多变量时滞系统的闭环渐进辨识研究与应用

多变量时滞系统由于滞后以及相互之间的耦合而难以得到理想的辨识效果,因此基于渐进理论,提出一种多变量的闭环辨识方法.采用阶跃作为测试信号,利用两阶段闭环辨识算法进行辨识.首先采用递推ARX算法实现高阶ARX模型对真实模型的逼近,消除耦合,然后利用二阶泰勒近似实现对滞后的辨识,最后采用渐进准则(ASYC)对模型进行降阶,利用输出误差(OE)准则对降阶后的模型进行参数估计.该方法在实际项目的应用中得到了较好的效果,应用前景广阔.     

应用非线性降阶观测器在线估计连续搅拌反应釜系统状态

本文以实际非线性连续搅拌反应釜(CSTR)为背景,利用 Lyapunov稳定性和指数可观系统概念,提出并设计了一种适合于工程实际应用的非线性降阶观测器,并实际用作实时在线观测估计CSTR系统的不可测状态。结果表明,这种观测器计算量小、结构简单、适应性强,且便于实时在线估算,即使系统处于随机干扰的环境,亦能获得满意的结果。     

一种新的双线性数据协调方法

在流程工业数据校正中,若涉及多组分物料平衡或能量平衡时,问题则转换为一类特殊的非线性问题,即双线性数据协调.今针对双线性数据协调传统方法的不足,给出了一种新的方法.首先提出了一种消除不可观测变量的方法,通过消除不可观测变量及部分非冗余变量将协调问题降维,并将问题分解为两个子问题;然后针对分解后的子问题,利用微粒群优化算法(PSO)求解.与传统方法相比,该方法在确保高协调精度的基础上具有较好的协调运算效率,并能处理过程中含有不可观测变量的情况.今对一个实例进行了仿真,仿真结果表明该方法的有效性.     

工程应用中一种有用降阶方法

模型降阶大部分方法都是通过纯粹的数学计算来降阶,计算量很大,有时得到的简化模型可能不稳定。为了解决以上问题,本文提出将阶跃响应建模法运用于模型降阶,通过作图和计算来确定低阶逼近模型参数的图解降阶法,并在文中做了大量仿真以便来验证本文中提出的降阶方法得到的低阶模型的近似程度。通过仿真比较可知,文中提出的图解降阶法不但计算量小,而且得到的低阶逼近模型稳定、精确,在工程上非常实用,为工程师分析和设计过程阶次较高的控制系统提供了一种方便快捷的降阶方法。     

基于测井数据小波变换的基准面旋回划分方法研究

测井数据的小波变换为地层基准面旋回的识别提供了可靠信息.本文基于连续小波变换和离散小波变换的特点,提出了一种在大尺度旋回划分中应用连续小波变换,小尺度旋回划分中应用离散小波变换的新方法,对GR测井信号进行小波分解,利用小波变换后的小波系数曲线进行基准面旋回的划分,最终结果表明该方法合理有效,具有可行性.这些研究是对小波变换应用于基准面旋回划分的一次有意义的探索.     

油藏数值模拟模型降阶方法研究及应用

数值模拟技术是油藏开发的关键技术之一,随着近年来非常规油气资源的不断开发和大数据时代到来,油藏数值模拟对模型的要求达到了新的高度,采用网格数更精细化的模型网格,其网格数可达到几百万,传统油藏模拟器进行模拟将非常耗时,所以对模型进行降阶就十分有必要。目前模型降阶已经在很多领域应用,对于复杂的系统降阶和多种模式融合都有一定的发展,但在油藏数值模拟中应用不多。本文分别列举Krylov子空间法、TBR方法、POD方法等模型降阶理论,并挑选出较好的降阶方法。     

一类非线性系统分析的新方法—斜波脉冲函数的应用

利用斜波脉冲函数的线性化特性和简洁的运算规则，本文将一类非线性系统转化为双线性系统然后进行分析和求解。与泰勒展开等其它方法相比较，所提出的方法不仅近似精度高，且不受范围限制，对被近似的函数无可微分的要求，所推导的算法还 具有连续递推的功能，很适合用计算机进行求解。     

框架结构半主动控制优化设计

以框架结构为研究对象,在设计出作动器的前提下,对结构设计参数进行摄动,以控制效果为优化目标来确定最优设计参数。在模型降阶方面,选择了基于系统内平衡理论的降阶方法。考虑到磁流变阻尼器的非线性特点及结构设计参数发生摄动时系统的可控性,研究了控制力为阶跃函数和具有较大的增益裕度的广义预测控制,并提出了相应的半主动控制方法,最后给出了计算实例。     

一氧化碳变换工艺与设备优化设计

结合工程设计运行实例,根据CO变换反应的工艺特点,从变换工艺、关键设备的优化设计角度探讨减小CO变换装置系统阻力降的方法。     

受扰双线性系统的近似最优扰动抑制方法

研究具有外界持续扰动作用下双线性系统的最优控制问题.关于二次型性能指标给出了一种设计最优扰动抑制控制律的逐次逼近方法.利用该算法可将在扰动作用下双线性系统的最优控制问题转化为求解一组线性非齐次两点边值序列问题.通过迭代序列得到的最优扰动抑制控制律由解析的线性前馈-反馈项和序列极限形式的非线性补偿项组成.通过截取非线性补偿序列的有限项,可以得到近似最优扰动抑制控制律.仿真结果表明,该方法抑制外部持续扰动的鲁棒性优于经典反馈最优控制.     

Approximate Pole Placement with Dominance for Continuous Delay Systems by PID Controllers

It is well known that a continuous‐time feedback system with time delay has infinite spectrum and it is not possible to assign such infinite spectrum with a finite‐dimensional controller. In such a case, only the partial pole placement may be feasible and hopefully some of the assigned poles are dominant. But there is no easy way to guarantee dominance of the desired poles. In this paper, an analytical PID design method is proposed for continuous‐time delay systems to achieve approximate pole placement with dominance. Its idea is to bypass continuous infinite spectrum problem by converting a delay process to a rational discrete model and getting back continuous PID controller from its discrete form designed for the model with pole placement. Simulation results are included to illustrate the effectiveness of this method.     

Frequency Domain Estimation of Continuous Time Cointegrated Models with Mixed Frequency and Mixed Sample Data

Recent work by the author on mixed frequency data analysis has focused on the estimation of cointegrated systems in continuous time based on a fully specified dynamic system of equations, while the estimation of cointegrating vectors in a discrete time system has been approached using a semiparametric frequency domain estimator. We extend the latter approach to cover the continuous time case, establishing the asymptotic properties of the frequency domain estimator and explore, in a simulation study, the effects of misspecifying the continuous time dynamic model in discrete time compared to treating the dynamics non‐parametrically. An empirical illustration is also provided.     

An aggregation model reduction method for one‐dimensional distributed systems

A method for deriving reduced dynamic models of one‐dimensional distributed systems is presented. It inherits the concepts of the aggregated modeling method of Lévine and Rouchon originally derived for simple staged distillation models and can be applied to both spatially discrete and continuous systems. The method is based on partitioning the system into intervals of steady‐state systems, which are connected by dynamic aggregation elements. By presolving and substituting the steady‐state systems, a discrete low‐order dynamic model is obtained. A characteristic property of the aggregation method is that the original and the reduced model assume identical steady states. For spatially continuous systems, the method is an alternative to discretization methods like finite‐difference and finite‐element methods. Implementation details of the method are discussed, and the principle is illustrated on three example systems, namely a distillation column, a heat exchanger, and a fixed‐bed reactor. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Deterministic Parameter Change Models in Continuous and Discrete Time

We consider a model of deterministic one-time parameter change in a continuous time autoregressive model around a deterministic trend function. The exact discrete time analogue model is detailed and compared to corresponding parameter change models adopted in the discrete time literature. The relationships between the parameters in the continuous time model and the discrete time analogue model are also explored. Our results show that the discrete time models used in the literature can be justified by the corresponding continuous time model, with a only a minor modification needed for the (most likely) case where the changepoint does not coincide with one of the discrete time observation points. The implications of our results for a number of extant discrete time models and testing procedures are discussed.     

Periodic section modeling of convective continuous powder mixing processes

This study aims to develop a general model of the convective continuous mixing process. The main idea is that continuous mixing can be considered as a combination of powder flow and mixing processes. Although powder flow is characterized by the residence time distribution (RTD), powder mixing can be described by a batch mixing process simulated in one periodic section of the continuous mixer. By characterizing the two processes separately, we can calculate the number of sections required to achieve certain homogeneity. In this study, continuous mixing is simulated using the discrete element method, and segregating and non‐segregating mixing cases are tested to investigate the applicability of the model. Results show satisfactory predictions by the model, which is able to characterize the continuous mixing performance of both mixing cases. On the basis of this study, we were also able to suggest a novel method in design and control of continuous powder mixing systems. © 2011 American Institute of Chemical Engineers AIChE J , 2012     

A group-contribution,continuous-thermodynamics framework for calculation of vapor-liquid equilibria

Continuous thermodynamics is extended to the calculation of vapor-liquid equilibria with UNIFAC. For many mixtures of discrete compounds, the UNIFAC model is an efficient method for calculating vapor-liquid equilibria of systems when experimental data are scarce or non-existent. Such constraints are especially important in continuous mixtures. The continuous distribution is characterized by model compounds chosen to reproduce the TBP analysis. The UNIFAC model is extended to systems including several gases, and the new parameters for several light gases are given. The new algorithm is described in detail. Sample calculations are shown for several semi-continuous systems.     

Disjunctive modeling for optimal control of hybrid systems

In this contribution, a novel approach for the modeling and numerical optimal control of hybrid (discrete–continuous dynamic) systems based on a disjunctive problem formulation is proposed. It is shown that a disjunctive model representation, which constitutes an alternative to mixed-integer model formulations, provides a very flexible, intuitive and effective way to formulate hybrid (discrete–continuous dynamic) optimization problems. The structure and properties of the disjunctive process models can be exploited for an efficient and robust numerical solution by applying generalized disjunctive programming techniques. The proposed modeling and optimization approach will be illustrated by means of optimal control of hybrid systems embedding linear discrete–continuous dynamic models.     

Effect of time delays in characterizing the continuous mixing of non-Newtonian fluids in stirred-tank reactors

Aqueous xanthan gum solutions are pseudoplastic fluids possessing yield stress. Their continuous mixing is an extremely complicated phenomenon exhibiting non idealities such as channeling, recirculation and dead zones within the stirred-tank reactors. To characterize the continuous mixing of xanthan gum solutions, three dynamic models were utilized: (1) a dynamic model with 2 time delays in discrete time domain, (2) a dynamic model with two time delays in continuous time domain, and (3) a simplified dynamic model with a single time delay in discrete time domain. A hybrid genetic algorithm was employed to estimate the model parameters through the experimental input–output dynamic data. The extents of channeling and fully mixed volume were used to compare the performances of these three models. The dynamic model parameters exerting strong influence on the response predicted by the dynamic model were identified. It was observed that the models with 2 time delays gave a better match with the experimental results.     

改进的全息搜索策略及其在化工优化中的应用

引言 基于样本数据估计反应动力学参数是常见的化工优化问题.参数估计的通用规则是偏差最小化,许多经典的序贯类寻优方法都可用于这一目的,例如Powell共轭梯度法、模式搜索法、变度量法等.     

Repetitive model predictive control of a reverse flow reactor

This paper deals with the control of a catalytic reverse flow reactor (RFR) used for methane combustion. The periodic flow reversals effected on the system makes it both continuous and discrete in nature (i.e., a hybrid system). Control of this system is challenging due to the unsteady state behavior of the process along with its mixed discrete and continuous behavior. Although model predictive control (MPC) is proven to be a powerful technique for several processes it becomes less effective in systems such as the RFR where the model prediction errors and the effect of disturbances on the plant output repeat from time to time. In such cases, control can be improved if the repetitive error pattern is exploited. A novel repetitive model predictive control (RMPC) strategy, that combines the basic concepts of iterative learning control (ILC) and repetitive control (RC) along with the concepts of MPC, is proposed for such systems. In the proposed strategy, the state variables of the model are reset periodically along with predictive control action such that the process follows the reference trajectory as closely as possible. The results obtained prove that the RMPC approach provides an excellent performance for the control of the RFR.