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。     

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。     

基于改进型GPC算法的液位优化控制

以双容水箱为被控对象,利用MATLAB/Simulink程序开发工具,对广义预测控制(GPC)进行仿真和实验研究。首先建立一个控制系统的数学模型,然后对GPC算法进行选择,最后对应用广义预测控制算法的模型进行了仿真验证。实验结果表明:所提出的算法具有良好的动态响应性能,可快速跟踪设定值,得到了较好的控制效果。     

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.     

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。     

新型模糊预测PID控制在pH中和过程中的应用

利用自适应学习算法及模糊推理方法在线修正pH过程所得的局部线性化模型，同时基于广义预测控制(GPC)的思想和离散PID算法的相互关系，提出了一种以预测控制这类先进控制方法为思想，以经典PID控制为实现的新型控制器。其中，控制器的参数通过GPC与PID的相互关系递推计算得到。仿真研究表明本文所提出方法的可行性和有效性。     

混炼胶质量预测和排胶控制的GMPLS模型研究

针对混炼胶密炼工艺特性，提出广义多路部分最小二乘（GMPLs）算法，建立混炼胶门尼粘度预测和排胶控制模型，克服了普通最小二乘算法在间歇生产质量控制建模中的局限性。试验结果表明，该模型预测混炼胶质量效果良好，并可根据工艺要求在线调整排胶工艺参数，实现智能排胶控制，提高混炼胶质量。     

模糊广义预测控制在水泥分解炉温度控制中的应用

本文提出了一种模糊广义预测控制算法(GPC),用于解决水泥厂煤炭热值的变化对模型精确性的不利影响。采用该算法能够大幅提高分解炉温度控制系统模型的精确性,获得更加稳定的控制效果。该算法已在实际生产线上投入使用,控制效果非常出色,证明了该算法的有效性。     

广义预测控制及其在蒸馏塔温度控制中的应用

随着广义预测控制(GPC)在工业过程控制中的广泛应用,GPC的研究已成为当前自动控制工程界的研究热点.阐述了GPC的各种算法和应用研究状况,并对GPC在减压塔原油蒸馏装置温度控制中的应用进行了讨论.     

一类间歇生产过程的迭代学习控制算法及其收敛性分析

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,并且考虑到实际生产中存在外部干扰和不确定因素的影响,本文对间歇过程模型参数动态更新问题进行了分析,建立了间歇生产过程产品质量的神经模糊(NF)预测模型,提出了一种新颖的批次轴参数自适应调节算法。在此基础上,构造了一种基于数据驱动的间歇生产过程产品质量迭代学习控制算法,并对优化问题的收敛性给出了严格的数学证明。最后,将本文提出的算法用于一类典型的间歇过程终点质量控制研究,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。     

An LMI Method to Robust Iterative Learning Fault-tolerant Guaranteed Cost Control for Batch Processes

Based on an equivalent two-dimensional Fornasini-Marchsini model for a batch process in industry, a closed-loop robust iterative learning fault-tolerant guaranteed cost control scheme is proposed for batch processes with actuator failures. This paper introduces relevant concepts of the fault-tolerant guaranteed cost control and formulates the robust iterative learning reliable guaranteed cost controller （ILRGCC）. A significant advantage is that the proposed ILRGCC design method can be used for on-line optimization against batch-to-batch process uncertainties to realize robust tracking of set-point trajectory in time and batch-to-batch sequences. For the convenience of implementation, only measured output errors of current and previous cycles are used to design a synthetic controller for iterative learning control, consisting of dynamic output feedback plus feed-forward control. The proposed controller can not only guarantee the closed-loop convergency along time and cycle sequences but also satisfy the H∞ performance level and a cost function with upper bounds for all admissible uncertainties and any actuator failures. Sufficient conditions for the controller solution are derived in terms of linear matrix inequalities （LMIs）, and design procedures, which formulate a convex optimization problem with LMI constraints, are presented. An example of injection molding is given to illustrate the effectiveness and advantages of the ILRGCC design approach.     

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.     

Iterative learning controller synthesis using FIR models for batch processes

Adaptive iterative learning control based on the measured input-output data is proposed to solve the traditional iterative learning control problem in the batch process. It produces a control law with self-tuning capability by combining a batch-to-batch model estimation procedure with the control design technique. To build the unknown batch operation system, the finite impulse response (FIR) model with the lifted system is constructed for easy construction of a recursive least squares algorithm. It can identify the pattern of the current operation batch. The proposed model reference control method is applied to feedback control of the lifted system. It finds an appropriate control input so that the desired performance of the batch output can track the prescribed finite-time trajectory by iterative trials. Furthermore, on-line tracking control is developed to explore the possible adjustments of the future input trajectories within a batch. This can remove the disturbances in the current batch rather than the next batch trial and keep the product specifications consistent at the end of each batch. To validate the theoretical findings of the proposed strategies, two simulation problems are investigated.     

Batch-to-batch optimal control of a batch polymerisation process based on stacked neural network models

A neural network based batch-to-batch optimal control strategy is proposed in this paper. In order to overcome the difficulty in developing mechanistic models for batch processes, stacked neural network models are developed from process operational data. Stacked neural networks have enhanced model generalisation capability and can also provide model prediction confidence bounds. However, the optimal control policy calculated based on a neural network model may not be optimal when applied to the true process due to model plant mismatches and the presence of unknown disturbances. Due to the repetitive nature of batch processes, it is possible to improve the operation of the next batch using the information of the current and previous batch runs. A batch-to-batch optimal control strategy based on the linearisation of stacked neural network model is proposed in this paper. Applications to a simulated batch polymerisation reactor demonstrate that the proposed method can improve process performance from batch to batch in the presence of model plant mismatches and unknown disturbances.     

Iterative learning control for the systematic design of supersaturation controlled batch cooling crystallisation processes

The paper presents an approach to improve the product quality from batch-to-batch by exploiting the repetitive nature of batch processes to update the operating trajectories using process knowledge obtained from previous runs. The data based methodology is focused on using the linear time varying (LTV) perturbation model in an iterative learning control (ILC) framework to provide a convergent batch-to-batch improvement of the process performance indicator. The major contribution of this work is the development of a novel hierarchical ILC (HILC) scheme for systematic design of the supersaturation controller (SSC) of seeded batch cooling crystallizers. The HILC is used to determine the required supersaturation setpoint for the SSC and the corresponding temperature trajectory required to produce crystals with desired end-point property. The performance and robustness of these approaches are evaluated through simulation case studies. These results demonstrate the potential of the ILC approaches for controlling batch processes without rigorous process models.     

基于MKPLS和SQP方法的间歇过程迭代优化控制

采用多向核偏最小二乘（MKPLS）方法建立间歇过程的模型并进行操作条件的优化。由于存在模型失配和未知扰动，基于MKPLS模型的最优控制轨迹在实际对象上往往难以实现最优的产品质量指标。本文利用间歇过程批次间的重复特性与序贯二次规划（SQP）优化算法中迭代计算的相似特点，提出了一种基于MKPLS模型的批次间优化调整策略，使得经过逐步优化调整得到的控制轨迹作用于实际对象时，可以得到更优的质量指标。该方法的有效性在苯乙烯聚合反应器和乙醇流加发酵过程的仿真对象上得到了验证。     

一类连续/间歇过程的迭代学习模型预测控制

An iterative learning model predictive control (ILMPC) technique is applied to a class of continuous/batch processes. Such processes are characterized by the operations of batch processes generating periodic strong disturbances to the continuous processes and traditional regulatory controllers are unable to eliminate these periodic disturbances. ILMPC integrates the feature of iterative learning control (ILC) handling repetitive signal and the flexibility of model predictive control (MPC). By on-line monitoring the operation status of batch processes, an event-driven iterative learning algorithm for batch repetitive disturbances is initiated and the soft constraints are adjusted timely as the feasible region is away from the desired operating zone. The results of an industrial appli-cation show that the proposed ILMPC method is effective for a class of continuous/batch processes.     

间歇过程的批间自优化控制

针对间歇过程的实时优化问题,提出了一种基于最优性条件近似法的批间自优化控制策略。首先获取标称工作点的最优输入轨迹形态,将其参数化为少量决策变量,简化问题复杂度。然后根据参数化后的决策变量得到批间优化的最优性条件,并建立批次终端可测变量和最优性条件之间的回归模型,将其作为被控变量进行批间跟踪控制。对一个间歇反应器进行了仿真研究,结果表明方法能有效实现间歇过程的批间自优化控制。     

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Based on the two-dimensional (2D) systemtheory, an integrated predictive iterative learning control (2D-IPILC) strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control (ILC) andmodel predictive control(MPC) is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By minimizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type (Ptype) ILC despite the model error and disturbances.