Multivariable Control Performance Assessment Based on Generalized Minimum Variance Benchmark

This paper is concerned with the control performance assessment based on the multivariable generalized minimum variance benchmark.An explicit expression for the feedback controller-invariant(the generalized minimum variance)term of the multivariable control system is obtained,which is used as a standard benchmark for the assessment of the control performance for multi input multi output(MIMO)process.The proposed approach is based on the multivariable minimum variance benchmark.In comparison with the minimum variance benchmark, the developed method is more reasonable and practical for the control performance assessment of multivariable systems.The approach is illustrated by a simulation example and an industrial application.

Robust Design for Generalized Minimum Variance Controllers

In order to improve the robustness and noise resistance of generalized minimum valance cothrol systems, several generalizedminimum variance control schemes are synthetically analyzed. The output variance caused by stochastic noise is decomposed to two parts. One part accords with the output variance of minboum vedance control and the other is the additional term of output variance causedby the control weighting factors. At the same time, the sensitivity function of modeling error is also deduced. A new robast design method that can minimize the sensitivity and the additional part of output variance is Presented by regulating variable parameters of contollers. The simulation results of self-tuning control show the effect of this method.

SIMULATION STUDY OF GENERALIZED MINIMUM VARIANCE CONTROL FOR AN EXTRACTION TURBINE

In an extraction turbine, pressure of the extracted steam and rotate speed of the rotor are two important controlled quantities. The traditional linear state feedback control method is not perfect enough to control the two quantities accurately because of existence of nonlinearity and coupling. A generalized minimum variance control method is studied for an extraction turbine. Firstly, a nonlinear mathematical model of the control system about the two quantities is transformed into a linear system with two white noises. Secondly, a generalized minimum variance control law is applied to the system. A comparative simulation is done. The simulation results indicate that precision and dynamic quality of the regulating system under the new control law are both better than those under the state feedback control law.

Design of Weigh Feeder Control System for Energy Saving Using Performance-Adaptive Method

This paper discusses a design method for the control system of a weigh feeder that supplies powder and granular material at a constant rate. Most weigh feeders employed in industry are controlled by proportional and integral （PI） compensation, and the control performance is decided by the selection of parameters. To attain advanced control performance by PI control, the PI parameters are designed on the basis of generalized minimum variance control （GMVC）. In this study, to achieve user-specified control performance by GMVC-based PI control, the design parameters of GMVC are automatically adjusted using a performance-adaptive method. The control performance discussed in this study consists of the variance of the control error and that of the difference in the control input. In a conventional performance-adaptive method, the variance of the control error is reduced. In this study, to reduce energy consumption and to achieve user-specified control performance, the variance of the difference in the control input is specified and the design parameter is determined. To demonstrate its effectiveness, the proposed method is applied to an actual weigh feeder.

Nonlinear Decoupling PID Control Using Neural Networks and Multiple Models

For a class of complex industrial processes with strong nonlinearity, serious coupling and uncertainty, a nonlinear decoupling proportional-integral-differential （PID） controller is proposed, which consists of a traditional PID controller, a decoupling compensator and a feedforward compensator for the unmodeled dynamics. The parameters of such controller is selected based on the generalized minimum variance control law. The unmodeled dynamics is estimated and compensated by neural networks, a switching mechanism is introduced to improve tracking performance, then a nonlinear decoupling PID control algorithm is proposed. All signals in such switching system are globally bounded and the tracking error is convergent. Simulations show effectiveness of the algorithm.

Study on Adaptive Control with Neural Network Compensation

A scheme of adaptive control based on a recurrent neural network with a neural network compensation is presented for a class of nonlinear systems with a nonlinear prefix. The recurrent neural network is used to identify the unknown nonlinear part and compensate the difference between the real output and the identified model output. The identified model of the controlled object consists of a linear model and the neural network. The generalized minimum variance control method is used to identify parameters, which can deal with the problem of adaptive control of systems with unknown nonlinear part, which can not be controlled by traditional methods. Simulation results show that this algorithm has higher precision, faster convergent speed.

计及静态电压稳定约束的最少发电机优化调度方法

电压稳定控制中的控制数量多,且紧急状态时要求控制措施快速响应。构建一种以最少发电机调整数量为目标的优化调度数学模型,该模型为包含离散变量和连续变量的非线性混合整数规划问题,为快速获得高质量的可行解,提出一种由快速筛选和模型转化组成的阶段式求解方法。利用负荷裕度对发电机有功出力的灵敏度快速筛选出可提高负荷裕度的发电机集合,缩小离散变量的搜索范围;将原问题转化为混合整数线性规划子问题以及非线性规划子问题进行交替求解,得到需要调整的发电机数量及其出力。通过IEEE 118和IEEE 300节点系统进行测试,结果表明所提控制模型和求解方法可有效提高电力系统负荷裕度,减少可行解中包含的发电机调整数量,与分支定界法的对比验证了所提求解方法的快速性和有效性。

重大风险事件下中美股票市场风险溢出效应研究

金融全球化背景下,重大风险事件所具有的突发性与不确定性给世界金融市场的稳定带来了极大的挑战,各国政府当局和学术界高度关注金融风险的快速传导和跨国、跨市场的溢出问题。本文基于溢出指数模型,从静态和动态两个层面定量研究中美股票市场风险溢出效应的动态趋势以及发生重大风险事件时风险溢出强度和传导方向的动态演变过程。研究发现,中美股票市场之间存在显著的风险溢出效应,美国市场是风险净溢出者,中国市场是风险溢出的净接受者;中美股市之间的风险溢出效应具有明显的时变特征,政治、经济、金融以及公共卫生等重大风险事件对股票市场溢出效应有显著增强的影响;中美股市风险溢出效应的传导方向由市场间的单向溢出转变为A股市场、中国香港市场和美国市场两两之间显著的双向溢出。因此,建议监管当局根据风险的时变特征和传导方向增强金融风险防控政策决策的全局性和前瞻性。

基于DEA模型的环境治理效率评价——以河南为例

在考察国内学者对环境治理问题的相关研究后,从新的视角出发,运用条件广义方差极小法和变差系数法对评价指标进行筛选,然后运用数据包络分析方法(DEA),对河南省近8年的环境污染治理相对有效性进行实证研究。结果表明,河南省的环境污染治理效率波动较大,环境治理技术水平亟需改进,环境污染治理效率有待提高。基于此研究结论,提出了相应的建议。

广义最小方差自校正控制在某型无人机中的应用

某型无人机主要用于典型空中目标的模拟飞行,需要严格稳定可靠的自主飞行控制,无人机在整个飞行包线中呈现非线性、时变特性,由此在建立其纵向运动数学模型的基础上,设计了无人机的纵向姿态广义最小方差自校正控制器。仿真结果表明,系统具有较强的跟踪能力、抗干扰能力以及抗过程参数变化能力,比常规PID控制系统具有更好的飞行控制性能。

超磁致伸缩致动器的广义最小方差-模糊PID控制方法

为了克服超磁致伸缩致动器用于精密驱动定位时磁滞现象所带来的影响,在进行磁滞补偿的前提下,研究了自校正PID控制原理,建立了基于广义最小方差原理(GMV)及模糊(Fuzzy)规则的广义最小方差-模糊PID(GMV-FuzzyPID)控制方法。介绍了广义最小方差原理的主要思想,并由此推导出PID参数与被控对象参数的关系。在上述关系的基础上,建立了以误差与误差变化量为输入,待定参数k为输出的模糊规则。最后,根据在线估计的被控对象参数及GMV-Fuzzy PID的输出,在线调整PID参数,实现磁滞补偿后对致动器的控制。实验结果表明:采用GMV-Fuzzy PID控制器,每次运算时间比采用Fuzzy PID控制器缩短了0.015 4 s,跟踪误差均方差相差0.036μm。该方法能有效消除被控对象由于扰动带来的影响并缩短运算时间,在一些实时性及控制精度要求较高的精密加工场合,有良好的应用价值。

多项式方程法在水轮发电机调速器中的应用

PID控制、最少拍控制均存在对系统参数敏感的问题,对于水轮发电机组具有参数时变性和非最小相位特性这样的系统,上述2种控制都难以达到满意的控制效果,但通过对PID控制过程的数据挖掘,用MATLAB中的regress函数可得到控制器的线性解析式,这个多项式方程包含了PID控制规律,但又具有对系统参数不敏感的特点,应用于水轮发电机组调速系统表现出了很强的鲁棒性。

基于改进遗传算法的直流锅炉主蒸汽温度自校正控制

针对变负荷下的超(超)临界机组主蒸汽温度控制问题,提出了一种基于改进遗传算法(GA)的自校正控制策略。采用GA辨识被控对象模型参数,并对种群初始化方法和遗传算子部分结构进行了改进;采用最小方差率构造控制器,并结合GA设计了自校正控制的运算流程。对某超临界直流锅炉主蒸汽温度控制的仿真结果表明,所提自校正控制方法具有更快的响应速度和更强的抗干扰能力,能够满足变负荷下的主蒸汽温度控制需求。

基于即时学习的非线性系统自适应PID控制

当使用先进策略整定PID控制器参数时,往往要依赖于系统所辨识的模型,而模型的精度与优化算法的计算效率直接影响到系统的控制效果.本文利用即时学习算法的本质自适应特点(建模数据在时间与空间上相邻性),来提高辨识模型的精度,并基于广义最小方差的性能指标,用等价多项式的方法,推导出PID形式的控制律,从而避免其他优化算法带来的计算量,提高了控制精度与计算效率.仿真结果验证了该方法的有效性.

统一的自校正控制器

本文提出两种统一的广义最小方差自校正控制器,以控制具有任意维数输入/输出的(ADIADO)系统.这两种新的控制器解决了以前无法处理的输入/输出维数不等、B_0奇异系统的自校正控制问题.几个典型的仿真例子证明了这两种控制器的统一性和有效性.

非线性自适应控制——克服非线性的影响

非线性自适应控制系统中，当工作点发生转移时，被控过程的增益有时会有很大的变化，这对使用广义最小方差控制的自校正控制器来说会产生一定的影响．文中对这种影响机理进行了讨论，并针对单变量系统提出两种合理的解决方法．

基于神经网络的一类非线性系统的自适应控制

基于递归神经网络给出了仅含一个非线性环节的一类非线性系统的自适应控制方案。该方案采用递归神经网络辨识非线性系统中的未知非线性环节。沿用广义最小方差自校正控制方法，可以解决非线性环节未知和工作点变化时传统方法无法控制的自适应控制问题。理论分析和仿真结果表明，该方法具有很好的控制效果。

具有可变性能指标的自校正自适应控制理论的研究

本文在广义最小方差基础上提出了具有可变性能指标函数的自校正控制方案，依据所要求的特征方程之根在线地改变权函数来确定控制律．本方案控制的算法不仅简单而且容易实现．计算机模拟仿真结果表明此控制律性能优于一般的自校正控制律，最终利用本文的方案对船舶航向实施自校正控制进行了讨论并实现了在“大明山”号轮船上的实际海试，结果表明性能优良．

工业过程PI控制器性能评价

针对工业过程的PI控制器,提出了一种控制器性能评价的新算法。该算法以广义最小方差为目标,推导出最优PI控制器参数。以该最优控制器为基础,得到PI控制下系统的最优控制性能。然后,以最优控制的输出为基准,进行性能评价,并给出了性能评价的可操作步骤。最后通过仿真实验验证了该方法的有效性。

测量噪声对GMVC性能影响分析与改进方法

由于工作环境的改变及各类干扰信号的影响,易使控制器性能降低,影响控制效果,因此需设计出一种可以在复杂情况下能够稳定工作的控制器。针对上述问题,提出了一种基于广义最小方差控制(GMVC)的控制器设计方法。GMVC控制器设计过程一般不考虑测量噪声,然而在实际过程中,系统不可能工作于理想情况下,会受到测量噪声的影响。因此首先分析了测量噪声对GMVC控制系统输出的影响,提出了采用数字滤波器的方式减小测量噪声对控制系统输出的影响,仿真结果说明了该方法的可行性和有效性。