结构动力响应计算的瞬时界面关连矩阵法

结构动力响应计算的瞬时界面关连矩阵法杜国君，彭天国（东北重型机械学院）ＴＲＡＮＳＩＥＮＴＢＯＵＮＤＡＲＹＳＵＲＦＡＣＥＲＥＬＥＶＡＮＴＭＡＴＲＩＸＭＥＴＨＯＤＦＯＲＣＡＬＣＵＬＡＴＩＮＧＤＹＮＡＭＩＣＲＥＳＰＯＮＳＥＯＦＳＴＲＵＣＴＵＲＥ￥ＤｕＧｕｏ...     

边界元法中非对称满系数矩阵方程组的分块求解法

边界元法中非对称满系数矩阵方程组的分块求解法刘晓坤，王建军（西安石油学院）路民旭，张大鸣（西北工业大学）ＡＢＬＯＣＫＥＱＵＡＴＩＯＮＳＯＬＵＴＩＯＮＴＥＣＨＮＩＱＵＥＡＮＤＩＴＳＰＲＯＧＲＡＭＦＯＲＵＮＳＹＭＭＥＴＲＩＣＤＥＮＳＥＭＡＴＲＩＸＩＮＴＨ...     

高分辨光时域反射（OTDR）测量系统

高分辨光时域反射（ＯＴＤＲ）测量系统沈阳工业大学邵大川１引言时域反射法（ＯＴＤＲ）是一种用途十分广泛的测量方法，特别是在广播通信事业快速发展的今天，它被视为测定信道特性的最有效的方法之一。其主要原因有二：一是时域反射法简单易用，且是非损坏性的；二是测...     

矩形板动力响应计算的迁移子结构法

矩形板动力响应计算的迁移子结构法杜国君，陈继荣（东北重型机械学院，齐齐哈尔）ＴＨＥＴＲＡＮＳＦＥＲＳＵＢＳＴＲＵＣＴＵＲＥＭＥＴＨＯＤＡＢＯＵＴＤＹＮＡＭＩＣＲＥＳＰＯＮＳＥＣＡＬＣＵＬＡＴＩＯＮＯＦＲＥＣＴＡＮＧＵＬＡＲＰＬＡＴＥＳ￥ＤｕＧｕｏ－ｊ...     

线性规划内点法的并行计算

线性规划内点法的并行计算王思群，魏紫銮（中国科学院计算数学与科学工程计算研究所）ＰＡＲＡＬＬＥＬＣＯＭＰＵＴＡＴＩＯＮＦＯＲＬＩＮＥＡＲＰＲＯＧＲＡＭＭＩＮＧＵＮＤＥＲＴＨＥＩＮＴＥＲＩＯＲＰＯＩＮＴＭＥＴＨＯＤ￥ＷａｎｇＳｉｑｕｎ；ＷｅｉＺｉｌｕａ...     

关于strong形RUE－NRF归结的反倒

关于ｓｔｒｏｎｇ形ＲＵＥ－ＮＲＦ归结的反倒欧阳丹彤，刘叙华（吉林大学计算机科学系长春１３００２３）ＡＣＯＵＮＴＥＲＥＸＡＭＰＬＥＯＦＲＵＥ－ＮＲＦＲＥＳＯＬＵＴＩＯＮＩＮＳＴＲＯＮＧＦＯＲＭ￥ＯｕｙａｎｇＤａｎｔｏｎｇａｎｄＬｉｕＸｕｈｕａ（Ｄｅｐａ...     

ERP小辞典(20)

ＥＯＱ：这是经济订货批量法（ＥＣＯＮＯＭＩＣＯＲＤＥＲＱＵＡＮＴＩＴＹ）的英文缩写,这是一种理想化的确定批量的方法。它假设需求是均匀发生的,从而平均库存量是订货批量的一半。其中,年需求量、每次订货费用、年保管费占平均库存的百分比及物料单价等都是ＥＯＱ需考虑的基本要素。除此之外,还有固定订货批量法（ＦＯＱ）、按需确定批量法（ＬＦＬ）、按固定时区的需求量确定批量法（ＦＰＲ）、时区订货批量法（ＰＯＱ）、最小单位费用法（ＬＵＣ）、最小总费用法（ＬＴＣ）、ＷＡＧＮＥＲ－ＷＨＩＴＩＮ算法等都可供企业在确定计…     

任意长度GFT的W变换算法

任意长度ＧＦＴ的Ｗ变换算法付彬（湖南大学数学系）曾泳泓（国防科技大学七系）Ｗ－ＴＲＡＮＳＦＯＲＭＡＬＧＯＲＩＴＨＭＯＦＱＦＴＷＩＴＨＡＲＢＩＴＲＡＲＹＬＥＮＧＴＨ￥ＦｕＢｉｎ（ＨｕｎａｎＵｎｉｖｅｒｓｉｔｙ，Ｃｈａｎｇｓｈａ，Ｈｕａｎａ）ＺｅｎｇＹｏ...     

关系数据库与面向对象数据库的集成

本文从ＲＤＢ（ｒｅｌａｔｉｏｎａｌｄａｔａｂａｓｅ）的缺欠、ＯＯＤＢ（ｏｂｊｅｃｔｏｒｉｅｎｔｅｄｄａｔａｂａｓｅ）的不足和新的数据库应用需求３个方面指出了统一关系范型和ＯＯ范型、集成ＲＤＢ与ＯＯＤＢ的趋势，讨论了集成ＲＤＢ与ＯＯＤＢ的３种途径，最后介绍了一个集成关系与对象模型的多媒体数据库系统ＴＨＣＩＭＨＹＰＥＲ的总体设计.     

带状（块）Toeplitz方程组的快速并行算法

带状（块）Ｔｏｅｐｌｉｔｚ方程组的快速并行算法成礼智，蒋增荣（国防科技大学）ＦＡＳＴＡＮＤＰＡＲＡＬＬＥＬＡＬＧＯＲＩＴＨＭＳＦＯＲＳＯＬＶＩＮＧＢＡＮＤ（ＢＬＯＣＫ）ＴＯＥＰＬＩＴＺＳＹＳＴＥＭＳＯＦＥＱＵＡＴＩＯＮＳ￥ＣｈｅｎｇＬｉ－ｚｈｉ；Ｊｉ...     

Stabilising feedback control schemes of dynamical systems with completely unknown saturated inputs: an adaptive design method

The objective of this paper is mainly to present a simple design method to synthesise the stabilising feedback control schemes for dynamical systems with input saturations. In this paper, such a simple design method, called an adaptive design method, is presented so that (i) the presented design method is easy to understand for the system designers; (ii) it is not necessary to know any information on the saturated input nonlinearities; and (iii) the resulting control schemes are simple and easy to implement in practical control problems. Here, a linear dynamical system with any unknown saturated input nonlinearities is used to describe this method. For such dynamical systems, by making use of the presented design method, the resulting feedback control schemes consist of a conventional optimal control law and an adaptive control gain, and the resulting closed-loop control dynamical systems are globally stable. By combining the presented adaptive design method with other control ones, a number of interesting results can be expected for a rather large class of dynamical systems with saturation in the actuators. Finally, some illustrative numerical examples are provided to demonstrate the validity of the presented adaptive design method.     

基于多变量广义预测控制的不稳定系统控制结构选择方法

对于多输入多输出系统, 在控制系统设计时首先要对被控变量和操纵变量进行控制结构选择. Bristol提出的相关增益矩阵(Relative gain array, RGA)法, 以及学者们后来提出的各种改进方法, 都只适用于稳定系统. 本文针对不稳定系统, 基于多变量广义预测控制(Generalized predictive control, GPC)的闭环控制律提出了一种控制结构的变量匹配准则. 通过对预测时域、控制时域等各个参数的优化选择, 使系统闭环稳定; 由闭环控制律得到被控变量期望值与操纵变量的相关性矩阵, 以此得出控制结构的变量配对方案. 通过实例研究表明, 对于开环不稳定系统, 该方法可以得出正确的变量配对结果.     

Anti-synchronization of four-wing chaotic systems via sliding mode control

Sliding mode control is an important method used in nonlinear control systems. In robust control systems, the sliding mode control is often adopted due to its inherent advantages of easy realization, fast response and good transient performance as well as its insensitivity to parameter uncertainties and disturbances. In this paper, we derive new results based on the sliding mode control for the anti-synchronization of identical Qi three-dimensional (3D) four-wing chaotic systems (2008) and identical Liu 3D four-wing chaotic systems (2009). The stability results for the anti-synchronization schemes derived in this paper using sliding mode control (SMC) are established using Lyapunov stability theory. Since the Lyapunov exponents are not required for these calculations, the SMC method is very effective and convenient to achieve global chaos anti-synchronization of the identical Qi four-wing chaotic systems and identical Liu four-wing chaotic systems. Numerical simulations are shown to illustrate and validate the synchronization schemes derived in this paper.     

基于ESO的一类线性时变系统自学习滑模控制方法

针对一类线性时变系统的控制问题,提出了一种基于扩张状态观测器的自学习滑模控制方法。该方法首先设计了两种非线性光滑函数, 然后将两种光滑函数分别应用于扩张状态观测器和滑模趋近律的设计。为了进一步提高系统的自适应控制能力, 使用最速下降法对滑模控制器的增益参数进行自学习镇定。仿真结果表明了该控制方法不仅响应速度快、控制精度高, 而且有效解决了现有理论方法难以解决的问题, 因而是一种有效的不依赖于被控对象模型的LTV系统控制方法。     

网络控制系统的最大允许时延界

提出确定多输入多输出网络控制系统的最大允许时延界的新方法. 由于网络诱导时延的分布特性, 整个多输入多输出网络控制系统实际上是一个多时延系统. 利用李雅普诺夫第二方法, 得到网络控制系统时延相关渐近稳定性判据. 最大允许时延界和输出反馈镇定控制器均可通过求解矩阵不等式(LMI)得到. 仿真比较说明了本文结果的正确性和可行性.     

Generalized Hamilton–Jacobi–Bellman Formulation -Based Neural Network Control of Affine Nonlinear Discrete-Time Systems

In this paper, we consider the use of nonlinear networks towards obtaining nearly optimal solutions to the control of nonlinear discrete-time (DT) systems. The method is based on least squares successive approximation solution of the generalized Hamilton-Jacobi-Bellman (GHJB) equation which appears in optimization problems. Successive approximation using the GHJB has not been applied for nonlinear DT systems. The proposed recursive method solves the GHJB equation in DT on a well-defined region of attraction. The definition of GHJB, pre-Hamiltonian function, HJB equation, and method of updating the control function for the affine nonlinear DT systems under small perturbation assumption are proposed. A neural network (NN) is used to approximate the GHJB solution. It is shown that the result is a closed-loop control based on an NN that has been tuned a priori in offline mode. Numerical examples show that, for the linear DT system, the updated control laws will converge to the optimal control, and for nonlinear DT systems, the updated control laws will converge to the suboptimal control.     

Generalized hamilton-jacobi-bellman formulation -based neural network control of affine nonlinear discrete-time systems

In this paper, we consider the use of nonlinear networks towards obtaining nearly optimal solutions to the control of nonlinear discrete-time (DT) systems. The method is based on least squares successive approximation solution of the generalized Hamilton-Jacobi-Bellman (GHJB) equation which appears in optimization problems. Successive approximation using the GHJB has not been applied for nonlinear DT systems. The proposed recursive method solves the GHJB equation in DT on a well-defined region of attraction. The definition of GHJB, pre-Hamiltonian function, HJB equation, and method of updating the control function for the affine nonlinear DT systems under small perturbation assumption are proposed. A neural network (NN) is used to approximate the GHJB solution. It is shown that the result is a closed-loop control based on an NN that has been tuned a priori in offline mode. Numerical examples show that, for the linear DT system, the updated control laws will converge to the optimal control, and for nonlinear DT systems, the updated control laws will converge to the suboptimal control.     

A hybrid fault prediction method for control systems based on extended state observer and hidden Markov model

Fault diagnosis and prediction for complex control systems rely either on the collection of rich data for training neural networks or on the system models and prior knowledge of faults. These methods are difficult to apply directly in complex integrated systems due to the large uncertainties in practical scenarios. A new fault diagnosis and prediction technique that is based on extended state observer (ESO) and a hidden Markov model (HMM) for control systems is proposed in this paper. Real-time and predictive information that is obtained by ESO of the active disturbance rejection control (ADRC) is utilized to improve the HMM method for the fault prediction of control systems with large uncertain disturbances. The proposed approach realizes a high recognition rate with a small demand for data, and the dependence on the system model is weak without prior knowledge of faults. Fault prediction of the control system output can be realized without additional sensors. The proposed solution is evaluated in simulations of an asynchronous servo motor control system against the traditional control method and the ADRC control. The results indicate that the proposed method performs well in fault prediction and outperforms the traditional method in terms of control when disturbances and failures occur.     

基于GQM模型的工业控制系统风险评估方法

风险评估是保证工业控制系统安全的重要机制，当前，信息安全和功能安全的耦合越来越紧密，考虑到不同组织的业务目标和运营环境多样化程度高，工控系统信息安全风险评估应紧密结合业务目标。基于目标-问题-度量（GQM）模型，从目标确定、问题描述、度量指标定义工控系统风险评估流程，以工控系统所承载的业务目标为指引，基于风险场景模型提出问题，围绕提出的问题收集信息，根据收集的信息和数据对度量指标进行关联分析和评价。最后，以PLC风险评估为实例，具体说明和验证了基于GQM模型的工业控制系统风险评估方法的有效性。     

Robust multi-parametric model predictive control for LPV systems with application to anaesthesia

We present a multi-parametric model predictive controller (mpMPC) for discrete-time linear parameter-varying (LPV) systems based on the solution of the mpMPC problem for discrete-time linear time-invariant (LTI) systems. The control method yields a controller that adapts to parameter changes of the LPV system. This is accomplished by an add-on unit to the implementation of the mpMPC for LTI systems. No modification of the optimal mpMPC solution for LTI systems is needed. The mpMPC for LPV systems is entirely based on simple computational steps performed on-line. This control design method could improve the performance and robustness of a mpMPC for LPV systems with slowly varying parameters. We apply this method to process systems which suffer from slow variation of system parameters due, for example, to aging or degradation. As an illustrative example the reference tracking control problem of the hypnotic depth during intravenous anaesthesia is presented: the time varying system matrix mimics an external disturbance on the hypnotic depth. In this example the presented mpMPC for LPV systems shows a reduction of approximately 60% of the reference tracking error compared to the mpMPC for LTI systems.