非线性观测器及其应用(上)一类高阶非线性系统的观测器

针对子系统的不可测状态之间呈下三角阵式关联的一类主阶非线性系统,本文给出了其观测器型式和反馈增益阵的设计方法,同时进一步讲座了存在不可测输入时系统的观测,这种观测器设计简单,便于实施和调变量进行的处理,对于化工生产过程中经常遇到的这类高阶非线性系统,这种观测器设计简单,便于实施和调整。     

一种新的常压塔汽油干点观测方法

提出一种新的汽油干点观测方法－机理模型与神经网络相结合的汽油干点观测方法。这一方法是先将Luenberger观测器理论应用到常压塔顶这一具有不可测输入的非线性系统,构造Luenberger观测器观测出一些不可测量但可观测的内部物理量;然后将这些内部物理量与其它物理量一起作为输入信号,汽油干点作为输出信号构成神经网络观测汽油干点,并用改进的学习算法训练神经网络;通过与其它方法进行比较,表明这一观测方法有较高的精确度及较强的适应性。     

观测器理论在催化裂化装置中的应用研究

本文从催化裂化反应机理和干扰观测器的理论出发,提出了利用当量干扰反映原料不可测性质的变化。利用组合观测器观测当量干扰和不可测状态。并通过仿真实验和现场实测来验证观测器结构的合理性和观测结果的正确性。     

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.     

不确定性间歇过程的一种实时优化控制方法

针对不确定性间歇过程的实时优化问题,提出了一种集成批间和批内优化的新实时优化控制方法。首先求解标称模型得到最优输入轨迹的结构,然后将输入轨迹参数化为若干标量决策变量及子输入轨迹。对最优性条件中的终端约束部分,使用批间优化满足约束条件;对梯度轨迹部分,提出一种回归法近似最优输入轨迹,以解决不确定扰动的在线不可测问题,实现梯度轨迹的批内优化。对一个间歇反应器的仿真研究表明了新方法的有效性。     

不确定性间歇过程的一种实时优化控制方法

针对不确定性间歇过程的实时优化问题,提出了一种集成批间和批内优化的新实时优化控制方法。首先求解标称模型得到最优输入轨迹的结构,然后将输入轨迹参数化为若干标量决策变量及子输入轨迹。对最优性条件中的终端约束部分,使用批间优化满足约束条件;对梯度轨迹部分,提出一种回归法近似最优输入轨迹,以解决不确定扰动的在线不可测问题,实现梯度轨迹的批内优化。对一个间歇反应器的仿真研究表明了新方法的有效性。     

人工神经网络软测量仪表延迟时间处理及动态特性研究

采用BP和RBF网络,开发了人工神经网络软测量仪表软件,实现了不可测变量的在线观测。讨论并解决了延迟时间的确定与处理、动态特性的拟合等主要难点问题。利用三层BP网络辨识出非线性对象的延迟时间;采用将输出量引入到多层静态神经网络的入口和对输入数据进行衰减加权的方法,完成对系统动态特性的表征,使所开发的神经网络软测量仪表更真实地反映了系统的静态和动态性能,准确性高且有更好的适应性。     

基于观测器的网络切换模糊系统的鲁棒镇定

研究一类系统状态不可测的不确定性网络切换模糊系统的鲁棒镇定问题。考虑单包传输且没有数据包丢失的情况,建立网络切换模糊系统的离散模型,使用切换技术及多Lyapunov函数方法,设计模糊观测器及反馈控制器,并以矩阵不等式的形式给出使系统渐近稳定的条件。最后仿真结果表明方法的有效性。     

基于观测器的时滞系统故障诊断

针对状态含有时滞的故障系统,给出一种故障诊断设计方法。通过对系统设计未知输入观测器,利用观测的状态得到残差方程,从而诊断出时滞系统的故障,并能对传感器故障和执行器故障进行分离,最后给出故障诊断系统阈值的选取原则。     

正三角形投测网平差及归化改正

在高层建筑施工测量中地面建筑施工轴线控制网需要不断向上投测构成投测网,在实际施工中野外测量数据在现场迅速快捷处理是比较困难的问题,为了满足工程实践的需要,探讨了满足投测精度的正三角形投测网平差问题并推导了相应的计算公式。投测网可以分两步进行平差计算,即:(1)采用条件平差消除观测值的矛盾;(2)利用第一步的平差结果及设计值计算各点的归化改正数,同时说明了该方法和秩亏自由平差结果是一致的,最后通过工程实例的具体数据说明该方法的正确性。     

基于广义状态观测器的催化裂化装置软仪表

与常规的输出校正软仪表不同,催化裂化装置软仪表的主导变量完全不可测,只能通过辅助变量进行在线校正,因此利用状态观测器构造催化裂化装置软仪表。催化裂化装置为指数1型广义非线性系统,理论分析证明,如果指数1型广义非线性系统在工作点线性化得到的广义线性变参数系统能检测,则可以通过线性化系统的在线极点配置来选取合适的状态观测器反馈增益阵,以确保广义状态观测器存在并在较大范围内是稳定的,从而保证软仪表对主导变量的在线估计值可以收敛于理论值。仿真结果表明,即使催化剂循环量为不可测输入变量,将其扩展为状态变量后,利用提升管反应器出口温度作为辅助变量进行在线校正,即可构成催化裂化装置软仪表完成对油品产率、催化剂循环量等不可测变量的在线估计,估计值能够稳定收敛到理论值并具有较好的动态性能。     

基于状态观测器的催化裂化装置不可测变量在线估计

采用基于非线性微分-代数方程的自适应状态观测器对催化裂化装置的油品产率、催化剂循环量等不可测变量进行在线估计.如果非线性微分-代数系统在工作点附近的线性化系统可观,则可以通过线性化系统的极点配置在线选取合适的状态观测器反馈增益阵,以实现能在较大范围内稳定工作的自适应状态观测器.可观性分析和观测器仿真表明,如果存在模型参...     

CONTROL AND ESTIMATION FOR PERTURBED NONLINEAR PROCESSES WITH DEADTIME

Feedback linearization techniques are used to deal with the nonlinear controller designs which have attracted many researchers' attention in recent years. The approach has been applied successfully to solve a number of practical nonlinear control problems, but typically requires on-line full state measurement which is usually not the case in real chemical process industries. In this paper, we address the problem of synthesizing nonlinear state feedback controllers for time-delay nonlinear systems which are perturbed by disturbances. On-line estimation of the unmeasurable disturbances and unavailable state variables is introduced to facilitate the implementation of coordinate transformations and state feedback and prediction. Two kinds of dynamic compensators are then proposed to handle the process deadtime. Finally numerical simulations in a CSTR example demonstrate the promising performance of the overall nonlinear control structure in disturbance rejection.     

MODELING AND CONTROL OF A TEMPERATURE-BASED HIGH-PURITY DISTILLATION COLUMN*

Industrial distillation columns typically lack adequate on-line instrumentation required for implementing high performance, model-based control systems which depend on composition measurements. The typical practice is to base product quality control schemes instead on tray temperature measurements since these are usually available on-line. While this strategy has been applied with success on a wide variety of distillation columns, some special considerations may be necessary in dealing with high-purity columns. This paper presents results which address some of the key issues involved with the use of tray temperature measurements as surrogates for composition measurements, and with the development of appropriate input/output models for multi variable control of high-purity columns. For the purpose of illustration, we investigate the performance of several model predictive control schemes based on a linear model, a “high frequency” model, and a nonlinear temperature transformation, along with that of conventional multiloop PID control. Closed-loop control performances are compared for setpoint changes as well as for changes in unmeasurable load disturbances.     

基于子系统关联变量轮换的大系统在线协调优化

化工过程复杂大系统的在线优化过程中,存在流程前后的关联导致优化时间过长或得不到优化解的问题,需要按子系统优化并协调的优化方法来解决,而协调优化方法存在关联变量寻优方向不一致的问题。提出了一种基于子系统间关联变量轮换思想的分解协调优化方法,对大系统分解得到的子系统以轮换的方式进行优化,子系统中包含的多个优化问题分别在固定关联变量优化独立变量和固定独立变量优化关联变量的条件下求解,此轮换过程迭代进行,直至满足优化终止条件。将提出的方法应用在催化裂化装置仿真实例中与整体优化方法的结果作比较,证明了方法的有效性。最后,将基于关联变量轮换的协调优化方法应用在化工过程的在线优化中,结果表明本方法在在线优化应用中是可行的。     

Robust controller synthesis for multivariable nonlinear systems with unmeasured disturbances

This work concerns robust controller synthesis using the differential geometric concepts for minimum phase nonlinear systems with unmeasurable disturbances. A pseudo-linearization of the disturbance model at the input-output linearization stage is applied to yield a linear subsystem for controller design. Based on this linear model, a multi-loop controller framework is implemented, whereby μ-synthesis is used to design off-line robust controller in the outer loop while state feedback is implemented in the inner loop. Through proper selection of weights, the outer robust controller is explicitly designed to address both uncertainty and disturbance rejection whereas the inner controller is used for on-line static state feedback. Numerical simulations are used to illustrate robustness of the controller for multi-input multi-output temperature control in two non-isothermal continuous stirred tank reactors in series.     

化工过程基于广义非线性状态观测器的不可测输入和不确定模型参数软仪表(英文)

Chemical processes are usually nonlinear singular systems. In this study, a soft sensor using nonlinear singular state observer is established for unknown inputs and uncertain model parameters in chemical processes, which are augmented as state variables. Based on the observability of the singular system, this paper presents a simplified observability criterion under certain conditions for unknown inputs and uncertain model parameters. When the observability is satisfied, the unknown inputs and the uncertain model parameters are estimated online by the soft sensor using augmented nonlinear singular state observer. The riser reactor of fluid catalytic cracking unit is used as an example for analysis and simulation. With the catalyst circulation rate as the only unknown input without model error, one temperature sensor at the riser reactor outlet will ensure the correct estimation for the catalyst cir- culation rate. However, when uncertain model parameters also exist, additional temperature sensors must be used to ensure correct estimation for unknown inputs and uncertain model parameters of chemical processes.     

Multi-stage economic model predictive control for a gold cyanidation leaching process under uncertainty

In order to dynamically operate the gold cyanidation leaching process (GCLP) under uncertainty, a multi-stage economic model predictive control (EMPC) is proposed for GCLP for the transient and steady-state economic optimization. The proposed multi-stage EMPC is composed of two steps. In the first step, the unmeasurable uncertain parameters are estimated by using Tikhonov regularization based method, so as to avoid amplification and propagation of the noise measurements into the estimation. Based on the estimated results, the scenario tree for multi-stage EMPC is generated from the historical data using a data-driven approach, and the control inputs are obtained from solving the resulting large nonlinear programming problem (NLP) at each sampling point. The resulting uncertainty model and the probability of each scenario are more consistent with the actual industrial GCLP, and the solutions are less conservative. The efficiency of the proposed multi-stage EMPC is verified through a simulated industrial GCLP. Compared with other EMPC methods, including classic EMPC and multi-stage EMPC with box uncertainty region, the proposed method can reduce the economic cost while accounting for the constraints at the same time.     

FCCU反再系统异常工况的安全分析

石油化工行业因生产规模大、生产工艺复杂、原料多易燃易爆,所以具有高危险性。对石化行业进行安全分析是保证安全生产的重要途径。基于石油催化裂化(FCCU)反再系统动态模型实现了再生催化剂失活、再生器稀相尾燃、反应器催化剂跑损3种异常工况的动态模拟。将工商管理模式的及时生产方式(just-in-time,JIT)应用于反再系统的安全分析,建立了上述4种异常工况的简化模型,实现了异常工况的及时识别。该方法能够有效地辨识不同状态下过程的运行规律,具有一定的可行性和优势,可以为实际生产安全提供准确、可靠的指导。将JIT理论用于动态过程的非线性分析,希望为动态系统的辨识提供一种新思路。     

Soft sensors model optimization and application for the refinery real-time prediction of toluene content

Industrial facilities nowadays show an increasing need for continuous measurements, monitoring and controlling many process variables. The on-line process analyzers, being the key indicators of process and product quality, are often unavailable or malfunction. This paper describes development of soft sensor models based on the real plant data that could replace an on-line analyzer when it is unavailable, or to monitor and diagnose an analyzer’s performance. Soft sensors for continuous toluene content estimation based on the real aromatic plant data are developed. The autoregressive model with exogenous inputs, output error, the nonlinear autoregressive model consisted of exogenous inputs and Hammerstein–Wiener models were developed. In case of complex real-plant processes a large number of model regressors and coefficients need to be optimized. To overcome an exhaustive trial-and-error procedure of optimal model regressor order determination, differential evolution optimization method is applied. In general, the proposed approach could be, of interest for the development of dynamic polynomial identification models. The performance of the models are validated on the real-plant data.