Journal of Process Control最新文献_第6页

Novel observer-based fault-tolerant tracking control of input-constrained polymerization reactor with statistical analysis 基于观测器的统计分析输入约束聚合反应器容错跟踪控制

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

Journal of Process Control

Pub Date : 2025-11-01 Epub Date: 2025-09-10 DOI: 10.1016/j.jprocont.2025.103541

Zahra Ahangari Sisi , Mehdi Mirzaei , Sadra Rafatnia , Somayeh Jamshidi , Maryam Farbodi

The polymerization reaction within a continuous stirred tank reactor is modeled as a multivariable, nonlinear control process with input constraints. This study proposes a novel optimization-based approach for fault diagnosis and compensation, despite the uncertainties and disturbances present in the dynamic model of the polymerization reactor. This approach facilitates the design of a reliable model-based controller through the estimation of system perturbations. The proposed strategy mitigates external disturbances, time-varying uncertainties, and faults by incorporating complementary terms, calculated in real-time from output measurements, into the initial process model. To ensure robust performance of the fault detection mechanism, the threshold bounds for external disturbances and other uncertainties are determined stochastically using the Monte Carlo simulation approach. A continuous predictive controller is designed in closed form based on the updated reactor model, accounting for the presence of control input limitations. The constrained controller is formulated by solving an optimization problem using the Karush–Kuhn–Tucker (KKT) conditions. The boundedness of the tracking errors is established under the constrained multivariable controller. The results demonstrate that the proposed method exhibits high sensitivity, accuracy, and robustness in fault detection and isolation for a nonlinear uncertain reactor. Simulations confirm the superior performance of the proposed observer-based fault-tolerant control system over existing passive and active actuator fault-tolerant control methods.

将连续搅拌釜反应器内的聚合反应建模为具有输入约束的多变量非线性控制过程。本研究提出了一种新的基于优化的故障诊断和补偿方法，尽管聚合反应器的动态模型中存在不确定性和干扰。该方法通过对系统扰动的估计，便于设计可靠的基于模型的控制器。该策略通过将从输出测量实时计算的互补项纳入初始过程模型，减轻了外部干扰、时变不确定性和故障。为了保证故障检测机制的鲁棒性，采用蒙特卡罗模拟方法随机确定外部干扰和其他不确定性的阈值边界。考虑到控制输入的限制，在更新的反应器模型的基础上，以封闭形式设计了连续预测控制器。利用Karush-Kuhn-Tucker （KKT）条件求解优化问题，建立了约束控制器。在约束多变量控制下，建立了跟踪误差的有界性。结果表明，该方法对非线性不确定电抗器的故障检测和隔离具有较高的灵敏度、准确性和鲁棒性。仿真结果表明，基于观测器的容错控制系统优于现有的被动和主动执行器容错控制方法。

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引用次数: 0

Observer based dual-layer sliding mode fault-tolerant control for dissolved oxygen concentration in wastewater treatment process 基于观测器的废水处理过程溶解氧浓度双层滑模容错控制

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

Journal of Process Control

Pub Date : 2025-11-01 Epub Date: 2025-09-09 DOI: 10.1016/j.jprocont.2025.103538

Hongyan Yang, Qi Zou, Honggui Han

Fault-tolerant control (FTC) of dissolved oxygen concentration is the core technology to ensure the robustness of wastewater treatment process (WWTP). However, the dynamic characteristics of microbial community are difficult to be modeled accurately, and external disturbances such as fluctuations in influent water quality and equipment failures further increase the control difficulty. Therefore, how to effectively compensate for the unmodeled dynamics and improve the system robustness is still a key problem to be solved in the field of WWTP control. In order to address this problem, this paper proposes an FTC method for dissolved oxygen concentration that integrates a dual sliding mode observation mechanism and an intelligent optimization strategy. Firstly, a state observer with an adaptive compensation mechanism is constructed based on the sliding mode control (SMC) method to realize the simultaneous estimation of dissolved oxygen concentration and unmodeled dynamics. Secondly, an adaptive robust fault-tolerant controller is designed by combining the Lyapunov stability theory. Then, a double sliding mode surface containing observation error and control error is established. Thirdly, a differential evolutionary algorithm is introduced to perform a global optimization of the robust gain parameters, which transforms the complex robustness problem into an optimal gain solving problem. Simulation experiments are conducted to compare the fault-tolerant control effect of different control methods with the proposed method, and the results verify the superiority of the method proposed in this paper.

溶解氧浓度容错控制是保证污水处理过程鲁棒性的核心技术。然而，微生物群落的动态特征难以准确建模，而进水水质波动和设备故障等外部干扰进一步增加了控制难度。因此，如何有效地补偿未建模的动力学，提高系统的鲁棒性仍然是污水处理控制领域需要解决的关键问题。为了解决这一问题，本文提出了一种溶解氧浓度的FTC方法，该方法集成了双滑模观测机制和智能优化策略。首先，基于滑模控制（SMC）方法构造了带自适应补偿机制的状态观测器，实现了溶解氧浓度和未建模动力学的同步估计；其次，结合李雅普诺夫稳定性理论，设计了自适应鲁棒容错控制器。然后，建立了包含观测误差和控制误差的双滑模曲面。第三，引入差分进化算法对鲁棒增益参数进行全局优化，将复杂的鲁棒性问题转化为最优增益求解问题。通过仿真实验比较了不同控制方法与本文方法的容错控制效果，结果验证了本文方法的优越性。

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引用次数: 0

Operational zone-specific univariate alarm design for incipient faults 针对早期故障的特定操作区域单变量报警设计

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

Journal of Process Control

Pub Date : 2025-11-01 Epub Date: 2025-09-06 DOI: 10.1016/j.jprocont.2025.103536

Mohsen Asaadi , Fan Yang , Weichi Wu

Alarm systems are essential components of industrial process monitoring, supporting both safety and operational efficiency by detecting deviations from normal conditions. Traditional alarm design methods often assume stationary, which limits their ability to reflect the evolving nature of incipient faults. These faults develop gradually and, if not properly addressed, can lead to critical failures. Timely and accurate detection is therefore vital to minimize false alarms, reduce missed detections, and improve response effectiveness. This study proposes a time-variant statistical modeling framework to characterize the behavior of process variables affected by incipient faults. A new alarm system design methodology is introduced, guided by three key performance indices: Missed Alarm Rate (MAR), False Alarm Rate (FAR), and Average Alarm Delay (AAD). The methodology uses the Narrowest Over Threshold change-point detection technique to segment the process into distinct operational zones, including the Normal Operating Zone (NOZ), Rising Zone (RZ), Fault Zone (FZ), and Return to Normal (RTN). By employing a piecewise time-variant model, the alarm system’s performance is assessed in a manner that captures local trends and transitions. The resulting indices are dynamic, offering a more detailed projection of the process variable’s behavior over time. In particular, the AAD metric reflects realistic delay patterns and avoids the misleading interpretations often associated with stationary models. The proposed method is validated through Monte Carlo simulations and demonstrated using the Tennessee Eastman Process benchmark. Results show that the time-variant model provides a more accurate and interpretable representation of process dynamics and alarm behavior than traditional approaches.

报警系统是工业过程监控的重要组成部分，通过检测正常情况的偏差来支持安全性和操作效率。传统的报警设计方法通常假设是平稳的，这限制了它们反映早期故障演变性质的能力。这些故障逐渐发展，如果处理不当，可能导致严重故障。因此，及时和准确的检测对于最大限度地减少误报、减少漏检和提高响应效率至关重要。本研究提出了一个时变统计建模框架，以表征受早期故障影响的过程变量的行为。介绍了一种新的报警系统设计方法，该方法以漏报率（MAR）、误报率（FAR）和平均报警延迟（AAD）三个关键性能指标为指导。该方法使用最窄阈值变更点检测技术将流程划分为不同的操作区域，包括正常操作区域（NOZ）、上升区域（RZ）、故障区域（FZ）和恢复正常（RTN）。通过采用分段时变模型，以捕获局部趋势和转变的方式评估报警系统的性能。所得到的指数是动态的，提供了过程变量随时间变化的更详细的预测。特别是，AAD度量反映了实际的延迟模式，并避免了通常与平稳模型相关的误导性解释。通过蒙特卡罗模拟验证了所提出的方法，并使用田纳西伊士曼过程基准进行了验证。结果表明，与传统方法相比，时变模型提供了更准确和可解释的过程动态和报警行为表示。

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引用次数: 0

Real-time identification of most critical alarms for alarm flood reduction 实时识别最关键的报警，减少报警洪水

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

Journal of Process Control

Pub Date : 2025-11-01 Epub Date: 2025-10-03 DOI: 10.1016/j.jprocont.2025.103563

Md Habibur Rahaman, Haniyeh Seyed Alinezhad, Tongwen Chen

In complex processes, the activation of a single alarm can trigger a cascade of consequences that affect multiple interconnected components. This can lead to a rapid increase in the number of active alarms. This sudden surge in alarms is often referred to as an alarm flood. Alarm floods are a common source of operational burden for operators, overwhelming them with a high volume of alarm notifications. If critical alarms are not promptly and accurately identified, decision-making processes can be undermined. This paper addresses these challenges by introducing a novel approach for identifying and prioritizing critical alarms from each alarm flood. The contributions of this work are twofold: First, hidden Markov models (HMMs) are employed to construct a likelihood matrix that uncovers relationships among alarm variables and identifies the most critical alarms through a directed acyclic graph (DAG). Second, expectation-maximization (EM) algorithm is applied to update the likelihood matrix dynamically and generate time-evolving plots for real-time identification of critical alarms. Case studies are conducted using a vinyl acetate monomer simulator to demonstrate the effectiveness of the proposed approach. The results highlight accurate identification and prioritization of critical alarms, enabling operators to focus on the most important process abnormalities.

在复杂的过程中，单个警报的激活可以触发影响多个相互连接的组件的级联结果。这可能导致活动告警的数量迅速增加。警报的突然激增通常被称为警报洪水。警报泛滥是运营商运营负担的常见来源，大量的警报通知使他们不堪重负。如果不能及时准确地识别关键警报，就会破坏决策过程。本文通过引入一种新的方法来识别和优先处理每个警报洪水中的关键警报来解决这些挑战。这项工作的贡献是双重的：首先，使用隐马尔可夫模型（hmm）来构建一个似然矩阵，该似然矩阵揭示了警报变量之间的关系，并通过有向无环图（DAG）识别最关键的警报。其次，应用期望最大化（EM）算法动态更新似然矩阵，生成时间演化图，实现关键告警的实时识别；使用醋酸乙烯单体模拟器进行了案例研究，以证明所提出方法的有效性。结果突出了关键警报的准确识别和优先级排序，使操作员能够专注于最重要的过程异常。

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引用次数: 0

Real-time freeze point prediction using multirate measurements in the blending process 在混合过程中使用多速率测量的实时凝固点预测

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

Journal of Process Control

Pub Date : 2025-11-01 Epub Date: 2025-09-23 DOI: 10.1016/j.jprocont.2025.103550

Khizer Mohamed , Om Prakash , Junyao Xie , Yanjun Ma , Haitao Zhang , Biao Huang

In blending processes, real-time monitoring of product properties is crucial for maintaining quality and optimizing operational efficiency. However, properties such as the freeze point are typically measured using slow and expensive laboratory tests. To enable real-time monitoring, analyzers are developed based on these laboratory measurements. Additionally, there are certain compounds whose freeze point is less than

- 70^{\circ} C

, which are beyond the detection limits of conventional laboratory techniques. This paper introduces a framework that combines the expectation–maximization algorithm with particle-filtering to estimate the freeze point of a compound used in the fuel-blending process, where conventional laboratory methods struggle to provide measurements. The method integrates multirate data, by combining high-frequency sensor data with low-frequency laboratory measurements, to estimate the freeze point. The soft sensor parameters are then identified using the estimated freeze point and directly measured input features such as the true boiling point. The proposed model allows estimation of the freeze point, particularly for components whose properties are not readily measurable using standard laboratory techniques. The proposed approach is compared against two other approaches: (1) a estimation using only high-frequency sensor data and (2) a estimation using only slow laboratory measurements. The soft sensor developed using the proposed framework reduces dependence on offline testing, providing a cost-effective and operationally viable alternative, while validation with industrial data confirms its applicability and effectiveness in real time, achieving an

R^{2}

value of 0.4074 that demonstrates reasonable predictive performance under industrial conditions.

在混合过程中，产品性能的实时监控对于保持质量和优化操作效率至关重要。然而，诸如凝固点之类的特性通常是通过缓慢而昂贵的实验室测试来测量的。为了实现实时监控，根据这些实验室测量结果开发了分析仪。此外，还有某些凝固点小于- 70°C的化合物，超出了传统实验室技术的检测极限。本文介绍了一个将期望最大化算法与颗粒过滤相结合的框架，以估计燃料混合过程中使用的化合物的凝固点，而传统的实验室方法难以提供测量。该方法通过将高频传感器数据与低频实验室测量数据相结合，集成多速率数据来估计凝固点。然后使用估计的冰点和直接测量的输入特征（如真沸点）来识别软传感器参数。所提出的模型允许对凝固点进行估计，特别是对于那些不能用标准实验室技术轻易测量的成分。将所提出的方法与其他两种方法进行比较：(1)仅使用高频传感器数据的估计和(2)仅使用慢速实验室测量的估计。利用所提出的框架开发的软传感器减少了对离线测试的依赖，提供了一种具有成本效益和操作可行性的替代方案，而工业数据验证则证实了其实时适用性和有效性，R2值为0.4074，在工业条件下显示出合理的预测性能。

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引用次数: 0

Adaptive error feedback regulation problem of a first-order hyperbolic PDE system with unknown exosystem 未知外系的一阶双曲PDE系统的自适应误差反馈调节问题

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

Journal of Process Control

Pub Date : 2025-11-01 Epub Date: 2025-10-10 DOI: 10.1016/j.jprocont.2025.103562

Xin Wang, Feng-Fei Jin

This paper studies the output regulation problem for a first-order hyperbolic PDE system with disturbances generated by an unknown finite-dimensional exosystem. The main challenges arise from unbounded control and observation operators, as well as non-collocated input–output configuration. We first introduce a coordinate transformation that simplifies the system dynamics. Next, based on the transformed system, we design an observer and apply an adaptive internal model principle to estimate the unknown harmonic frequencies of the exosystem. We present a controller that achieves exponentially stable output regulation for the resulting closed-loop system. Finally, the effectiveness of the controller is demonstrated through numerical simulations which demonstrate effective parameter tracking,

g (0, t)

(the regulated output) achieves accurate tracking of

Φ_{r e f} (t)

(the reference signal), and the solution remain uniformly bounded of the

g

-part in closed-loop system.

研究了一类具有未知有限维外系统扰动的一阶双曲PDE系统的输出调节问题。主要挑战来自无界控制和观察操作符，以及非配置的输入输出配置。我们首先引入一个坐标变换来简化系统动力学。然后，基于变换后的系统，设计观测器，应用自适应内模原理估计系统的未知谐波频率。我们提出了一种控制器，使闭环系统的输出调节达到指数稳定。最后，通过数值仿真验证了该控制器的有效性。仿真结果表明，g(0,t)（被调节输出）实现了对参考信号Φref(t)的精确跟踪，且在闭环系统中g部分解保持一致有界。

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引用次数: 0

Deep learning-based model predictive control with exponential weighting strategy and its application in energy management systems 基于深度学习的指数加权模型预测控制及其在能源管理系统中的应用

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

Journal of Process Control

Pub Date : 2025-11-01 Epub Date: 2025-09-17 DOI: 10.1016/j.jprocont.2025.103542

Dan Cui , Yanfang Mo , Xiaofeng Yuan , Lingjian Ye , Kai Wang , Feifan Shen , Yalin Wang , Chunhua Yang , Weihua Gui

Building energy management plays an important role in improving the overall system efficiency and reducing energy consumption. To achieve this goal, it is significant and challenging for the optimization of energy consumption and the utilization of renewable energy sources. This work presents a deep learning-based model predictive control with exponential weighting (DLEMPC) strategy to control and optimize Energy Management Systems (EMS). First, an exponential weighting technique with decreasing characteristic is introduced to the cost function over the timeslots in the receding horizon of the MPC to improve the control performance of the system, which aims to obtain the control actions by paying more importance on recent timeslots in the finite time-horizon. Second, a controller based on the deep belief network (DBN) model is proposed to reduce computational complexity of the rolling horizon optimization in practical applications. The deep learning controller is obtained by training it with a large number of input and output data pairs that are generated from a well-defined MPC designed with the new cost function. Finally, the DLEMPC strategy is used to control and optimize an EMS, connected to a grid, battery, HVAC, and solar panel. The results demonstrate that DLEMPC strategy can significantly improve the energy efficiency of buildings and reduce energy consumption compared to the traditional MPC formula.

建筑能源管理对提高系统整体效率、降低能耗具有重要作用。实现这一目标，对能源消耗的优化和可再生能源的利用具有重要的意义和挑战性。本文提出了一种基于深度学习的指数加权模型预测控制（DLEMPC）策略来控制和优化能源管理系统。首先，为了提高系统的控制性能，在MPC的后退水平时隙的代价函数中引入了具有递减特征的指数加权技术，其目的是在有限的时间范围内更重视最近时隙的控制动作。其次，提出了一种基于深度信念网络（DBN）模型的控制器，以降低实际应用中滚动地平线优化的计算复杂度。深度学习控制器是通过训练大量的输入输出数据对得到的，这些数据对是由一个定义良好的MPC生成的，该MPC设计了新的成本函数。最后，将该策略用于控制和优化与电网、电池、暖通空调和太阳能电池板相连的EMS。结果表明，与传统的MPC公式相比，DLEMPC策略可以显著提高建筑的能源效率，降低能耗。

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引用次数: 0

Advances in modeling and control of nonlinear distributed parameter systems and their applications: A review 非线性分布参数系统的建模与控制及其应用研究进展

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

Journal of Process Control

Pub Date : 2025-11-01 Epub Date: 2025-09-20 DOI: 10.1016/j.jprocont.2025.103549

Bowen Xu , Weiqi Yang , Xinjiang Lu , Yunxu Bai , Yajun Wang

Numerous processes in various fields including engineering, physics, and chemistry, etc., belong to distributed parameter systems (DPSs). These systems are strongly spatiotemporal coupled, possessing complex time-varying dynamics and infinite-dimensional spatial distribution characteristics. Additionally, there are unknown initial/ boundary conditions and parameter variation during the interaction of information or energy exchange, especially in complex application scenarios (i.e., large operation range, large spatial region, etc.). These factors make the modeling, prediction and control of spatiotemporal dynamics extremely difficult and challenging. With the enrichment of computational resources and data-driven/ intelligent methods, many new frameworks and strategies are designed and applied for nonlinear DPSs, which promotes the research diversity and maturity of DPS theory. Meanwhile, the development also gives rise to new problems. From the perspective of review, this paper starts from the practical modeling and control problems in combination with several application cases of nonlinear DPSs, and summarizes the research and application progress, including traditional methods, data-driven methods, intelligent modeling methods etc., and looks forward to the future development trends, providing guidance for related research and practical problem-solving of nonlinear DPSs.

工程、物理、化学等领域的许多过程都属于分布式参数系统（dps）。这些系统具有强烈的时空耦合性，具有复杂的时变动力学和无限维空间分布特征。此外，在信息或能量交换的交互过程中，存在未知的初始/边界条件和参数变化，特别是在复杂的应用场景中（如大操作范围、大空间区域等）。这些因素使得时空动态的建模、预测和控制变得极其困难和具有挑战性。随着计算资源的丰富和数据驱动/智能方法的发展，许多新的DPS框架和策略被设计和应用，促进了DPS理论研究的多样性和成熟度。与此同时，发展也带来了新的问题。本文从综述的角度出发，结合非线性dps的几个应用案例，从实际建模和控制问题出发，总结了包括传统方法、数据驱动方法、智能建模方法等方面的研究和应用进展，并展望了未来的发展趋势，为非线性dps的相关研究和实际解决提供指导。

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引用次数: 0

Prior model identification for stochastic optimal control of continuous aqueous two-phase flotation 连续水两相浮选随机最优控制的先验模型辨识

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

Journal of Process Control

Pub Date : 2025-11-01 Epub Date: 2025-09-23 DOI: 10.1016/j.jprocont.2025.103524

Katrin Baumgärtner , Kim Carina Lohfink , Hermann Nirschl , Moritz Diehl

In chemical process control, where an accurate model of the system dynamics is often not available, advanced control strategies such as stochastic optimal control promise superior control performance as opposed to nominal approaches neglecting the – often significant – uncertainty associated with the model predictions. A crucial prerequisite for stochastic optimal control is a suitable description of the uncertainty associated with the available model as well as a computational description of how this uncertainty evolves as more measurements become available. In this work, we exemplify how a stochastic model might be identified from experimental data and illustrate how non-stochastic models fail to describe the available data in the presence of high inter-experimental variation within the dataset. To this end, model identification from experimental data of the continuous aqueous two-phase flotation serves as a case study. In a second step, we showcase the performance of an optimization-based control strategy which is based on the identified stochastic model in closed-loop experiments.

在化学过程控制中，通常无法获得精确的系统动力学模型，与忽略与模型预测相关的（通常是显著的）不确定性的标称方法相反，诸如随机最优控制之类的高级控制策略保证了优越的控制性能。随机最优控制的一个关键先决条件是对与可用模型相关的不确定性的适当描述，以及随着更多测量变得可用，这种不确定性如何演变的计算描述。在这项工作中，我们举例说明了如何从实验数据中识别随机模型，并说明了在数据集中存在高实验间差异的情况下，非随机模型如何无法描述可用数据。为此，从连续两相水浮选实验数据中进行模型识别作为案例研究。在第二步中，我们展示了基于识别的随机模型的基于优化的控制策略在闭环实验中的性能。

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引用次数: 0

Research on control methods for gas-liquid separators based on UKF-LSTM hybrid observation and sliding mode control 基于UKF-LSTM混合观测与滑模控制的气液分离器控制方法研究

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

Journal of Process Control

Pub Date : 2025-11-01 Epub Date: 2025-10-25 DOI: 10.1016/j.jprocont.2025.103573

Chuan Wang , Haojie Liao , Kui Xie , Chao Yu

This study proposes a robust control framework that integrates sliding mode control (SMC) with a novel hybrid observer (UKF-LSTM in series) to stabilize separator level and pressure. The stability of the control system is ensured by the Lyapunov method. A significant innovation is a hybrid observer that combines an Unscented Kalman Filter (UKF) and a Long Short-Term Memory (LSTM) network in series to accurately estimate the unmeasurable multiphase inflow. In OLGA plug flow simulations, the framework reduced flow estimation Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 73.9 % and 64.7 % over the baseline. The Control tests showed Integral of Squared Error (ISE), Integral of Absolute Error (IAE), and Integral of Time-weighted Absolute Error (ITAE) were 49.8 %, 24.8 %, and 18.0 %, with convergence accelerated by at least 250 s. Results demonstrate that the method achieves a practical balance between accuracy, robustness, and computational efficiency, making it suitable for real-time industrial separator control under variable conditions.

本研究提出了一种鲁棒控制框架，该框架将滑模控制（SMC）与新型混合观测器（UKF-LSTM串联）相结合，以稳定分离器液位和压力。采用李亚普诺夫方法保证了控制系统的稳定性。一个重要的创新是混合观测器，它将Unscented卡尔曼滤波器（UKF）和长短期记忆（LSTM）网络串联在一起，以准确估计不可测量的多相流入。在OLGA塞流模拟中，该框架将流量估计的平均绝对误差（MAE）和均方根误差（RMSE）比基线分别降低了73.9 %和64.7 %。对照试验表明，平方误差积分（ISE）、绝对误差积分（IAE）和时间加权绝对误差积分（ITAE）分别为49.8 %、24.8 %和18.0 %，收敛速度至少加快250 s。结果表明，该方法在精度、鲁棒性和计算效率之间取得了很好的平衡，适用于工业分选机在可变条件下的实时控制。

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引用次数: 0