随机控制系统

讨论了一类具有不确定噪声的连续时间广义随机控制系统的鲁棒Kalman滤波器的设计问题。

This paper considers the design method of a robust Kalman filter for continuous time descriptor stochastic control systems with uncertain noise .

通过应用扩散过程的最优控制问题在[10]中刻画了一类非线性随机控制系统的可控集合，当H∞控制器是由[17]中一类非线性随机时滞系统构造。

A controller set of a class of nonlinear stochastic control systems was characterized by using optimal control theory of diffusion processes in [ 10 ] , while H ∞ controllers was constructed for a class of nonlinear stochastic time-delay systems in [ 17 ] .

随机控制系统稳态Kalman滤波器新算法

New algorithms of steady state Kalman filter for stochastic control systems

基于CO2和TVOC浓度的空调新风随机控制系统

Air conditioning fresh air random control systems based on concentration of CO_2 and TVOC

完全耦合的正倒向随机控制系统的LQ问题

The LQ problem for fully coupled forward-backward stochastic control system

用射影理论，基于Kalman滤波提出了通用和统一的白噪声估计方法，可统一解决带非零均值相关噪声的线性离散时变随机控制系统的白噪声滤波、平滑和预报问题。

By the projection theory , general and unified white noise estimation approach is proposed based on Kalman filtering . It can solve the white noise filtering , smoothing and prediction problems in a unified framework for linear discrete time_varying stochastic control systems with correlated noises having non_zero mean .

针对时滞、阶数和系数皆未知的离散时间线性随机控制系统（ARMAX模型），提出一种对时滞、阶数和系数同时进行递推估计的算法。

This paper proposes a new recursive estimate algorithm for unknown time-delay , orders and coefficients of linear discrete-time stochastic control systems ( ARMAX model ) .

应用现代时间序列分析方法，基于受控的自回归滑动平均（CARMA）新息模型，提出了随机控制系统稳态Kalman滤波器增益的两种新算法，避免了求解Riccati方程。

Using the modern time series analysis method , based on the controlled autoregressive moving average ( CARMA ) innovation model , two new algorithms of steady state Kalman filter gain for stochastic control systems are presented , where the solution of the Riccati equation is avoided .

对带相关噪声的线性离散随机控制系统，应用Kalman滤波方法，基于CARMA新息模型导出了统一的最优固定区间白噪声递推Wiener平滑器，它带有系数阵指数衰减到零的高阶多项式矩阵。

For the discrete stochastic control systems with correlated noises , using the Kalman filtering method , based on the CARMA innovation model , a unified optimal fixed-interval white noise recursive Wiener smoother is derived . It contains high degree polynomial matrices with coefficient matrices exponentially decaying to zero .

该文考虑具有未知阶数和系数的离散时间线性随机控制系统（ARMAX模型），提出一种便于在线实施的自适应控制新算法。

This paper considers single-input single-output linear discrete-time stochastic feedback control systems ( ARMAX model ) with unknown orders and coefficients , and propose a new adaptive control algorithm , which is easily implemented on-line .

应用偏差补偿最小二乘法辨识随机控制系统

Identification of Stochastic Control Systems by Using the Bias-compensated Least-squares Method

对多级多段随机控制系统也进行了讨论。

The case of stochastic multi-step systems is also discussed .

控制能量受限下随机控制系统精确能控性

Exact controllability of stochastic control systems with control energy constraint

随机控制系统大纯时滞辨识及结构辨识的偏差补偿算法

The Large Time Delay Identification and the Bias Compensation for Structure Discrimination of Stochastic Control Systems

由于随机控制系统能够精确描述系统周围的不确定因素，其理论研究一直受到许多学者的重视。

As stochastic control systems can accurately describe the uncertain factors surrounding system , their theoretical research has been highly thought by many scholars .

在非线性时滞随机控制系统中引入不确定性的数学模型可以更真实地描述实际控制过程和反应系统的参数摄动和外部扰动。

The uncertainty mathematics model may more factually describe the real nonlinear control system with time-delays and depict parameter perturbation and external disturbance of the system .

本文研究了部分可观测的随机控制系统及在线性二次最优控制，微分对策和最优投资组合选择等问题中的应用。

In this paper , we mainly study partially observed stochastic control systems and their applications to linear quadratic optimal control , differential game and optimal portfolio choice problems .

在控制系统的所有系数包含控制变量且控制域为凸集的假定下，得到了部分可观测的完全耦合正倒向随机控制系统的最大值原理。

The maximum principle for partially observed fully coupled forward and backward stochastic control system is considered under the assumption that the control variable can enter into all the coefficients of the control system but the control domain is a convex set .

对于确定性系统的最优控制规律和随机控制系统的卡尔曼滤波器也可以分别进行设计。本程序既可用于系统设计，又可用于配合现代控制理论教学使用。

The optimal control regular of determined system and the Kalman filter of random control system , by means of the program , can be designed respectively , The program may be used both the design of system and the teaching of modern control theory .

随机逻辑控制系统的PC程序设计

PC Programing on Random Logic Control System

介绍了将布袋收尘器随机PLC控制系统改为DCS系统控制的方法，重点阐述了用顺序控制和ST语言相结合实现布袋收尘器脉冲清灰的逻辑过程。

The paper introduces the method of transferring the random PLC control system of bag collector into DCS , and emphasizes the logic process of realization of bag collector 's pulse purging with the combination of continuous control and ST language .

因此研究随机脉冲控制系统具有重要的意义。

So it is great significance to study stochastic impulsive systems .

随机网络控制系统的性能分析与鲁棒控制

Performance Analysis and Robust Control for Stochastic Networked Control Systems

提出了一种有界随机动态控制系统输出均值的非线性鲁棒预测控制器。

A nonlinear one-step-ahead predictive mean controller for bounded dynamic stochastic systems is proposed .

随机分布控制系统的自适应迭代学习控制

Iterative Learning Control of Stochastic Distribution Control Systems

数字随机振动控制系统改进设计

Improved design of digital random vibration control system

随机振动控制系统中含未知噪声方差的自适应滤波

An adaptive Kalman filter for the system of controlling stochastic vibration with unknown noise variances

然而，目前基于功率谱均衡的数字式随机振动控制系统只能产生高斯分布的随机振动激励信号。

However , the digital random vibration control systems currently based on power spectrum fitting can only generate random vibration exciting signals with Gaussian probability distribution .

研究这类非线性多阶段随机最优控制系统，构造求解该系统的全局优化算法以及把算法应用到实际的三维水平井井眼轨道设计中。

The present dissertation intends to study the nonlinear , multi-phase , stochastic optimal control system , construct a global optimization algorithm , and applies the algorithm into the practical design of the trajectory of horizontal well .

为了达到施工控制的基本要求，可针对施工过程组成随机最优控制系统，对结构状态理论值与实测值之间的误差进行分析、调整、预测。

In order to achieve the basic requirement of the control of the construction process , we can view of random optimal control system of structure state between theoretical and measured , the paper analyses the error , the adjustment , the forecast .