Journal of Process Control最新文献_第3页

Just-in-time framework for robust soft sensing based on robust variational autoencoder 基于稳健变异自动编码器的稳健软传感即时框架

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-10-22 DOI: 10.1016/j.jprocont.2024.103325

Fan Guo , Kun Liu , Biao Huang

{"title":"Just-in-time framework for robust soft sensing based on robust variational autoencoder","authors":"Fan Guo , Kun Liu , Biao Huang","doi":"10.1016/j.jprocont.2024.103325","DOIUrl":"10.1016/j.jprocont.2024.103325","url":null,"abstract":"<div><div>Modeling with high-dimensional data subject to abnormal observations have always been a practical interest. In this paper, under the just-in-time learning (JITL) framework, a robust soft sensor modeling approach is developed based on robust Variational Autoencoder (VAE). Unlike the vanilla VAE that extracts features from the given dataset under the Gaussian prior assumption, robust VAE employs Student’s t-distribution as prior distribution to handle abnormal data. Under assumption of the Student’s t-prior, the proposed robust VAE model is capable of describing collected data contaminated with outliers. Once the robust VAE model is trained, each robust feature variable in the latent space can be determined. Subsequently, similarity measure is calculated using robust Kullback-Leibler divergence between two Student’s t-distributions, that is, the distribution of a new data sample and that of each historical data sample. After completing similarity measurement for a query sample, the weights for input-output historical data can be determined. Based on these weighted historical data samples, a robust probabilistic principal component regression (PPCR) is utilized to perform local modeling for prediction. Numerical simulations, including the Tennessee Eastman and Penicillin fermentation benchmark processes, are utilized to validate the proposed JITL-based robust soft sensor modeling method.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"143 ","pages":"Article 103325"},"PeriodicalIF":3.3,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Experimental implementation of extremum-seeking control: Gas fuel efficiency at electrical generation under power requirements 极值搜索控制的实验实施：电力需求下的发电气体燃料效率

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-10-22 DOI: 10.1016/j.jprocont.2024.103322

Ricardo Femat , Jesús Torres-Mireles , Nimrod Vázquez-Nava

引用次数: 0

Robust bilinear tracking control of a parabolic trough solar collector via saturation 通过饱和对抛物面槽式太阳能集热器进行鲁棒双线性跟踪控制

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-10-19 DOI: 10.1016/j.jprocont.2024.103321

Sarah Mechhoud , Zehor Belkhatir

引用次数: 0

Robust MPC design for multi-model infinite-dimensional distributed parameter systems 多模型无限维分布式参数系统的鲁棒 MPC 设计

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-10-07 DOI: 10.1016/j.jprocont.2024.103316

Lu Zhang , Junyao Xie , Charles Robert Koch , Stevan Dubljevic

{"title":"Robust MPC design for multi-model infinite-dimensional distributed parameter systems","authors":"Lu Zhang , Junyao Xie , Charles Robert Koch , Stevan Dubljevic","doi":"10.1016/j.jprocont.2024.103316","DOIUrl":"10.1016/j.jprocont.2024.103316","url":null,"abstract":"<div><div>Infinite-dimensional systems are essential for describing complex phenomena that exhibit continuous spatial and temporal variations. This article introduces a robust model predictive control (RMPC) design to regulate constrained multi-model infinite-dimensional systems governed by a class of hyperbolic/parabolic partial differential equations (PDEs). Model uncertainty stems from system parameters that are imprecisely determined, but can be quantitatively characterized within a certain range. The RMPC algorithm is designed in a discrete-time infinite-dimensional setting, achieved through the structure-preserving Cayley–Tustin transformation without model reduction nor spatial approximation. Robustness of the controller is ensured via constraining the future cost for each model dynamics accounting for uncertainty description. Properties of the closed-loop system are discussed, including feasibility, convergence, and asymptotic stability. The proposed controller is implemented by considering three typical infinite-dimensional distributed parameter process models, with simulation demonstrating the effectiveness and enhanced performance of the RMPC over the nominal model predictive controller.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"143 ","pages":"Article 103316"},"PeriodicalIF":3.3,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interval estimation of sensor fault in rotary steerable drilling tools based on set-membership approach 基于集合成员法的旋转转向钻具传感器故障间隔估计

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-10-03 DOI: 10.1016/j.jprocont.2024.103318

Weiliang Wang, Limao Zhu, Yanjia Su, Shuaishuai Huang, Yanfeng Geng

{"title":"Interval estimation of sensor fault in rotary steerable drilling tools based on set-membership approach","authors":"Weiliang Wang, Limao Zhu, Yanjia Su, Shuaishuai Huang, Yanfeng Geng","doi":"10.1016/j.jprocont.2024.103318","DOIUrl":"10.1016/j.jprocont.2024.103318","url":null,"abstract":"<div><div>The rotary steerable drilling tools (RSDTs) are invented for high-precision wellpath control. Fault diagnosis is essential for the RSDT as it can improve the reliability of the drilling processes. To estimate the sensor fault of the RSDT, this paper investigates the interval estimation problem for Lipschitz nonlinear systems with sensor fault via the set-membership approach. Firstly, the RSDT is modeled by a discrete-time Lipschitz nonlinear system with unknown inputs, noises, and parameter uncertainties. Next, based on the ellipsoid bundles, a set-membership fault estimator is presented, where the unknown inputs are decoupled. Additionally, a sufficient condition, which is derived based on the <span><math><mi>P</mi></math></span>-radius of the ellipsoid bundles, is put forward to design the parameters of the estimator with Lipschitz nonlinear terms. Then, the interval estimations of the states and fault are obtained via ellipsoid bundles. Finally, the efficiency of the proposed approach is evaluated through numerical simulations and experiments.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"143 ","pages":"Article 103318"},"PeriodicalIF":3.3,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Study of parabolic trough collector with crucial analysis on controller and estimator with variable mirror efficiency 研究抛物面槽式集热器，并对具有可变镜面效率的控制器和估算器进行关键分析

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-10-01 DOI: 10.1016/j.jprocont.2024.103317

Dibyajyoti Baidya , Surender Kannaiyan , Neeraj Dhanraj Bokde

{"title":"Study of parabolic trough collector with crucial analysis on controller and estimator with variable mirror efficiency","authors":"Dibyajyoti Baidya , Surender Kannaiyan , Neeraj Dhanraj Bokde","doi":"10.1016/j.jprocont.2024.103317","DOIUrl":"10.1016/j.jprocont.2024.103317","url":null,"abstract":"<div><div>Solar Thermal Power (STP) plants are a crucial technology for converting solar energy into electricity. Among the STP, the Parabolic Trough Collector (PTC) is one of the integral parts of STP systems for electrical power generation. Maximizing PTC performance is a challenge due to dynamic and unpredictable solar radiation and environmental disturbances, such as cloud cover and dust accumulation. The optical efficiency parameter of the PTC is critical for calculating the desired heat gain, which is difficult to measure in real-time, necessitating sophisticated control and estimation strategies for optimal heat regulation. This study introduces two control mechanisms for the PTC system and comparing their efficacy. A classical PI controller supplemented by Static Feed-Forward (SFF) control demonstrates improved performance with an optimal transfer function model. In contrast, Nonlinear Model Predictive Control (NMPC) excels in disturbance rejection and setpoint tracking, considering operational constraints. The NMPC controller notably outperforms the PI controller with SFF in performance metrics, with the Integral Time Squared Error (ITSE) decreasing by approximately 99% across case studies. Also, in the case of heat gain, NMPC controller exhibits percentage increases when compared to the PI controller. Furthermore, since the measurement of optical efficiency is essential, but due to difficulty in the measurement for various reasons, the estimator is used as a virtual sensor to estimate those optical efficiency. The unmeasured states and parameters, including optical efficiencies of the PTC, are estimated using Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) techniques, with UKF exhibiting superior accuracy. However, considering the average computation time, EKF is preferred for real-time applications.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"143 ","pages":"Article 103317"},"PeriodicalIF":3.3,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142428442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Closed-loop training of static output feedback neural network controllers for large systems: A distillation case study 大型系统静态输出反馈神经网络控制器的闭环训练：蒸馏案例研究

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-09-21 DOI: 10.1016/j.jprocont.2024.103302

Evren Mert Turan, Johannes Jäschke

{"title":"Closed-loop training of static output feedback neural network controllers for large systems: A distillation case study","authors":"Evren Mert Turan, Johannes Jäschke","doi":"10.1016/j.jprocont.2024.103302","DOIUrl":"10.1016/j.jprocont.2024.103302","url":null,"abstract":"<div><p>The online implementation of model predictive control has two main disadvantages: it requires an estimate of the entire model state and an optimisation problem must be solved online. These issues have typically been treated separately. This work proposes an integrated approach for the offline training of an output feedback neural network controller in closed-loop. As the training is performed offline, the neural network can be efficiently evaluated online to find control actions given noisy measurements as inputs. In addition, as the controller is trained in closed loop we are able to train for robustness to uncertainty and also design the controller to only use a selection of measurements. The choice of measurements can greatly change the controller performance and robustness. We demonstrate that although measurements can be automatically selected by regularisation, choosing measurements based on engineering judgement is an effective alternative. The proposed method is demonstrated by extensive simulations using a non-linear distillation column model of 50 states. We show that a controller using only 4 measurements is able to control the system with a decrease in performance of only 15% compared to MPC with perfect state feedback.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"143 ","pages":"Article 103302"},"PeriodicalIF":3.3,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A survey and experimental study for embedding-aware generative models: Features, models, and any-shot scenarios 嵌入式感知生成模型的调查和实验研究：特征、模型和任意拍摄场景

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-09-21 DOI: 10.1016/j.jprocont.2024.103297

Jiaqi Yue, Jiancheng Zhao, Liangjun Feng, Chunhui Zhao

{"title":"A survey and experimental study for embedding-aware generative models: Features, models, and any-shot scenarios","authors":"Jiaqi Yue, Jiancheng Zhao, Liangjun Feng, Chunhui Zhao","doi":"10.1016/j.jprocont.2024.103297","DOIUrl":"10.1016/j.jprocont.2024.103297","url":null,"abstract":"<div><p>In the era of industrial artificial intelligence, grappling with data insufficiency remains a formidable challenge that stands at the forefront of our progress. The embedding-aware generative model emerges as a promising solution, tackling this issue head-on in the realm of zero-shot learning by ingeniously constructing a generator that bridges the gap between semantic and feature spaces. Thanks to the predefined benchmark and protocols, the number of proposed embedding-aware generative models for zero-shot learning is increasing rapidly. We argue that it is time to take a step back and reconsider the embedding-aware generative paradigm. The main work of this paper is two-fold. First, embedding features in benchmark datasets are somehow overlooked, which potentially limits the performance of generative models, while most researchers focus on how to improve them. Therefore, we conduct a systematic evaluation of 10 representative embedding-aware generative models and prove that even simple representation modifications on the embedding features can improve the performance of generative models for zero-shot learning remarkably. So it is time to pay more attention to the current embedding features in benchmark datasets. Second, based on five benchmark datasets, each with six any-shot learning scenarios, we systematically compare the performance of ten typical embedding-aware generative models for the first time, and we give a strong baseline for zero-shot learning and few-shot learning. Meanwhile, a comprehensive generative model repository, namely, generative any-shot learning repository, is provided, which contains the models, features, parameters, and scenarios of embedding-aware generative models for zero-shot learning and few-shot learning. Any results in this paper can be readily reproduced with only one command line based on generative any-shot learning.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"143 ","pages":"Article 103297"},"PeriodicalIF":3.3,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Physics-informed neural networks for multi-stage Koopman modeling of microbial fermentation processes 用于微生物发酵过程多级库普曼建模的物理信息神经网络

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-09-19 DOI: 10.1016/j.jprocont.2024.103315

Quan Li, Jingran Zhang, Haiying Wan, Zhonggai Zhao, Fei Liu

{"title":"Physics-informed neural networks for multi-stage Koopman modeling of microbial fermentation processes","authors":"Quan Li, Jingran Zhang, Haiying Wan, Zhonggai Zhao, Fei Liu","doi":"10.1016/j.jprocont.2024.103315","DOIUrl":"10.1016/j.jprocont.2024.103315","url":null,"abstract":"<div><p>This paper investigates the modeling problem of microbial fermentation suitable for model-based control design techniques. Given the evident nonlinear and stage characteristics of microbial fermentation processes, a single data-driven model cannot fully capture microbial growth characteristics. Therefore, we propose a multi-stage Koopman modeling method based on physics-informed neural networks. Initially, the fuzzy C-means clustering algorithm is employed to partition the microbial growth stages. Subsequently, the Koopman operator is approximated through physics-informed neural networks. Utilizing the Koopman operator to map the dynamic behavior of the microbial fermentation system into a high-dimensional linear space, and modeling each growth stage separately in the linear space. Compared to conventional neural network methods, physics-informed neural networks integrate the advantages of physical models and neural networks, thereby better preserving the dynamic information of microbial growth and enhancing the model’s generalization performance. A penicillin fermentation case study verifies the effectiveness of our proposed method.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"143 ","pages":"Article 103315"},"PeriodicalIF":3.3,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142271935","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Image based Modeling and Control for Batch Processes 基于图像的批处理建模和控制

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-09-16 DOI: 10.1016/j.jprocont.2024.103314

Aswin Chandrasekar, Kevork Baghdassarian, Farshad Moayedi, Hassan Abdulhussain, Vladimir Gritsichine, Michael R. Thompson, Prashant Mhaskar

{"title":"Image based Modeling and Control for Batch Processes","authors":"Aswin Chandrasekar, Kevork Baghdassarian, Farshad Moayedi, Hassan Abdulhussain, Vladimir Gritsichine, Michael R. Thompson, Prashant Mhaskar","doi":"10.1016/j.jprocont.2024.103314","DOIUrl":"10.1016/j.jprocont.2024.103314","url":null,"abstract":"<div><p>This manuscript addresses the problem of leveraging thermal images for modeling and feedback control, specifically tailored for terminal quality control of batch processes. The primary objective, common in many batch processes, is to produce products with quality variables aligning with user specifications, available for measurement only at batch termination, precluding the direct use of classical control strategies. Furthermore, in many instances, traditional online sensors such as thermocouples may not be available, but instead spectral inputs like thermal images or acoustic data may be more readily available for feedback control. The challenge is to not only use the non-traditional sensor data for building a dynamic model but also to use that model for terminal quality control. The proposed approach involves a multi-layered modeling strategy. Initially, a dimensionality reduction technique is employed to condense the high-dimensional image into a set of representative outputs. Subsequently, subspace identification (SSID) is applied to develop a Linear Time-Invariant (LTI) State Space (SS) model between the inputs and the reduced outputs. Finally, a Partial Least Squares (PLS) model is constructed linking the terminal states of a batch (identified using SSID) with the product qualities obtained for that specific batch. This model is then incorporated into a Model Predictive Control (MPC) formulation. The effectiveness of the MPC is illustrated by showcasing its capability to generate products of high quality by deploying the MPC on a bi-axial lab-scale rotational molding setup.</p></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":"143 ","pages":"Article 103314"},"PeriodicalIF":3.3,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0959152424001549/pdfft?md5=de874348a6817509c225aa28b2b6051c&pid=1-s2.0-S0959152424001549-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142243193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0