控制器具有良好的对偶性质，能得到较好的学习和控制效果．     

基于模型参考自适应方法的对偶控制方法研究

针对未知参数随机系统，基于模型参考自适应方法和双准则函数，设计了具有控制和学习作用的对偶控制器．通过控制器的学习作用提高了控制精度，有效地减少了启动时的超调．仿真结果表明，设计的控制器能够具有较好的跟踪性能．     

红外成像目标模拟器方位系统的

红外成像目标模拟器方位系统是一参数快时变系统,针对该系统的具体情况,提出了一种对偶自校正PID控制器.在每一自适应步,通过谱分解得到最优PID参数,然后基于双重指标进行对偶校正,得到一种既保持对偶效应,又十分简单易行的对偶自校正PID控制器,成功地消除了传统自适应控制系统的"关断”、"终止”和"猝发”等现象,收到了良好的控制效果.该控制器适于参数随机变化或快时变系统.     

非线性系统参数估计及与之对偶的自适应控制

非线性系统参数估计及与之对偶的自适应控制侯忠生，韩志刚（东北大学自控系沈阳１１０００６）（黑龙江大学应用数学研究所哈尔滨１５００８０）关键词：非线性系统，参数估计算法，自适应控制算法，收敛性，对偶性．１引言近十几年来，非线性系统自适应控制的研究非常活...     

红外成像目标模拟器方位系统的对偶自校正PID控制

红餐成像目标模拟器方位系统是一参数快时变系统,针对该系统的具体情况提出了一种对偶自校正ＰＩＤ控制器,在-每一适应步,通过谱分解得到最优ＰＩＤ参数,然后基于双重指 标进行对偶校正,得到一种既保持对偶效应,垒十分简单易行的对偶自校正ＰＩＤ控制器,成功地消除了传统自适应控制系统的“关断”、“终止”和“猝发”等现象,收到了良好的的控制效果,该控制器适于参数随机变化或快进变系统。     

一类对偶自适应预测函数控制算法

针对未知参数系统的自适应预测函数控制,模型尚未辨识完成或外界干扰造成的模型不准确,会严重影响控制效果,并产生较大的超调和波动.由此,提出一类对偶自适应预测函数控制(Dtml Adaptive Predictive Function Control,DAPFC)算法.在模型辨识的过程中,通过辨识误差的大小,利用对偶控制方法来调整原有自适应控制律,尽可能地获取未知参数信息并抑制由于模型失配造成的控制量的波动.改善了系统在模型失配时的控制效果,并具有较强的鲁棒性.仿真结果表明,该算法具有良好的控制品质.     

一种改进的自适应对偶控制算法

针对自适应对偶控制算法难以克服关断现象的问题,提出了一种改进的对偶控制算法.通过协方差矩阵中特征值和当前控制量在数量级上的比例关系,按照当前控制量的大小,调整抑制作用在以控制量为中心的一定范围内,实现动态调整谨慎控制作用的控制律,使得谨慎控制的抑制作用不会因为模型失配而变得过大.仿真结果表明,这种根据实际情况而动态调整参数的对偶控制算法可以有效克服关断现象,表现出良好的控制品质.     

基于最大互信息指标的对偶控制研究

针对参数未知但恒定的随机系统, 研究了基于最大互信息指标的对偶控制. 运用了Kalman滤波器估计随机系统未知参数的方法; 研究了最大互信息指标所具有的对偶特性, 即跟踪理想的目标以及探测未知参数的不确定性; 采用了两级优化算法获得次优对偶控制律. 算例验证了此算法的有效性和可行性.     

基于自适应理论的影像合成技术

提出了一种基于对偶树复小波-Contourlet变换的自适应影像合成新算法。算法将全色图像和多光谱图像进行对偶树复小波-Contourlet变换分解后,针对不同的频率域特点选择不同的合成规则。对低频系数选取区域能量的加权系数自适应合成规则,对高频系数特性选用了区域特征自适应的合成规则,最后通过重构得到合成图像。将其他的合成算法和本算法进行了主观和客观的对比,结果表明,基于对偶树复小波-Contourlet变换区域特征自适应的影像合成算法是一种有效可行的影像合成算法。     

噪声未知的LQG控制问题研究

对具有未知方差的线性二次高斯(LQG)问题,本文提出了具有学习特点的对偶自适应控制方法.控制器一方面能够控制系统朝着期望的目标运行,另一方面又能对未知参数进行学习,仿真结果表明了控制方法的有效性.     

Combining model reference adaptive controllers and stochastic self-tuning regulators

The similarities between model reference adaptive controllers (MRAC) for minimum phase plants where the control objectives are specified by reference models and stochastic self-tuning regulators (S-STURE) for minimum phase plants where the control objectives are specified by autoregressive moving average (ARMA) stochastic models are investigated.These similarities permit the extension, for these two classes of adaptive systems, of the duality existing in the linear case with known parameters between modal control and minimum variance control. The convergence analysis of these schemes in a combined deterministic and stochastic environment allows one finally to define schemes which behave as a desired MRAC in a deterministic environment and as a desired S-STURE in a stochastic environment. The problem of removing the positive real conditions for convergence in deterministic and stochastic environment is also discussed.     

奇异摄动型卡尔曼滤波算法及其在互联电力系统负荷频率控制中的应用

本文讨论了一种大系统实时随机最优控制算法--奇异摄动型卡尔曼滤波的设计问题. 建立了线性定常离散互联电力系统的奇异摄动模型.奇异摄动型卡尔曼滤波每循环一步所需 要的计算时间和内存量会大大减小.文中还考虑了控制增益的计算问题.通过对华北互联电 力系统LFC的仿真证明了其控制效果和奇异摄动型卡尔曼滤波算法的有效性.     

Finite Horizon Minimax Optimal Control of Stochastic Partially Observed Time Varying Uncertain Systems

We consider a linear-quadratic problem of minimax optimal control for stochastic uncertain control systems with output measurement. The uncertainty in the system satisfies a stochastic integral quadratic constraint. To convert the constrained optimization problem into an unconstrained one, a special S-procedure is applied. The resulting unconstrained game-type optimization problem is then converted into a risk-sensitive stochastic control problem with an exponential-of-integral cost functional. This is achieved via a certain duality relation between stochastic dynamic games and risk-sensitive stochastic control. The solution of the risk-sensitive stochastic control problem in terms of a pair of differential matrix Riccati equations is then used to establish a minimax optimal control law for the original uncertain system with uncertainty subject to the stochastic integral quadratic constraint. Date received: May 13, 1997. Date revised: March 18, 1998.     

无限时间长时延网络控制系统的随机最优控制

考虑二次性能指标下线性网络控制系统的随机最优控制问题,建立了控制器为事件驱动时长时延线性网络控制系统的数学模型,证明了在无限时间情况下离散随机黎卡提代数方程解的存在性,设计出无限时间情况下线性网络控制系统的随机最优控制器,得到相应的最优性能指标的表达形式,并证明了相应的随机最优控制器可使网络控制系统均方指数稳定.最后以网络控制下的倒立摆为对象进行仿真研究,仿真结果表明该方法的正确性和有效性.     

一类具有漂移、扩散及停时的奇异型随机控制

以随机分析的知识和最优控制理论为基础,推广了一类带停时的奇异型随机控制中的折扣费用模型,主要在受控状态过程中增加了漂移因子和扩散因子,使其为一随机微分方程的解,并将费用函数一般化.通过求解一组变分方程,证明了最优控制及最优停时的存在性,并给出了最优费用函数的解析表达式.     

Optimal control of a stochastic system with an exponential-of-integral performance criterion

An approach based on the theory of positive semigroups is introduced for the analysis of the infinite-horizon, exponential-of-integral optimal control problem for a stochastic system of the Ito form. Existence conditions for state-feedback admissible controllers are formulated and optimality conditions are derived. Connections between the exponential-of-integral optimal control problem and stochastic differential games are discussed.     

Input delay compensation of nonlinear stochastic systems with both state and input delays by prediction approach

In this study, we present an extension of the prediction scheme (dynamic control) for nonlinear stochastic systems with both input and state delays. The stochastic system includes multiplicative noise and it is modelled as Ito stochastic differential equation. Input delay is considered to be equal or less than state delay and both delays are considered to be constant. At first, a new formula for prediction of the system's state is presented and then by means of this prediction vector, control input is constructed. To calculate the stabilising gain of the predictive controller, some sufficient delay-independent conditions in the form of linear matrix inequality are presented. Finally, simulation examples are given to illustrate the effectiveness of the proposed approach.     

On the quantum master equation under feedback control

The nature of the quantum trajectories, described by stochastic master equations, may be jump-like or diffusive, depending upon different measurement processes. There are many different unravelings corresponding to different types of stochastic master equations for a given master equation. In this paper, we study the relationship between the quantum stochastic master equations and the quantum master equations in the Markovian case under feedback control. We show that the corresponding unraveling no longer exists when we further consider feedback control besides measurement. It is due to the fact that the information gained by the measurement plays an important role in the control process. The master equation governing the evolution of ensemble average cannot be restored simply by eliminating the noise term unlike the case without a control term. By establishing a fundamental limit on performance of the master equation with feedback control, we demonstrate the differences between the stochastic master equation and the master equation via theoretical proof and simulation, and show the superiority of the stochastic master equation for feedback control.     

Cautious stochastic computer control applied to concrete mixing

An application of direct digital control of a concrete mixing process is presented. The process which is irreversible is modelled as a linear stochastic system. The control problem is formulated as a final value control problem. A general final value prediction problem is formilated and solved. The regulator becomes an adaptive stochastic regulator. Its control actions are based on the uncertainty in the control-error which is calculated as the difference between the setpoint and the predicted final value. The resulting algorithm is implemented on a small minicomputer based control system and is used in every day production since more than a year ago.     

Stochastic model predictive control: Insights and performance comparisons for linear systems

In this paper, we define several instances of model predictive control (MPC) for linear systems, including both deterministic and stochastic formulations. We show by explicit computation of the associated control laws that, under certain conditions, different formulations lead to identical results. This paper provides insights into the performance of stochastic MPC. Amongst other things, it shows that stochastic MPC and traditional MPC can give identical results in special cases. In cases where the solutions are different, we show that the explicit formulation of the problem can give insight into the performance gap.