基于预测观测器的时滞系统故障诊断

提出一种新的可有效检测和估计时滞系统故障的预测观测器.借助预测控制思想,在选取的优化时域内构建预测观测器,通过调节预测观测器中的可调参数来跟踪系统实际输出.与以往基于H∞滤波器的故障诊断不同,预测观测器将其转化为控制问题来解决,不但能检测出故障的发生,而且还可估计出故障函数,对不同类型的故障也有很好的适应性.对各类故障的仿真结果表明了该方法的可行性和有效性.     

自适应神经元非模型多变量系统解耦控制

根据解耦原理和神经网络的思想,提出一种不依赖对象模型的自适应神经元解耦方法,并以采用单神经元PID作为控制器的三输入三输出系统为例,详细分析了神经元解耦控制的学习方法,及在多变量系统中的工作机理,仿真结果表明,其构成的神经元补偿器具有很强的自学习功能和自适应解耦能力。     

两种基于不同周期的多变量预测控制研究

本文在一般预测控制的基础之上，对于两种特殊的系统分别提出了对应的预测控制算法，仿真效果表明了这两种算法的正确性和实用性。     

Fast reference governors for second-order linear systems with constraints and an input time-delay

This note provides a solution to the constrained command tracking problem using reference governors for a class of continuous-time second order linear systems with an input delay and with pointwise-in-time state and control constraints. The reference governor modifies the command to a closed-loop system based on the prediction of whether the system response to constant commands violates the specified constraints. The solution relies on classical control results for second order linear systems and requires only checking whether predicted outputs violate the constraints at a small number of time instants (e.g., four time instants in the single output case). This greatly simplifies the online computation, especially when a reference governor is applied to system models that are (slowly) changing in time. The effectiveness of the proposed method is demonstrated by a numerical example.     

多变量双线性广义预测极点配置自校正解耦控制器(续)

对一类多变量双线性系统提出了一种基于预测状态空间实现的GPP自校正控制算法,建立了预测状态与模型结构参数和输入输出信息之间的直接关系,给出了含有多个加权矩阵的多变量二次型性能指标。增加了系统设计的自由度。由于加权因子可以根据闭环系统稳定性要求以及系统动态特性、前馈零点增补、输出滤波和跟踪要求分别加以选取,可以保证闭环系统稳定并改善了系统动态特性增强了鲁棒性,仿真结果表明了该算法具有GPP的诸多优点。     

Multiple models and neural networks based decoupling control of ball mill coal-pulverizing systems

Using a ball mill coal-pulverizing system as a motivating/application example, a class of complex industrial processes is investigated in this paper, which has strong couplings among loops, high nonlinearities and time-varying dynamics under different operation conditions. Focusing on such processes, an intelligent decoupling control method is developed, where the effects of nonlinearities are dealt with by neural network compensations and coupling effects are handled by specifically designed decoupling compensators, while the effect of time-varying dynamics is treated by a switching mechanism among multiple models. The stability and convergence of the closed-loop system are analyzed. The proposed method has been applied to the ball mill coal-pulverizing systems of 200 MW units in a heat power plant in China. Application results show that the system outputs are maintained in desired scopes, the electric energy consumption per unit coal has been reduced by 10.3%, and the production rate has been increased by 8%.     

双输入双输出时延系统的系统预报控制

本文针对双输入双输出时延系统提出了脉冲响应系数预报控制和解耦控制，该方案具有较好的适应性。仿真结果证明了上述控制方案的有效性。     

人工鱼群算法优化的PID神经网络解耦控制

针对多变量系统解耦控制的要求和特点,传统的PID神经网络在选取初始权值难以确定,往往是随机得到,容易导致采用的BP学习算法陷入局部极值.提出了一种人工鱼群算法优化PID神经网络初始权值.通过对多变量控制对象的mat-lab仿真验证,把人工鱼群算法优化得到的最优初始权值带入PID神经网络,结果显示加快了PID神经网络的收敛速度,使控制量迅速地接近控制目标,保证了系统稳定性,取得了满意的控制效果.     

时滞系统的状态预测观测器及预测控制器设计

研究基于状态空间模型的时滞控制系统的状态预测观测器及最优预测控制器的设计问题。针对控制项含有时滞的系统,设计一种全维状态预测观测器,并将其用于时滞控制系统的最优状态预测反馈控制。通过该状态预测观测器可将闭环系统的时滞项移至系统闭环结构之外,从而使其优化控制规律完全可按无时滞系统进行设计。所给出的性能指标计算公式表明,该预测控制器关于二次型性能指标是次优的。     

基于labVIEW的解耦控制系统实现

介绍了自行设计的一套解耦控制实验系统的硬件构成和软件设计,模块式硬件结构简单灵活、安全可靠,基于labVIEW开发平台的人机界面友好、操作方便,大大简化了系统开发过程,并使该系统具有了工程应用意义。最后给出了基于该系统的一例控制结果。     

一类非最小相位系统的多变量多模型解耦控制器

为解决系统暂态响应变差问题,提出一种基于多模型切换的多变量直接自适应控制器,通过加权多项式矩阵的选择,可消除稳态误差,实现静态解耦控制,该控制器由多个参数已知固定模型和两个自适应模型构成,多个固定参数控制器模型可由系统参数模型通过映射直接得到,并与邻城一起完全覆盖控制器参数模型集,仿真结果表明,对于非最小相位系统,暂态响应可得到明显改善。     

Correlation-based tuning of decoupling multivariable controllers

The iterative method labelled correlation-based tuning (CbT) is considered in this paper for tuning linear time-invariant multivariable controllers. The approach allows one to tune some elements of the controller transfer function matrix to satisfy the desired closed-loop performance, while the other elements are tuned to mutually decouple the closed-loop outputs. Decoupling is achieved by decorrelating a given reference with the non-corresponding outputs. The controller parameters are calculated either by solving a correlation equation (decorrelation procedure) or by minimizing a cross-correlation function (correlation reduction). In addition, the preferred way of exciting a 2×2 system for CbT is investigated via the accuracy of the estimated controller parameters. It is shown that simultaneous excitation of both reference signals does not improve the accuracy of the estimated controller parameters compared to the case of sequential excitation. In fact, one must choose between low experimental cost (simultaneous excitation) and better accuracy of the estimated parameters (sequential excitation). The theoretical results are illustrated via three simulation studies.     

Decoupling and arbitrary coefficient assignment in time-delay systems

This paper considers decoupling control of time-delay systems. We assume a new decoupling condition which is stronger than the one obtained until now and R[σ]-controllability. Then, a dynamic compensator to achieve decoupling and arbitrary coefficient assignment is presented.     

Multivariable multipurpose controllers

In this note, a procedure is presented to design a multivariable system to achieve arbitrary pole-placement, decoupling, asymptotic tracking and disturbance rejection. The procedure is a modification of the one in [1]. The total degree of compensators required in this design is generally smaller than the one there.     

FGC多变量预测解耦控制仿真

该文提出一种基于线性化模型冷连轧机动态变规格(FGC)解耦控制器。该控制器采用预测控制算法,将解耦和控制器的设计融为一体,设计过程相对简单。仿真结果证明系统有很好的动态响应,很大程度上弥补了线性化模型的不足。     

Multivariable predictive control of a pressurized tank using neural networks

The behavior of a multivariable predictive control scheme based on neural networks applied to a model of a nonlinear multivariable real process, consisting of a pressurized tank is investigated in this paper. The neural scheme consists of three neural networks; the first is meant for the identification of plant parameters (identifier), the second one is for the prediction of future control errors (predictor) and the third one, based on the two previous, compute the control input to be applied to the plant (controller). The weights of the neural networks are updated on-line, using standard and dynamic backpropagation. The model of the nonlinear process is driven to an operation point and it is then controlled with the proposed neural control scheme, analyzing the maximum range over the neural control works properly.     

含状态和输入时滞的网络控制系统镇定

研究含有更广泛被控对象的网络控制系统镇定问题．假定被控对象含有时变状态和输入时滞,采用Lyapunov-Krasovskii泛函方法分析了系统的稳定性．所提出的方法可转化为线性矩阵不等式的形式,因而可较为容易地求得控制器和广义最大允许时延．仿真结果说明了所提出方法的有效性．