Journal of Process Control最新文献

英文中文

Real-time physical activity detection module during sensor augmented insulin pump therapy 传感器增强胰岛素泵治疗过程中的实时身体活动检测模块

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2026-01-01 DOI: 10.1016/j.jprocont.2025.103608

Eleonora Manzoni , Emilia Fushimi , Eleonora M. Aiello , Zoey Li , Robin Gal , Corby K. Martin , Susana R. Patton , Simone Del Favero , Francis J. Doyle III

Individuals living with type 1 diabetes (T1D) face important challenges when engaging in physical activity (PA), as it necessitates careful management of blood glucose levels often through insulin adjustments and carbohydrate intake. Integrating PA detection into sensor augmented insulin pumps (SAP) is a promising strategy to enhance glycemic control by suggesting basal insulin reduction or carbohydrate ingestion in the critical 24 h after the PA detection.

We have developed a real-time, model-based module for PA detection based solely on measured glucose levels, insulin infusion rates, and carbohydrate intake. The approach is based on the monitoring of the magnitude as well as various statistical properties of the prediction residuals, i.e., the discrepancies between actual sensor-measured glucose levels and model-predicted levels.

We tested our algorithm on the Type 1 Diabetes and Exercise Initiative (T1DEXI) dataset, which includes structured sessions of aerobic, resistance, and interval exercises. In a dataset containing all three activity types, the detection approach based on the median of the prediction residuals successfully detected an average of 59% of PA instances, while keeping the false alarms to 3.5 per considered timeframe, when considering models tailored to each participant. When using a population model identified on in-silico data from the UVa/Padova T1D simulator, the approach successfully detected 62% of PAs, while keeping the false alarms to 3.6 per considered timeframe.

These encouraging findings open the possibility of integrating PA detection into SAP systems without the need for additional physiological signals, thus enabling improved glucose management.

1型糖尿病（T1D）患者在进行体育活动（PA）时面临着重要的挑战，因为它需要经常通过胰岛素调节和碳水化合物摄入来仔细管理血糖水平。将PA检测整合到传感器增强胰岛素泵（SAP）中是一种很有前景的策略，通过提示在PA检测后关键的24小时内基础胰岛素减少或碳水化合物摄入来加强血糖控制。我们开发了一个实时的、基于模型的模块，用于仅根据测量的葡萄糖水平、胰岛素输注率和碳水化合物摄入量检测PA。该方法基于对预测残差的大小和各种统计特性的监测，即传感器实际测量的葡萄糖水平与模型预测的水平之间的差异。我们在1型糖尿病和运动倡议（T1DEXI）数据集上测试了我们的算法，该数据集包括有氧、阻力和间歇运动的结构化会话。在包含所有三种活动类型的数据集中，基于预测残差中位数的检测方法成功地检测了平均59%的PA实例，同时在考虑为每个参与者定制的模型时，将每个考虑的时间框架的假警报保持在3.5。当使用来自UVa/Padova T1D模拟器的计算机数据识别的种群模型时，该方法成功检测到62%的pa，同时将每个考虑的时间范围内的假警报保持在3.6。这些令人鼓舞的发现开启了将PA检测整合到SAP系统的可能性，而不需要额外的生理信号，从而改善葡萄糖管理。

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引用次数: 0

Robust soft sensing with causal and injectivity-preserving Graph Neural Network 基于因果保注入图神经网络的鲁棒软检测

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-12-27 DOI: 10.1016/j.jprocont.2025.103614

Warren Acheampong , Om Prakash , Biao Huang

Graph Neural Networks (GNNs) excel in soft sensing by effectively modeling complex interdependencies among process variables. This study presents a graph-based framework for improved process quality prediction in nonlinear, dynamic industrial systems. We address two key challenges in chemical process soft sensing: (i) unknown graphs where the structure is not available a priori, and (ii) injectivity issues from scalar features. To resolve non-injective aggregation, where distinct neighborhoods become indistinguishable, we expand the input domain to preserve structural uniqueness in both undirected and directed graphs. We also propose a method for learning directed graphs using Sparse Debiased Dynamic Mode Decomposition, which captures temporal dynamics and produces sparse, interpretable, and noise-resilient representations. An end-to-end framework jointly learns the graph structure and GNN parameters, allowing the graph to adapt during training based on the prediction task. The proposed methods are validated through simulations under varying noise levels and a benchmark case study involving a Sulfur Recovery Unit, demonstrating strong robustness and predictive performance.

图神经网络（gnn）通过有效地模拟过程变量之间复杂的相互依赖关系，在软测量方面表现出色。本研究提出了一个基于图的框架，用于改进非线性动态工业系统的过程质量预测。我们解决了化学过程软测量中的两个关键挑战：(i)结构不可先验的未知图，以及（ii）标量特征的注入性问题。为了解决非内射聚集问题，我们扩展了输入域以保持无向图和有向图的结构唯一性。我们还提出了一种使用稀疏去偏动态模式分解学习有向图的方法，该方法捕获时间动态并产生稀疏、可解释和抗噪声的表示。端到端框架共同学习图结构和GNN参数，允许图在训练过程中根据预测任务进行适应。通过不同噪声水平下的模拟和涉及硫回收装置的基准案例研究，验证了所提出的方法的鲁棒性和预测性能。

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引用次数: 0

Distributed set-membership estimation for Markov jump linear systems with uncertain transition probabilities 具有不确定转移概率的马尔可夫跳跃线性系统的分布集隶属度估计

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-12-24 DOI: 10.1016/j.jprocont.2025.103613

Hui Zhang , Wangyan Li , Jie Bao , Fei Liu

This paper investigates distributed set-membership estimation (DSME) for Markov linear jump systems (MJLSs) with uncertain transition probabilities. To effectively leverage the information of mode probability within the framework of DSME, the MJLS is transformed into a parameter uncertainty system for each mode by using the indicator function method. An outer bounding ellipsoid method is developed to cover the union, transforming the multiplicative uncertainties from transition probability uncertainties and the state estimation set (SES) into additive ellipsoids. A novel consensus-based distributed set-membership estimator is proposed, incorporating the mode and node information. Furthermore, a sufficient condition for the existence of the SES is developed. The SES for each mode is optimized by minimizing the trace of the shape matrix, from which the overall SES is derived (as their Minkowski sum). A wastewater treatment process example is presented to illustrate the effectiveness of the proposed method.

研究了具有不确定转移概率的马尔可夫线性跳跃系统的分布集隶属度估计。为了有效利用DSME框架内的模态概率信息，采用指标函数法将MJLS转化为各模态的参数不确定性系统。提出了一种覆盖并集的外边界椭球方法，将乘性不确定性从转移概率不确定性和状态估计集（SES）转化为可加性椭球。提出了一种新的基于共识的分布式集隶属度估计方法，该方法结合了模态和节点信息。进一步给出了系统存在的充分条件。通过最小化形状矩阵的轨迹来优化每种模式的SES，并从中导出总体SES（作为它们的闵可夫斯基和）。最后通过一个污水处理实例说明了该方法的有效性。

引用次数: 0

Observer-based diagnosis and predictive framework for sensor-fault tolerant control of process systems 过程系统传感器容错控制的观测器诊断与预测框架

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-12-19 DOI: 10.1016/j.jprocont.2025.103610

Ritu Ranjan, Costas Kravaris

Sensors are ubiquitous in modern industrial systems, and are prone to faults due to harsh condition in which they are placed. Sensor faults can impact the product quality and operational safety as control loops heavily depends on the accuracy of sensor measurement feedback. In this paper, we propose active sensor-fault tolerant control (FTC) strategies that can take proactive measures during faults involving timely correction of faulty measurements, ensuring the system remains within predefined safety and quality constraints. The concept of maximal output admissible set is leveraged to determine acceptable operating set (AOS) which is the set of initial process states that meet safety and quality constraints at all times. To support decision-making, we introduce a novel critical fault function (CFF) that quantifies the fault size and time available before the system exits the AOS if no corrective action is taken. While the AOS and CFF are computed offline, the CFF is implemented online with real-time fault estimates to trigger measurement correction in time. A linear functional observer and a nonlinear state observer, combined with a predictive scheme is proposed to estimate fault size and enhance robustness during transient phase of observers. Alternatively, a bank of state observers is used for fault detection and isolation and subsequently the state observer estimator based on healthy sensors are utilized for state feedback in control loops. The proposed sensor FTC strategy is tested on an exothermic Continuous Stirred Tank Reactor (CSTR) as a case study. The results demonstrate the strategy's effectiveness in handling sensor faults, ensuring both quality and safety constraints are met. Thus, this paper contributes to the advancement of practical active sensor FTC ensuring the resilience of industrial systems.

传感器在现代工业系统中无处不在，由于它们所处的恶劣条件，容易出现故障。传感器故障会影响产品质量和运行安全，因为控制回路严重依赖于传感器测量反馈的准确性。在本文中，我们提出了主动传感器容错控制（FTC）策略，该策略可以在故障期间采取主动措施，包括及时纠正故障测量，确保系统保持在预定义的安全和质量约束范围内。利用最大输出允许集的概念来确定可接受操作集，即在任何时候满足安全和质量约束的初始过程状态的集合。为了支持决策，我们引入了一种新的临界故障函数（CFF），该函数量化了在不采取纠正措施的情况下系统退出AOS之前的故障大小和可用时间。AOS和CFF是离线计算的，而CFF是在线实现的，带有实时故障估计，可以及时触发测量校正。提出了一种线性泛函观测器和非线性状态观测器，并结合预测方案来估计故障大小，增强观测器在暂态阶段的鲁棒性。或者，使用一组状态观测器进行故障检测和隔离，然后利用基于健康传感器的状态观测器估计器在控制回路中进行状态反馈。以放热连续搅拌槽式反应器（CSTR）为例，对所提出的传感器FTC策略进行了测试。结果表明，该策略在处理传感器故障时是有效的，同时保证了质量和安全约束。因此，本文有助于推进实用的主动传感器FTC，以确保工业系统的弹性。

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引用次数: 0

Prescribed performance based direct data-driven model predictive control for continuous stirred tank reactor 基于预定性能的连续搅拌槽式反应器直接数据驱动模型预测控制

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-12-19 DOI: 10.1016/j.jprocont.2025.103609

Chengyu Zhou , Li Jia , Jianfang Li

Continuous stirred tank reactor (CSTR) is the most important and widely used reaction equipment in the process industry. The use of an indirect data-driven model predictive control (MPC) plays an important role in controlling the key variable in the CSTR system. However, because of the complex nonlinear dynamics in the reaction process, the existing indirect data-driven MPC strategies are always unable to avoid the problem of unmodeled dynamics, resulting in the inability to ensure the control performance of the system. To this end, this paper designs a new direct data-driven model predictive control (DDMPC) method for the CSTR system under the prescribed performance control (PPC) framework. Using dynamic linearization technology, a converted-output-based equivalent data-driven prediction model in the input–output sense to the original CSTR system is first established to deal with the unknown system dynamics under performance constraints. Then, a prescribed performance function and the converted-output-based data-driven prediction model are directly incorporated into the criterion function to derive the constraint MPC control scheme, which achieves the prescribed performance requirements of the system. Furthermore, the stability of the tracking error and the bounded-input-bounded-output (BIBO) are rigorously proved based on the contractive mapping principle. As a result, the resulting DDMPC control scheme only requires the input and output data of the controlled CSTR system without any explicit model information. In the end, the effectiveness and superiority of the developed control method are demonstrated by a nonlinear CSTR system.

连续搅拌槽式反应器（CSTR）是过程工业中最重要、应用最广泛的反应设备。间接数据驱动模型预测控制（MPC）在CSTR系统的关键变量控制中起着重要的作用。然而，由于反应过程中存在复杂的非线性动力学，现有的间接数据驱动MPC策略总是无法避免未建模的动力学问题，导致无法保证系统的控制性能。为此，本文设计了一种在规定性能控制（PPC）框架下的CSTR系统直接数据驱动模型预测控制（DDMPC）方法。利用动态线性化技术，首先建立了基于转换输出的原CSTR系统输入输出意义上的等效数据驱动预测模型，以处理性能约束下未知的系统动态。然后，将规定的性能函数和基于转换输出的数据驱动预测模型直接纳入准则函数，推导出约束MPC控制方案，使系统达到规定的性能要求。此外，基于压缩映射原理，严格证明了跟踪误差和有界输入-有界输出（BIBO）的稳定性。因此，得到的DDMPC控制方案只需要被控CSTR系统的输入和输出数据，不需要任何显式的模型信息。最后，通过一个非线性CSTR系统验证了所提控制方法的有效性和优越性。

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引用次数: 0

Residual-based fault detection and isolation in control environment agriculture 基于残差的控制环境农业故障检测与隔离

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-12-13 DOI: 10.1016/j.jprocont.2025.103607

Lukas Munser , Ángeles Hoyo , Felix Petzke , Jaime A. Moreno , Stefan Streif

The detection and isolation of various faults in controlled environment agriculture is a notoriously complicated task, since various biological, sensory, and mechanical phenomena may interact with each other. In the present work, a residual-based approach is presented which enables the detection, isolation and quantification of different types of faults. For this purpose, observers are designed that can approximate the residuals despite model inaccuracies and measurement noise. The approach is demonstrated through experiments in a small-scale vertical farming unit whereby it is possible to distinguish between different fault types during operation.

由于各种生物、感官和机械现象可能相互作用，因此在受控环境农业中检测和隔离各种故障是一项非常复杂的任务。在本工作中，提出了一种基于残差的方法，可以检测、隔离和量化不同类型的故障。为此，设计了可以在模型不准确和测量噪声的情况下近似残差的观测器。该方法通过在小型垂直农业单位的实验证明，在操作过程中可以区分不同的故障类型。

引用次数: 0

Multi-stage Economic Nonlinear Model Predictive Control of bioreactors using dynamic flux balance analysis models 基于动态通量平衡分析模型的生物反应器多阶段经济非线性模型预测控制

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-12-11 DOI: 10.1016/j.jprocont.2025.103595

Rafael D. de Oliveira, Johannes Jäschke

Dynamic Flux Balance Analysis (dFBA) models are powerful metabolic models that have a large potential for application in bioprocess control and optimisation. However, dFBA has an embedded linear FBA optimisation problem with degenerate solutions that give rise to multiple possible state trajectories that all satisfy the model. To address this uncertainty, we propose using a robust control approach based on Multi-stage Economic Nonlinear Model Predictive Control, which allows handling the degenerate solutions of the FBA problem without being too conservative. We propose to add a regularisation term to the FBA problem, to ensure a unique solution and generate the uncertainty scenarios by varying the regularization weights. The scenarios generated in that way then correspond to different solutions of the FBA problem. Then, the KKT conditions of the regularised FBA problem are imposed as equality constraints on the optimal control problem, which is solved using a direct collocation approach. Our methodology is evaluated through a case study on the optimal control of a fed-batch bioreactor for Escherichia coli growth, subject to a constraint on acetate concentration. The results demonstrate that the proposed MS-ENMPC approach, combined with the dFBA model, effectively satisfies the constraints despite uncertainties in the system trajectories.

动态通量平衡分析（dFBA）模型是一种功能强大的代谢模型，在生物过程控制和优化方面具有很大的应用潜力。然而，dFBA有一个嵌入式线性FBA优化问题，其退化解会产生多个可能的状态轨迹，这些轨迹都满足模型。为了解决这种不确定性，我们提出了一种基于多阶段经济非线性模型预测控制的鲁棒控制方法，该方法允许在不太保守的情况下处理FBA问题的退化解。我们建议在FBA问题中增加一个正则化项，以确保一个唯一的解，并通过改变正则化权重来生成不确定性场景。以这种方式生成的场景对应于FBA问题的不同解决方案。然后，将正则化FBA问题的KKT条件作为最优控制问题的等式约束，采用直接搭配法求解最优控制问题。我们的方法是通过一个案例研究，在醋酸盐浓度的限制下，对一个进料批式生物反应器进行大肠杆菌生长的最佳控制来评估的。结果表明，结合dFBA模型，提出的MS-ENMPC方法能够有效地满足系统轨迹存在不确定性的约束条件。

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引用次数: 0

An incremental physics-informed neural network for rapid prediction of suspended sediment plume for deep-sea mining 用于深海采矿悬浮沉积物羽流快速预测的增量物理信息神经网络

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-12-05 DOI: 10.1016/j.jprocont.2025.103604

Yanxin Zhang, Shaoyuan Li

The dynamic evolution of suspended sediment plume for deep-sea mining poses significant challenges for long-term prediction, owing to its inherently nonlinear transport behavior, unknown key parameters, and changing monitoring conditions after mining. To address these issues, this study proposes a framework integrating prediction, sensing, and refinement. Specifically, an incremental Physics-Informed Neural Network (PINN) enhanced with the Learning without Forgetting (LwF) strategy is developed to enable adaptive parameter updates while preserving prior physical knowledge. Furthermore, the sensor layout is optimized to enhance local observability. Numerical results demonstrate that, compared with the traditional PINN model, the proposed method effectively reduces prediction errors by 18.6% and achieves accurate prediction of the dynamic suspended sediment plume.

由于深海采矿悬浮沉积物羽流本身的非线性运移特性、关键参数未知以及开采后监测条件的变化，给长期预测带来了重大挑战。为了解决这些问题，本研究提出了一个集预测、感知和细化于一体的框架。具体而言，开发了一种增量物理信息神经网络（PINN），该网络通过无遗忘学习（LwF）策略增强，在保留先验物理知识的同时实现自适应参数更新。此外，优化了传感器布局，增强了局部可观测性。数值结果表明，与传统的PINN模型相比，该方法有效地降低了18.6%的预测误差，实现了对动态悬沙羽流的准确预测。

引用次数: 0

New design of two-dimensional model predictive iterative learning control with novel error compensation for batch processes 批处理误差补偿的二维模型预测迭代学习控制新设计

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-12-01 DOI: 10.1016/j.jprocont.2025.103592

Chonggao Hu , Ridong Zhang , Furong Gao

Traditional two-dimensional (2D) model predictive model iterative learning control strategies can only rely on feedback to passively deal with time delays, repetitive disturbances, and non-repetitive disturbances of batch processes. To address these shortcomings, this paper proposes a two-dimensional model predictive iterative learning control strategy using an improved state space model structure with new error compensation (2D-EC-MPILC). Firstly, a two-dimensional extended non-minimal state space (2D-ENMSS) model is established, which can provide more degrees of freedom for controller design. Secondly, a novel error compensation (EC) strategy is proposed to correct the tracking error value of the current batch. The novel 2D-EC-MPILC controller is designed with both additional tuning degrees and batch-wise error correction, ensuring an improved control performance. The proposed algorithm is tested on the holding pressure control system of an injection molding process and the temperature control system of a nonlinear batch reactor.

传统的二维（2D）模型预测模型迭代学习控制策略只能依靠反馈来被动处理批处理过程的时间延迟、重复干扰和非重复干扰。针对这些不足，本文提出了一种基于改进状态空间模型结构和新的误差补偿的二维模型预测迭代学习控制策略（2D-EC-MPILC）。首先，建立了二维扩展非最小状态空间（2D-ENMSS）模型，为控制器设计提供了更大的自由度；其次，提出了一种新的误差补偿策略来修正当前批次的跟踪误差值。新型2D-EC-MPILC控制器设计具有额外的调谐度和批量误差校正，确保了更好的控制性能。在注射成型过程保压控制系统和非线性间歇反应器温度控制系统上对该算法进行了验证。

引用次数: 0

Efficient computation for sample-based state estimators using deep neural networks 基于样本的深度神经网络状态估计器的高效计算

IF 3.9 2区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

Journal of Process Control

Pub Date : 2025-12-01 DOI: 10.1016/j.jprocont.2025.103591

Abhilash Dev, Sharad Bhartiya, Sachin Patwardhan

Sample-based nonlinear state estimation is an intensive problem to solve and significantly contributes to the computation delay in real-time applications. This paper explores a novel neural network (NN) based state estimation method in order to reduce the computation time required by sample-based state estimators during online deployment. The proposed method obtains estimates for the predicted as well as the filtered states, wherein the NN is trained using data obtained from simulations of constrained state estimators. The efficacy of the method is demonstrated by learning constrained Unscented Kalman Filter (UKF) on a six-state benchmark Williams Otto reactor with four measurements using a deep feedforward NN. The NN is then used as the proposal density in lieu of UKF for a Particle Filter (PF) implementation. Another example consists of exploring the role of the trained NN as a state estimator in a nonlinear internal model control (NIMC) application for tracking of economic setpoints in the benchmark Willaims-Otto reactor. Both these applications show that the proposed approach significantly outperforms the sample-based estimator in terms of the computation time while matching its performance, thereby reducing the latency related to sample-based state estimators.

在实时应用中，基于样本的非线性状态估计是一个需要大量解决的问题，对计算延迟有很大影响。为了减少基于样本的状态估计器在线部署时的计算时间，提出了一种基于神经网络的状态估计方法。该方法获得预测状态和过滤状态的估计，其中神经网络使用从约束状态估计器模拟中获得的数据进行训练。通过使用深度前馈神经网络在具有四个测量值的六状态基准Williams Otto反应器上学习约束无气味卡尔曼滤波器（UKF），证明了该方法的有效性。然后将NN用作提议密度，代替UKF用于粒子滤波（PF）实现。另一个例子包括探索在非线性内模控制（NIMC）应用中，训练好的神经网络作为状态估计器的作用，用于跟踪基准williams - otto反应堆的经济设定值。这两个应用都表明，所提出的方法在匹配其性能的同时，在计算时间方面显著优于基于样本的估计器，从而减少了与基于样本的状态估计器相关的延迟。

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