基于卡尔曼滤波器的间歇反应预测控制

在间歇反应过程的温度跟踪控制问题中，为了实现全工况范围内反应器温度设定值的精确跟踪，设计了一种改进的预测控制算法。在反应自身产热对系统输出影响未知的情况下，用带有输入干扰的系统模型来建模夹套温度与反应温度之间关系，并将该干扰项增广为状态变量，然后结合线性卡尔曼滤波器估计出干扰的大小及其对系统输出的影响；结合估计出的干扰，在DMC控制算法对其加以补偿。最后在半实物的仿真环境中，验证了该控制算法在间歇温度控制中的效果，结果表明，该控制方法在一定程度上克服了反应产热对温度跟踪精度的影响，改善了控制效果。     

鲁棒预测迭代学习控制在间歇过程中的运用

考虑间歇反应中存在的非线性、实际情况中的输入输出约束要求和扰动的重复特性和非重复特性,将采用迭代学习和预测控制相结合的方法设计控制器,使得系统输出跟踪给定参考轨迹,最终使得间歇反应能够满足产品质量要求.由于迭代学习控制系统从本质上看汇聚了时间和批次两个变量,故可称为2维系统.针对2维系统,采用李亚普诺夫函数确保系统的稳定性并得到系统的控制序列,上述的控制序列可通过求解线性矩阵不等式求得.为了验证算法的有效性,将上述控制算法应用在对连续搅拌釜(CSTR)温度期望轨迹的跟踪控制中,仿真结果表明了控制算法的有效性.     

基于指令滤波和期望补偿的液压位置跟踪系统控制研究

针对液压控制系统中存在的强非线性、参数不确定性和高频干扰等问题,提出一种基于指令滤波和期望补偿的鲁棒控制算法.该控制算法结合扩展状态观测器(ESO),实现了对速度及外干扰的估计;所设计的自适应控制算法可以在线估计系统的不确定参数;利用期望补偿方法,将实际速度信号替换为相应的期望值,以减少对控制效果的影响.此外,利用指令...     

正交频分复用系统中的判决反馈干扰消除方法*

针对正交频分复用系统中Hadamard载波干扰矩阵估计方法估计效果差,提出了基于判决反馈的载波干扰矩阵估计方法.该方法根据载波干扰矩阵可用傅里叶变换矩阵对角化的特性,通过离散傅里叶变换的输出来估计载波干扰系数,再根据符号判决结果更新载波干扰矩阵,可较快地跟踪信道的快速衰落.实验仿真表明,该方法与Hadamard载波干扰矩阵估计方法相比,结合比特交织编码调制技术可进一步提高系统的载波干扰消除能力.     

考虑执行器性能约束的刚体航天器鲁棒姿态跟踪控制

针对存在模型不确定性、外界干扰力矩和执行器性能受限等约束条件下的刚体航天器姿态跟踪控制问题进行研究,并基于滑模控制、反步控制、自适应控制、辅助系统和动态面控制等方法设计相应的鲁棒姿态跟踪控制算法.利用自适应控制实现了对具有多项式形式上界函数的系统未知不确定性进行在线估计和补偿;通过建立描述执行器动态特性的低通滤波模型,并结合辅助系统方法,以确保执行器输出控制力矩的幅值及其变化率均满足一定的饱和约束;通过引入动态面控制法,避免期望虚拟控制信号的一阶导数项直接出现在控制器中,简化了闭环姿态跟踪控制器的设计形式.最后,通过数值仿真验证了所提出控制算法的有效性和可行性.     

基于信息融合估计的离散系统最优跟踪控制

针对期望输出和未来干扰无预见的离散线性系统最优跟踪问题,提出一种基于信息融合最优估计的控制方法.若将当前给定值和可测干扰值分别看作系统未来输出和干扰的预见值,则跟踪控制问题可转化为具有无限预见步数的预见控制问题,并将无限预见信息融合成一步等效预见信息,进而获得近似最优融合控制律.对线性直流电机系统和宏观经济系统的仿真结果均验证了该控制器在提高系统跟踪精度和抑制干扰等方面的有效性.     

加速抑制随机初态误差影响的迭代学习控制

针对一类具有不确定性的多输入多输出非线性系统,提出一种迭代学习控制算法.该算法具有的特点是:针对任意初态情形,结合开环 D型迭代学习控制器的优点,在时间轴上设计了一个随迭代次数增加而缩短的时间段.在该时间段上,控制算法对状态偏差进行修正,以使系统输出在此段时间后跟踪期望输出,且系统跟踪误差收敛到一个界内.这个界仅由系统自身不确定性和不确定的外界干扰决定,与初态误差无关.当外界扰动为0,以及迭代次数趋于无穷时,经过上述时间段后,系统输出精确跟踪期望输出.理论证明和仿真结果都说明了该算法的有效性.     

时滞离散系统的模糊—最优跟踪控制

工业过程中,时滞和系统的离散特性常会带来显著的影响,使系统的性能指标下降,产生不稳定;文章介绍了时滞离散系统的模糊—最优跟踪控制,该方法运用最优跟踪控制的极小值原理寻求最优控制律,结合模糊控制对运动控制反应快的优点,使该方法能够较好地计算出系统实际输出相对期望输出的偏差,并通过反馈回路校正实现无静差跟踪控制;在不同时滞条件下做大量的仿真计算,使火电厂主蒸汽温度有效的快速跟踪设定值;实验分析验证了该方法的有效性,表明模糊—最优跟踪控制器能够切换平滑,跟踪控制误差小,抗外干扰能力强。     

干扰与控制非同位一维薛定谔方程的输出跟踪

本文研究了干扰与控制非同位一维薛定谔方程的输出跟踪.首先,利用系统的无穷维结构与输出设计了用于估计干扰的无穷维干扰估计器.其次,建立自适应伺服机制使得跟踪误差u^~(1,t)∈L²(0,∞),且闭环系统的所有子系统有界.最后,对闭环系统进行数值模拟,模拟结果表明控制方案的有效性.     

干扰与控制非同位一维薛定谔方程的输出跟踪（英文）

本文研究了干扰与控制非同位一维薛定谔方程的输出跟踪.首先,利用系统的无穷维结构与输出设计了用于估计干扰的无穷维干扰估计器.其次,建立自适应伺服机制使得跟踪误差?u(1, t)∈L2(0,∞),且闭环系统的所有子系统有界.最后,对闭环系统进行数值模拟,模拟结果表明控制方案的有效性.     

Model predictive control of an industrial packed bed reactor using neural networks

This paper discusses an industrial application of a multivariable nonlinear feedforward/feedback model predictive control where the model is given by a dynamic neural network. A multi-pass packed bed reactor temperature profile is modelled via recurrent neural networks using the backpropagation through time training algorithm. This model is then used in conjunction with an optimizer to build a nonlinear model predictive controller. Results show that, compared with conventional control schemes, the neural network model based controller can achieve tighter temperature control for disturbance rejection     

Unscented Kalman filter based nonlinear model predictive control of a LDPE autoclave reactor

This paper presents a multivariable nonlinear model predictive control (NMPC) scheme for the regulation of a low-density polyethylene (LDPE) autoclave reactor. A detailed mechanistic process model developed previously was used to describe the dynamics of the LDPE reactor and the properties of the polymer product. Closed-loop simulations are used to demonstrate the disturbance rejection and tracking performance of the NMPC algorithm for control of reactor temperature and weight-averaged molecular weight (WAMW). In addition, the effect of parametric uncertainty in the kinetic rate constants of the LDPE reactor model on closed-loop performance is discussed. The unscented Kalman filtering (UKF) algorithm is employed to estimate plant states and disturbances. All control simulations were performed under conditions of noisy process measurements and structural plant–model mismatch. Where appropriate, the performance of the NMPC algorithm is contrasted with that of linear model predictive control (LMPC). It is shown that for this application the closed-loop performance of the UKF based NMPC scheme is very good and is superior to that of the linear predictive controller.     

基于类面向对象的模型表达与反应器模拟模型系统研究

提出了基于类面向对象的模型结构化表达方法,形成由模型框架、模型类和模型实例构成的继承层次,并应用到天然气等离子体合成乙烯乙炔反应器模拟研究中。同时考虑到模拟模型在多种条件下的优化,以星形模式设计描述模型关系。另外针对等离子体系的特点还讨论了以RGB三色素的不同合成描述温度分布的方法。最后介绍了反应器模拟模型系统的结构和功能。     

A control scheme for packed bed reactors having a changing catalyst activity profile. I: On-line parameter estimation and feedback control

The efficient control of an exothermic packed bed reactor is demonstrated in concert with the distributed parameter state estimation of temperature and concentration profiles and on-line estimation of the catalyst activity profile. The position-dependent catalyst activity is estimated using recursive least squares with a variable forgetting factor, a distributed mass balance model and the current estimated temperature profile. The temperature level in the reactor is regulated through control of the maximum estimated reactor temperature using the reactor coolant temperature as the manipulated variable. These methods are demonstrated both through closed-loop simulation and experiments with a laboratory-scale reactor for the partial oxidation of methanol. The experiments included both setpoint changes and disturbance rejection. The estimation and control system effectively overcomes errors in initial conditions, heat transfer parameters and kinetic parameters.     

广义模型预测控制算法在聚合反应釜温度控制上的应用研究

本文提出了一种全新的聚合反应釜温度控制算法,用于解决实际生产过程中反应釜温度具有的纯滞后、大惯性、多变量耦合等特点。考虑冷却水管网压力、冷却水温度和进料流量等多个变量对反应釜温度的影响,建立更加准确的数学模型进行控制,控制效果更加精确。基于该算法的聚合反应釜温度控制系统已经开发成功,并在实际聚合反应釜生产上投入使用,取得了比较满意的控制效果,验证了该算法的有效性。     

Robust iterative learning control of an exothermic semi-batch chemical reactor

This work focuses on the temperature control of a semi-batch chemical reactor used for flue chemicals production. Such reactor is equipped with a heating/cooling system composed of different thermal fluids. In order to ensure the tracking performance of the desired temperature profile, an iterative learning control (ILC) named batch model predictive control (BMPC) has been adopted. The synthesis of the considered strategy is illustrated and improvements of the algorithm scheme are proposed. Firstly, a guaranteed convergence of the algorithm is illustrated. Secondly, in presence of high frequency disturbance effects, an off-line filtering is adopted for enhancing the achieved performances. Third, a robust supervisory control procedure is employed to choose the right fluid and to reduce the superfluous fluid changeovers, mainly where fluids are of different nature. Finally, the incidence of repetitive disturbances, on line low frequency disturbances and model mismatch are investigated through simulation runs.     

Nonlinear inferential control of an autonomous periodic fixed-bed reactor

This paper deals with the control of an autonomous periodic fixed-bed reactor, which is called circulation loop reactor (CLR). This process can be described by a distributed parameter system with sustained oscillations. The state variables at some positions of such a process, e.g. the conversion at the reactor exit, may be required to vary in a smooth fashion, which in general is a difficult task due to the process's inherent complex behavior. Here, a straightforward control strategy of the CLR based on direct exit concentration regulation is presented. A relay feedback controller is found to be effective for achieving overall conversion while using relatively small external heating. In order to implement the control scheme without on-line concentration measurement, a nonlinear inferential control concept is adopted. A newly-developed nonlinear distributed parameter observer is used to infer the exit concentration from the temperature measurements at only a few positions. Moreover, an approach based on integrating a disturbance estimator is presented for improving the performance of the observer in the case of unmeasured inlet concentration. It is found that there exist simple determinate relations between the inlet variables or the model parameters and the maximum amplitude or the period of the oscillatory temperature at an axial position for the considered cases. By employing the oscillatory feature of the CLR, a method of estimating the inlet concentration is proposed, and a parameter estimation method based on the least square technique is given to update offline model parameters. It is shown that substantial benefits could be realized using the proposed nonlinear inferential control scheme for the CLR because it can provide excellent disturbance rejection capabilities and strong robustness.     

Combined Design of Disturbance Model and Observer for Offset-Free Model Predictive Control

This note presents a method for the combined design of an integrating disturbance model and of the observer (for the augmented system) to be used in offset-free model predictive controllers. A dynamic observer is designed for the original (nonaugmented) system by solving an H_prop control problem aimed at minimizing the effect of unmeasured disturbances and plant/model mismatch on the output prediction error. It is shown that, when offset-free control is sought, the dynamic observer is equivalent to choosing an integrating disturbance model and an observer for the augmented system. An example of a chemical reactor shows the main features and benefits of the proposed method.     

基于非线性系统T-S模糊模型的随机最小方差控制

以非线性系统的T-S模糊模型为基础,提出了一种基于最小方差性能指标的随机控制方法。分析了系统的稳定性问题并给出了系统稳定的充分条件。将此方法用于连续反应搅拌釜(CSTR)中和过程pH值控制的仿真试验,仿真结果表明了该方法的有效性。