一种间歇过程产品质量迭代学习控制策略

针对基于迭代学习控制的间歇过程产品质量优化控制算法难以进行收敛性分析的难题,以数据驱动的神经模糊模型为基础,提出一种新颖间歇过程的产品质量迭代学习控制方法。通过在优化算法中加入了新的约束条件,改变了最优解的搜索空间范围,从而使产品质量在批次轴上收敛,并创新性地对优化问题的收敛性给出了严格的数学证明。在理论研究的基础上,将提出的算法用于间歇连续反应釜的终点质量控制研究,仿真结果验证了本文算法的有效性和实用价值,为间歇过程的优化控制提供了一条新途径。     

基于无约束迭代学习的间歇生产过程优化控制

针对基于迭代学习控制的间歇过程优化控制算法难以进行收敛性分析的难题,本文基于数据驱动的神经模糊模型提出一种新颖的间歇过程无约束迭代学习控制方法,通过调节因子的变化去除了约束条件,使控制轨迹在批次轴上收敛,并创新性地对优化问题的收敛性给出了严格的数学证明。在理论研究的基础上,将本文提出的算法用于间歇连续反应釜的终点质量控制研究,仿真结果验证了本文算法的有效性和实用价值,为间歇过程的优化控制提供了一条新途径。     

基于T-S模糊模型的间歇过程的迭代学习容错控制

间歇过程不仅具有强非线性,同时还会受到诸如执行器等故障影响,研究非线性间歇过程在具有故障的情况下依然稳定运行至关重要。针对执行器增益故障及系统所具有的强非线性,提出一种新的基于间歇过程的T-S模糊模型的复合迭代学习容错控制方法。首先根据间歇过程的非线性模型,利用扇区非线性方法建立其T-S模糊故障模型,再利用间歇过程的二维特性与重复特性,在2D系统理论框架内,设计2D复合ILC容错控制器,进而构建此T-S模糊模型的等价二维Rosser模型,接着利用Lyapunov方法给出系统稳定充分条件并求解控制器增益。针对强非线性的连续搅拌釜进行仿真,结果表明所提出方法具有可行性与有效性。     

基于MPLS的间歇过程终点质量迭代优化控制

提出了多向偏最小二乘(MPLS)模型和迭代学习控制相结合的方法,实现间歇过程终点时刻产品质量指标的控制.利用间歇过程的重复特性,根据前一批次的终点质量偏差调整下-批次控制变量的轨迹,从而使质量指标逐步接近于理想指标.本文提出的方法可以有效地消除由于模型误差和未知扰动引起的质量偏差.在苯乙烯间歇聚合反应模型上进行了仿真分析,验证了该方法的有效性.     

基于间歇反映器的迭代学习控制

根据间歇反应的特性,提出改进型的PID迭代学习算法。用这种方法实现的PID控制器结果简单,作用于系统中可以得到较强的鲁棒性和较好的动态特性。通过仿真结果可以看出这种方法具有很好的可行性和实用性。     

基于改进PDF技术的间歇过程NFM模型

间歇过程是一类具有典型复杂非线性特性的生产过程,可以利用模糊神经网络(NFM)建立其输入输出的非线性映射关系。在前期的研究中曾提出过基于概率密度函数(PDF)技术的模型训练方法,成功解决了传统的基于MSE准则训练方法模型泛化能力弱的问题,但又产生了概率密度难以估计及目标PDF未知时模型性能不稳定的问题。针对这两个问题,引入了新的概率密度窗宽估计方法,并提出了在目标PDF未知时采用PDF预估器及其收缩策略的算法。仿真实验表明:该方法能够保证足够的概率密度估计精度和模型预测性能。     

基于输入轨迹参数化的间歇过程迭代学习控制

针对间歇过程的迭代学习控制问题,提出了一种基于输入轨迹参数化的迭代学习控制策略。根据最优输入轨迹的主要形态特征,将其参数化为较少量的决策变量,降低传统迭代学习控制复杂性的同时维持良好的优化控制效果。基于输入轨迹参数化的迭代学习控制策略能保持算法的简洁性和易实现性,在不确定扰动影响下逐步改善产品质量。对一个间歇反应器的仿真研究验证了本文方法的有效性。     

基于广义预测控制的间歇生产迭代优化控制

针对间歇生产,提出了一种基于广义预测控制的批次迭代优化控制策略--BGPC,在间歇过程中引入批次间优化的思想,将迭代学习控制ILC和广义预测控制GPC相结合,在GPC实时结构参数辨识的基础上利用前面批次的模型预测误差修正当前批次的模型预测值.该算法能够有效地克服模型失配、扰动和系统参数变化等情况.文章最后以一个数值例子和间歇反应器为对象进行仿真试验,验证了该算法是有效的.     

间歇生产过程的自动控制

本文简要介绍了间歇生产过程及其特点,结合间歇生产过程对控制的要求,提出了相应的自动控制方案。阐述了间歇生产过程自动化的实现方法。     

间歇生产过程的控制系统

论述了间歇生产过程控制的特点、硬件系统的选择原则和软件的开发方法及功能 ,并具体介绍了一个间歇精细化工的特殊生产过程控制的实例 ,对从事间歇生产过程控制开发、设计工作的人员 ,具有实用参考价值     

基于线性时变扰动模型的间歇过程最优迭代学习控制

A batch-to-batch optimal iterative learning control (ILC) strategy for the tracking control of product quality in batch processes is presented. The linear time-varying perturbation (LTVP) model is built for product quality around the nominal trajectories. To address problems of model-plant mismatches, model prediction errors in the previous batch run are added to the model predictions for the current batch run. Then tracking error transition models can be built, and the ILC law with direct error feedback is explicitly obtained. A rigorous theorem is proposed, to prove the convergence of tracking error under ILC. The proposed methodology is illustrated on a typical batch reactor and the results show that the performance of trajectory tracking is gradually improved by the ILC.     

High-order iterative learning model predictive control for batch chemical processes

In order to address two-dimensional (2D) control issue for a class of batch chemical processes, we propose a novel high-order iterative learning model predictive control (HILMPC) method in this paper. A set of local state-space models are first constructed to represent the batch chemical processes by adopting the just-in-time learning (JITL) technique. Meanwhile, a pre-clustered strategy is used to lessen the computational burden of the modelling process and improve the modelling efficiency. Then, a two-stage 2D controller is designed to achieve integrated control by combining high-order iterative learning control (HILC) on the batch domain with model predictive control (MPC) on the time domain. The resulting HILMPC controller can not only guarantee the convergence of the system on the batch domain, but also guarantee the closed-loop stability of the system on the time domain. The convergence of the HILMPC method is ensured by rigorous analysis. Two examples are presented in the end to demonstrate that the developed method provides better control performance than its previous counterpart.     

A novel two-dimensional PID controller design using two-dimensional model predictive iterative learning control optimization for batch processes

It is known that the key indicators of batch processes are controlled by conventional proportional–integral–derivative (PID) strategies from the view of one-dimensional (1D) framework. Under such conditions, the information among batches cannot be used sufficiently; meanwhile, the repetitive disturbances also cannot be handled well. In order to deal with such situations, a novel two-dimensional PID controller optimized by two-dimensional model predictive iterative learning control (2D-PID-MPILC) is proposed. The contributions of this paper can be summarized as follows. First, a novel two-dimensional PID (2D-PID) controller is developed by combining the advantages of a PID-type iterative learning control (PIDILC) strategy and the conventional PID method. This novel 2D-PID controller overcomes the aforementioned disadvantages and extends the conventional PID algorithm from one-dimension to two-dimensions. Second, the tuning guidelines of the presented 2D-PID controller are obtained from the two-dimensional model predictive control iterative control (2D-MPILC) method. Thus, the proposed approach inherits the advantages of both PID control, PIDILC strategy, and 2D-MPILC scheme. The superiority of the proposed method is verified by the case study on the injection modelling process.     

Deterministic vs. stochastic performance assessment of iterative learning control for batch processes

The performance assessment of linear time‐invariant batch processes when iterative learning control (ILC) is implemented has been discussed. Previous literatures show that conventional performance assessment cannot be directly applied to batch processes due to the nature of batch operations. Chen and Kong have suggested a new method to assess the control performance of batch processes using optimal ILC as the benchmark. In their work, ILC controllers are assumed to affect either stochastic or deterministic performance but without considering their interaction. This work elaborates the controllers effects on both stochastic and deterministic control performance of batch processes. It is shown that the optimal solution based on the minimum variance control law has a trade‐off between deterministic and stochastic performance, which can be shown by a trade‐off curve. Furthermore, a method is proposed to estimate this curve from routine operating data, against which the performance of ILC controllers can be assessed. Simulation studies are conducted to verify the proposed method. © 2012 American Institute of Chemical Engineers AIChE J, 59: 457–464, 2013     

时变间歇过程的2D-PID自适应控制方法

针对间歇过程存在的参数时变问题,提出一种基于二维PID(2D-PID)迭代学习框架的自适应控制方法。首先,通过粒子群优化算法快速获取初始的2D-PID控制参数。在批次内,采用自调整神经元PID控制器对其进行在线自适应调节。进一步,考虑批次间的重复特性,通过PID型迭代学习控制,以利用历史批次的信息来修正当前批次的调节变量,最终提高控制性能。通过间歇发酵过程的仿真和比较研究,验证了所提出方法的有效性。     

Robustness of reinforced gradient-type iterative learning control for batch processes with Gaussian noise☆

In this paper, a reinforced gradient-type iterative learning control profile is proposed by making use of system matrices and a proper learning step to improve the tracking performance of batch processes disturbed by exter-nal Gaussian white noise. The robustness is analyzed and the range of the step is specified by means of statistical technique and matrix theory. Compared with the conventional one, the proposed algorithm is more efficient to resist external noise. Numerical simulations of an injection molding process il ustrate that the proposed scheme is feasible and effective.     

基于Gath-Geva算法和核极限学习机的多阶段间歇过程软测量

间歇过程具有较强的非线性,多阶段、慢时变及批次间存在变化,采用单一预测模型不能反映间歇过程的多阶段特性及阶段间过渡特性。提出一种基于Gath-Geva聚类和核极限学习机（kernel extreme learning machine,KELM）的多模型软测量方法。首先采用主成分分析（principal component analysis,PCA）对输入做特征提取,然后利用Gath-Geva算法对间歇过程进行多阶段工况划分,根据生产工况特性划分为不同的操作阶段后,分别建立局部KELM模型。对任一待预测样本,分别计算其对应各局部模型的预测值,最后采用贝叶斯集成,将其隶属于各局部模型的模糊隶属度作为权重和预测值融合得到最终预测值。以青霉素发酵数据进行实验测试,结果表明所提多模型算法相较于单一模型,具有更高的预测精度。