新一代的自适应模型预测控制器

提出了新一代的自适应模型预测控制器,自适应MPC控制器由MPC控制模块、在线辨识模块、性能监控模块3个模块组成,相互协调配和来实现自适应MPC控制。除了控制器功能设计以外,其余过程均可自动进行。对于新建MPC应用,首先进行多变量测试与辨识,在模型符合控制要求时,自动进入控制器投运。通过控制器性能监视发现模型不满足控制要求精度时,触发一次多变量模型测试与辨识过程,替换原有模型进行控制,保证控制器性能始终处于最佳状态。自适应MPC控制器在PTA装置上的应用表明了算法的有效性。     

基于偏相关性分析的MPC控制器模型失配检测

在实际中,模型失配是导致模型预测控制性能下降的关键因素。传统的相关性分析方法在分析某一通道的预测残差和操作变量之间的相关性时,会受到其他操作变量及干扰的影响,导致结果不可靠,无法准确定位失配。针对上述问题,现采用偏相关性分析方法。在滤除其他操作变量和干扰影响的前提下,分析各通道预测残差和操作变量之间的相关性,将模型失配问题转化为一定区间上偏相关系数的分布问题。通过观察各通道偏相关系数的分布图判断是否发生失配,并通过Shell塔实验验证该方法的有效性。     

基于模型预测残差和目标函数的MPC实时性能监控

模型预测控制的性能受多种因素的影响,现有的模型质量评价指标没有考虑外界干扰的变化,反映系统整体性能时不够全面。针对上述问题,现结合两个指标:基于模型预测控制目标函数的历史性能指标和基于模型预测残差的协方差指标对系统性能进行实时监控。其中,历史性能指标用以评价系统的整体性能,协方差指标反映模型失配和干扰变化的影响。根据两个指标对不同性能影响因素的不同表现和性能恶化后对干扰新息的重新辨识结果,对系统性能下降的原因进行初步诊断,缩小性能下降源的范围,并通过Wood-berry塔实验验证了该方法的有效性。     

鲁棒模型预测控制系统的评估基准

在控制系统的性能评估中,基准的设计是个重要问题。将基本设计极限理论推广到模型预测控制系统(MPC),建立性能评估基准。直接考虑多输入多输出系统的频域扰动,建立输出反馈鲁棒模型预测控制器。此控制器仅仅依赖于过程参数,也是令闭环系统达到控制性能极限的基准控制器。建立了用于评估的性能指标,提出基于此基准的性能评估程序,用以评价其他模型预测控制系统的性能。数学算例证实了这一评估程序的有效性。     

基于加权L2-Hausdorff子空间距离的MPC控制回路性能诊断

针对当前MPC控制回路性能评价方法无法准确定位性能下降源的问题,提出一种基于加权L₂-Hausdorff子空间距离的控制回路性能诊断方法.该方法引用恶化性能子空间表征各个性能恶化工况下的回路特征,通过基于模型预测残差的闭环潜能指标实时监测控制回路性能.当发现性能下降时,构造加权L₂-Hausdorff子空间距离来度量当前回路性能模式与已知性能模式的相似度,通过距离聚类来定位回路性能下降的恶化源.最后,通过连续搅拌加热器(continuous stirred tank heater, CSTH)上的仿真实验,验证了所提方法的有效性及可靠性.     

复杂化工过程失配子模型深度诊断与修正算法

针对复杂化工生产过程中多变量预测控制系统发生模型失配导致控制性能下降造成的产品质量波动的问题，研究了过程失配子模型深度诊断与模型修正方法。考虑到复杂化工生产过程中被控变量为控制通道和扰动通道的综合响应，通过逐次移动操作变量到扰动通道的方法，评价移动后对模型质量指标的影响，从而判断出所移出子模型的性能，进而对失配子模型进行定位。进一步地，利用现场采集的历史数据用自回归滑动平均模型辨识法辨识出失配部分的模型，用于对原有子模型进行修正。实验采用Wood-Berry精馏过程对其进行动态仿真验证，结果证明了该算法的有效性。     

基于扰动观测器的模型预测控制在磨矿分级过程中的应用

磨矿分级过程（GCP）是冶金选矿行业的关键流程,其产品粒度指标必须严格控制,以保证精矿产品品位和金属回收率。GCP本质上是一个多变量强耦合过程,具有时滞和逆向特性,且存在强扰动。扰动的存在造成系统控制性能变差,甚至不稳定。以两输入两输出GCP为研究对象,提出了一种基于扰动观测器（DOB）的模型预测控制（MPC）复合控制方案DOB-MPC。仿真研究表明DOB-MPC不仅可以有效抑制GCP的外部扰动,而且可以抑制由模型失配和变量之间的耦合而导致的内部扰动;在获得良好的解耦控制能力的同时,取得了满意的抗扰动性能。     

模型预测控制系统的性能评价研究

以单容水箱、精馏塔为仿真对象,以模型预测控制作为实现过程,通过Matlab仿真方式,利用最小方差法进一步提高单容水箱单变量模型的预测控制性能;对精馏塔模型多变量系统,利用历史性能指标和设计性能指标,以残差的累积和图为手段进行模型预测控制性能监视,最后验证了所提方法的有效性。     

基于MPC控制技术优化VPSA制氧工艺的模拟

针对真空变压吸附制氧在gPROMS软件中建立了严格的数学模型，基于LiLSX吸附剂设计了两塔八步的真空变压吸附流程生产纯度为92%的O ₂。对此流程进行优化，其纯度和回收率有了明显的改进。在此基础上，引入实际生产中经常存在的如进料流量的变化以及吸附性能降低等扰动因素，使模拟工作更接近实际。根据产品气中O ₂纯度的反馈，采用模型辨识技术设计了MPC控制器，用于预测控制VPSA过程的动态行为。开环和闭环控制结果的对比显示，流程在设计的MPC控制下展现出更好的结果，这表明MPC控制策略可以明显改善空气分离制氧的生产过程。     

一种扰动自适应的鲁棒预测控制算法

针对实际生产中扰动的时变性,提出了一种扰动自适应的鲁棒预测控制（RAMPC）算法以提高扰动抑制性能。采用时间序列（ARMA）模型在线辨识系统的不可测扰动,通过基于多次迭代思想的递推辨识算法（multi-iteration pseudo-linear regression,MIPLR）来保证在线辨识的质量和收敛速度。考虑到数据与辨识模型的不确定性,改用min-max形式描述MPC算法的控制作用优化命题,并将在线辨识过程中的误差数据引入min-max命题,使在线辨识与控制作用鲁棒优化求解紧密结合起来,提高算法鲁棒性。进一步将此min-max问题转换为一个等效的非线性min问题,并采用多步线性化方法实现快速求解,解决了传统min-max方法在线计算负荷高的问题。仿真结果表明了该算法的有效性。     

Monitoring and retuning of low-level PID control loops

In this work, we focus on the problem of monitoring and retuning of low-level proportional-integral-derivative (PID) control loops used to regulate control actuators to the values computed by advanced model-based control systems like model predictive control (MPC). We consider the case where the real-time measurement of the actuation level is unavailable, and thus PID controller monitoring has to be achieved on the basis of process state measurements. A fault detection and isolation (FDI) method involving process models and real-time process measurements is used to monitor the PID control loops and compute appropriate residuals. Once poor tuning is detected and isolated, a PID tuning method based on the estimated transfer function of the control actuator is applied to the isolated, poorly functioning PID controller. An example of a non-linear reactor–separator process operating under MPC with low-level PID controllers regulating the control actuators is used to demonstrate the approach.     

基于加权偏离度统计方法的预测控制性能评估算法

针对带区域约束条件的预测控制系统性能评估问题,在考虑过程输出变量约束类型的基础上,提出了基于加权偏离度统计方法的控制性能评估算法。该方法依据控制要求的不同,将输出变量分为质量变量和约束变量,并结合工程经验合理选择变量的权重。基于系统闭环运行数据和约束设置,通过计算变量的加权偏离度得到控制系统的性能评估指标,从而为预测控制器的参数调整和性能提升提供了决策依据。系统仿真实例和工程应用证明了该评估算法对区域预测控制系统性能评估的有效性。     

Performance assessment of cascade controllers for nitrate control in a wastewater treatment process

A cascade control strategy is proposed to the benchmark simulation model 1 (BSM1) to enhance the treatment performance of nitrogen removal in a biological wastewater treatment plant. The proposed control approach consists of two control loops, a primary outer loop and a secondary inner loop. The method has two controllers of which the primary loop has a model predictive control (MPC) controller and the secondary loop has a proportional-integralderivative (PID) controller, which is a cascade MPC-PID controller. The primary MPC controller is to control the nitrate concentration in the effluent, and the secondary PID controller is to control the nitrate concentration in the final anoxic compartment. The proposed method controls the nitrate concentrations in the effluent as well as in the final anoxic reactor simultaneously to strictly satisfy the quality of the effluent as well as to remove the effects of disturbances more quickly by manipulating the external carbon dosage rate. Because the control performance assessment (CPA) technique has the features of determining the capability of the current controller and locating the best achievable performance, the other novelty of this paper is to suggest a relative closed-loop potential index (RCPI) which updates the CPA technology into a closed-loop cascade controller. The proposed method is compared with a cascade PID-PID control strategy and the original PID controller in BSM1 and an improved performance of the suggested cascade MPC-PID controller is obtained by using the CPA approach.     

Actuator stiction compensation via model predictive control for nonlinear processes

The problem of valve stiction is addressed, which is a nonlinear friction phenomenon that causes poor performance of control loops in the process industries. A model predictive control (MPC) stiction compensation formulation is developed including detailed dynamics for a sticky valve and additional constraints on the input rate of change and actuation magnitude to reduce control loop performance degradation and to prevent the MPC from requesting physically unrealistic control actions due to stiction. Although developed with a focus on stiction, the MPC‐based compensation method presented is general and has potential to compensate for other nonlinear valve dynamics which have some similarities to those caused by stiction. Feasibility and closed‐loop stability of the proposed MPC formulation are proven for a sufficiently small sampling period when Lyapunov‐based constraints are incorporated. Using a chemical process example with an economic model predictive controller (EMPC), the selection of appropriate constraints for the proposed method is demonstrated. The example verified the incorporation of the stiction dynamics and actuation magnitude constraints in the EMPC causes it to select set‐points that the valve output can reach and causes the operating constraints to be met. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2004–2023, 2016     

Development of a novel control strategy for a highly segmented injection mold tempering for inline part warpage control

The demand of precise injection-molded parts is steadily increasing and is today one of the most relevant challenges, due to local variations in temperature and pressure during the production of the part. These variations can lead to a significant change of the local specific volume, shrinkage potential, and inner stress, which ultimately results in part warpage. By homogenizing the local specific volume over the part according to the specific pvT-behavior of the polymer, warpage is expected to be reduced. The following work describes a new approach to control the local specific volume by a newly developed segmented and highly dynamic mold temperature control based on rapid heating ceramics and CO₂ evaporation chambers. Since injection molding is a dynamic process and heat transfer inside the mold is comparably slow, a special control strategy is necessary to activate the heating and cooling elements in advance. For this, a novel prediction strategy based on a discretization of the one-dimensional heat equation has been developed. Experimental trials including a classical PID controller and a model predictive control approach (MPC) show that the MPC is superior regarding the process stability.     

Modeling and predictive control of nonlinear processes using transfer learning method

This work develops a transfer learning (TL) framework for modeling and predictive control of nonlinear systems using recurrent neural networks (RNNs) with the knowledge obtained in modeling one process transferred to another. Specifically, transfer learning uses a pretrained model developed based on a source domain as the starting point, and adapts the model to a target process with similar configurations. The generalization error for TL-based RNN (TL-RNN) is first derived to demonstrate the generalization capability on the target process. The theoretical error bound that depends on model capacity and the discrepancy between source and target domains is then utilized to guide the development of pretrained models for improved model transferability. Subsequently, the TL-RNN model is utilized as the prediction model in model predictive controller (MPC) for the target process. Finally, the simulation study of chemical reactors via Aspen Plus Dynamics is used to demonstrate the benefits of transfer learning.     

A test for improved multi-step forecasting

Abstract.  We propose a general test of whether a time-series model, with parameters estimated by minimizing the single-step forecast error sum of squares, is robust with respect to multi-step prediction, for some specified lead time. The test may be applied to a, possibly seasonal, autoregressive integrated moving average (ARIMA) model using the parameters and residuals following maximum likelihood estimation. It is based on a score statistic, evaluated at these estimated parameters, which measures the sensitivity of the multi-step forecast error variance with respect to the parameters. We derive the large sample properties of the test and show by a simulation study that it has acceptable small sample size properties for higher lead times when applied to the integrated moving average or IMA model that gives rise to the exponentially weighted moving average predictor. We investigate the power of the test when the IMA(1,1) model has been fitted to an ARMA(1,1) process. Further, we demonstrate the high power of the test when an AR is fitted to a process generated as the sum of a stochastic trend and cycle plus noise. We use frequency domain methods for the derivation and sampling properties of the test, and to give insight into its application. The test is illustrated on two real series, and an R function for its general application is available from http://msor.victoria.ac.nz/Main/JohnHaywood .