Journal of Process Control最新文献_第3页

An inter-domain feature discrepancy method for multi-source partial domain fault diagnosis 多源局部域故障诊断的域间特征差异方法

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

Journal of Process Control

Pub Date : 2026-02-01 Epub Date: 2026-01-13 DOI: 10.1016/j.jprocont.2026.103627

Shuai Tan , Shuxuan Zeng , Jijie Han , Qingchao Jiang , Weimin Zhong , Jiayi Wang

Multi-source domain adaptation poses more complex challenges compared to traditional single source domain adaptation. While constrained target domain labeling and limited information from a single source can be mitigated, the inherent discrepancies among multiple domains exacerbate the difficulty of fault diagnosis under varying operating conditions, particularly in real industrial systems with diverse and intricate environments. To tackle these issues, a novel Multi-source Inter-domain Feature Discrepancy (MIFD) model is proposed in this paper, which differs from existing multi-source adaptation methods by explicitly modeling inter-domain feature discrepancies instead of solely enforcing a unified shared feature space through global or marginal distribution alignment. In the proposed framework, a three-scale alignment mechanism is introduced to jointly align feature representations, class semantics, and domain distributions, thereby constraining domain shifts at multiple semantic levels while preserving domain-pair-specific characteristics. A discrepancy-aware feature matching module is developed to enable the extraction of reliable and transferable features tailored to specific source–target domain pairs. Furthermore, a class-center and domain alignment strategy is designed to constrain conditional distributions and alleviate pseudo-label bias. In addition, a dual-level weighting scheme is proposed, by which domain contributions are adaptively quantified and irrelevant classes are automatically filtered. Experimental results on two benchmark fault diagnosis scenarios under partial label space settings demonstrate that the proposed MIFD model outperforms state-of-the-art multi-source domain adaptation methods by up to 5.13% on the CWRU dataset and achieves an improvement of 2.16% on the TEP dataset, effectively reducing negative transfer and domain conflicts while enhancing diagnostic robustness under label space inconsistency.

与传统的单源域自适应相比，多源域自适应面临更复杂的挑战。虽然可以减轻目标域标记的约束和单一来源的有限信息，但多域之间固有的差异加剧了在不同运行条件下的故障诊断困难，特别是在具有多样化和复杂环境的实际工业系统中。为了解决这些问题，本文提出了一种新的多源域间特征差异（MIFD）模型，该模型不同于现有的多源自适应方法，它明确地建模域间特征差异，而不是仅仅通过全局或边缘分布对齐来强制实现统一的共享特征空间。在该框架中，引入了一种三尺度对齐机制来联合对齐特征表示、类语义和领域分布，从而在保留领域对特定特征的同时约束多个语义级别的领域移动。开发了一个差异感知特征匹配模块，以实现针对特定源-目标域对提取可靠且可转移的特征。此外，还设计了类中心和领域对齐策略来约束条件分布和减轻伪标签偏差。此外，提出了一种自适应量化领域贡献和自动过滤不相关类的双级加权方案。在两个部分标签空间设置的基准故障诊断场景下的实验结果表明，本文提出的MIFD模型在CWRU数据集上的性能比目前最先进的多源域自适应方法提高了5.13%，在TEP数据集上的性能提高了2.16%，有效地减少了负迁移和域冲突，同时增强了标签空间不一致下的诊断鲁棒性。

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引用次数: 0

Correcting batch effects in fermentation processes using empirical Bayesian approach 利用经验贝叶斯方法修正发酵过程中的批效应

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

Journal of Process Control

Pub Date : 2026-02-01 Epub Date: 2026-01-03 DOI: 10.1016/j.jprocont.2025.103616

Kaiqiang Lou, Shunyi Zhao, Xiaoli Luan, Fei Liu

Modeling fermentation processes is challenging due to their nonlinear dynamics, time-dependent behavior, and inherent system uncertainties. Data-driven approaches, including black-box and gray-box models, are widely used in practice, but their performance relies heavily on the consistency and reliability of input data. A common issue affecting fermentation datasets is the presence of batch effects, which refer to systematic differences between datasets collected from separate fermentation runs conducted under similar conditions. These differences reduce data comparability and hinder reliable modeling. To address this problem, this study proposes an empirical Bayes-based method for fermentation datasets. A key component of the proposed approach is an unsupervised batch clustering strategy that enables more stable parameter estimation in the absence of within-batch replicates. The clustering-assisted ComBat method is applied to two representative cases: penicillin fermentation and Saccharomyces cerevisiae yeast fermentation. On the penicillin dataset (20 batches), the results demonstrate that the method effectively reduces batch-to-batch variability by 70.3% (median standard deviation) and improves data consistency by 74.4% (median coefficient of variation). Evaluation using the median absolute deviation confirms its advantage over conventional correction methods, resulting in a 64.4% reduction relative to the raw data. Additional tests on larger datasets further support its robustness and practical applicability.

由于发酵过程的非线性动力学、时变行为和固有的系统不确定性，建模是具有挑战性的。数据驱动方法，包括黑盒模型和灰盒模型，在实践中得到了广泛的应用，但它们的性能严重依赖于输入数据的一致性和可靠性。影响发酵数据集的一个常见问题是批次效应的存在，这是指在相似条件下进行的单独发酵运行收集的数据集之间的系统差异。这些差异降低了数据的可比性，阻碍了可靠的建模。为了解决这个问题，本研究提出了一种基于经验贝叶斯的发酵数据集方法。该方法的一个关键组成部分是无监督批聚类策略，该策略在没有批内重复的情况下实现更稳定的参数估计。将聚类辅助战斗方法应用于青霉素发酵和酿酒酵母发酵两个典型案例。在青霉素数据集（20批次）上，结果表明，该方法有效地将批间变异性（中位标准差）降低了70.3%，将数据一致性（中位变异系数）提高了74.4%。使用中位数绝对偏差的评估证实了它比传统校正方法的优势，相对于原始数据减少了64.4%。对更大数据集的额外测试进一步支持其鲁棒性和实际适用性。

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引用次数: 0

An explicit model predictive control framework based on physics-informed neural networks 基于物理信息神经网络的显式模型预测控制框架

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

Journal of Process Control

Pub Date : 2026-02-01 Epub Date: 2026-01-17 DOI: 10.1016/j.jprocont.2026.103634

Argyri Kardamaki , Teo Protoulis , Alex Alexandridis , Haralambos Sarimveis

This paper presents a novel control framework that integrates Physics-Informed Neural Networks (PINNs) with Model Predictive Control (MPC) for nonlinear dynamical systems. Unlike traditional MPC, which requires solving optimization problems in real time, the proposed method trains a single feedforward neural network to serve as an explicit controller that directly maps the current state, set-point, and disturbance signals to optimal control actions. The network is trained using a composite loss function that enforces the governing differential equations while incorporating control-oriented objectives such as set-point tracking, control smoothness, and soft constraints on states, inputs, and outputs. The proposed controller is validated on both single-input single-output (SISO) and multi-input multi-output (MIMO) water-tank benchmark systems, demonstrating accurate set-point tracking, effective measured disturbance rejection, and strong generalization across thousands of randomized test scenarios. A runtime comparison with a nonlinear MPC performing online optimization confirms that the explicit PINN-MPC approach achieves comparable control performance while requiring several orders of magnitude less computation time. These results highlight the scalability and computational efficiency of the proposed framework, positioning it as a novel paradigm for real-time control of nonlinear systems.

本文提出了一种将物理信息神经网络（PINNs）与模型预测控制（MPC）相结合的非线性动态系统控制框架。与需要实时解决优化问题的传统MPC不同，该方法训练单个前馈神经网络作为显式控制器，直接将当前状态、设定点和干扰信号映射到最优控制动作。该网络使用复合损失函数进行训练，该函数强化了控制微分方程，同时结合了面向控制的目标，如设定点跟踪、控制平滑性以及对状态、输入和输出的软约束。该控制器在单输入单输出（SISO）和多输入多输出（MIMO）水箱基准系统上进行了验证，证明了精确的设定点跟踪，有效的测量干扰抑制，以及在数千个随机测试场景中的强泛化。通过与在线优化的非线性MPC的运行时比较，证实了显式PINN-MPC方法在减少几个数量级的计算时间的同时获得了相当的控制性能。这些结果突出了所提出框架的可扩展性和计算效率，将其定位为非线性系统实时控制的新范例。

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引用次数: 0

Understanding the role of multi-agent technology on quality of manufacturing organizations: A hybrid MCDM analysis 理解多智能体技术对制造组织质量的作用：一个混合MCDM分析

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

Journal of Process Control

Pub Date : 2026-02-01 Epub Date: 2026-01-16 DOI: 10.1016/j.jprocont.2026.103628

Vikram Singh, Somesh Kumar Sharma

Maintaining quality in the manufacturing system has become a critical challenge in today’s rapidly evolving technological landscape. To overcome this, current research examines the role of Multi-Agent Technology (MAT) in improving the quality of manufacturing processes. For this, a conceptual framework consisting of eight factors and thirty-seven variables of MAT, identified from the literature, was analyzed using the Analytical Hierarchical Process (AHP), Sensitivity Analysis, Decision-Making Trial and Evaluation Laboratory (DEMATEL), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). AHP findings revealed ‘Production Planning’ as the highest-priority factor, followed by ‘Process Monitoring, Control, and Data Acquisition.’ DEMATEL established the interrelationships among variables, ensuring a collaborative approach to maintaining quality. Sensitivity analysis and TOPSIS validated the AHP results for consistency and robustness. The findings also indicated that Virtual Manufacturing, Distributed Digital Manufacturing, and Adaptive Agent-Based Architecture were the globally top ranked variables in the framework that help to ensure the quality of manufacturing processes. These findings contribute to developing autonomous, high-precision manufacturing systems for long-term competitiveness and quality assurance. This study provides valuable insights for researchers and managers, demonstrating that MAT and its parameters can be customized to optimize manufacturing quality.

在当今快速发展的技术环境中，保持制造系统的质量已成为一个关键的挑战。为了克服这一点，目前的研究考察了多智能体技术（MAT）在提高制造过程质量方面的作用。为此，利用层次分析法（AHP）、敏感性分析法、决策试验与评价实验室法（DEMATEL）和理想解相似性排序偏好法（TOPSIS）对从文献中确定的由8个因素和37个变量组成的MAT概念框架进行了分析。AHP调查结果显示，“生产计划”是最优先考虑的因素，其次是“过程监控、控制和数据采集”。DEMATEL建立了变量之间的相互关系，确保了保持质量的协作方法。敏感性分析和TOPSIS验证了AHP结果的一致性和稳健性。研究结果还表明，虚拟制造、分布式数字制造和基于自适应代理的体系结构是框架中全球排名最高的变量，有助于确保制造过程的质量。这些发现有助于开发自主的高精度制造系统，以实现长期竞争力和质量保证。本研究为研究人员和管理人员提供了有价值的见解，表明MAT及其参数可以定制以优化制造质量。

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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 : 2026-01-01 Epub 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

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 : 2026-01-01 Epub 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

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 : 2026-01-01 Epub 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

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 : 2026-01-01 Epub 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

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 : 2026-01-01 Epub 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

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 : 2026-01-01 Epub 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