Journal of Process Control最新文献

Numerical solution of nonlinear periodic optimal control problems using a Fourier integral pseudospectral method 用傅立叶积分伪谱法数值求解非线性周期优化控制问题

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-11-04 DOI: 10.1016/j.jprocont.2024.103326

Kareem T. Elgindy

{"title":"Numerical solution of nonlinear periodic optimal control problems using a Fourier integral pseudospectral method","authors":"Kareem T. Elgindy","doi":"10.1016/j.jprocont.2024.103326","DOIUrl":"10.1016/j.jprocont.2024.103326","url":null,"abstract":"<div><div>Many real-world systems exhibit cyclical behavior and nonlinear dynamics. Optimal control theory provides a framework for determining the best periodic control strategies for such systems. These strategies achieve the desired goals while minimizing the costs, energy use, or other relevant metrics. This study addresses this challenge by introducing the Fourier integral pseudospectral (FIPS) method. This method is applicable to a general class of nonlinear periodic process control problems with equality and/or inequality constraints, assuming sufficiently smooth solutions. The FIPS method performs collocation of the problem’s integral form at an equidistant set of nodes. Furthermore, it utilizes highly accurate Fourier integration matrices (FIMs) to approximate all necessary integrals. This approach transforms the original problem into a nonlinear programming problem (NLP) with algebraic constraints. We employed a direct numerical optimization method to solve this NLP effectively. This study establishes rigorous convergence properties and derives error estimates for the Fourier series, interpolants, and quadratures employed within the context of process control applications, focusing on smooth and continuous periodic functions. Finally, the accuracy and efficiency of the FIPS method are demonstrated through two illustrative nonlinear process-control problems.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579014","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

FPGA-embedded optimization algorithm to maximize the acetate productivity in a dark fermentation process 在黑暗发酵过程中最大限度提高醋酸生产率的 FPGA 嵌入式优化算法

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-11-01 DOI: 10.1016/j.jprocont.2024.103323

José de Jesús Colín-Robles , Ixbalank Torres-Zúñiga , Mario A. Ibarra-Manzano , J. Gabriel Aviña-Cervantes , Víctor Alcaraz-González

{"title":"FPGA-embedded optimization algorithm to maximize the acetate productivity in a dark fermentation process","authors":"José de Jesús Colín-Robles , Ixbalank Torres-Zúñiga , Mario A. Ibarra-Manzano , J. Gabriel Aviña-Cervantes , Víctor Alcaraz-González","doi":"10.1016/j.jprocont.2024.103323","DOIUrl":"10.1016/j.jprocont.2024.103323","url":null,"abstract":"<div><div>This paper presents an optimization strategy to online maximize the acetate productivity rate in a dark fermentation (DF) process. The Golden Section Search algorithm is used to compute the maximum acetate productivity rate as a function of the inlet chemical oxygen demand (COD) and the dilution rate, selected as a manipulated variable. Such maximum productivity is considered as a reference by a Super-Twisting controller to regulate the real acetate productivity rate of the DF process. Due to the lack of sensors to measure the COD online, the optimization strategy includes an unknown input observation strategy integrated by a Luenberger observer interconnected to a Super-Twisting observer to estimate the inlet COD concentration. The optimization algorithm is embedded in an FPGA (Field Programmable Gate Array) device to minimize hardware resources and power consumption. The feasibility of the online optimization strategy embedded in an FPGA, using a digital architecture designed with a fixed-point format representation, is demonstrated by numerical simulations. Results show that the optimization strategy requires 53% of the logic elements and 100% of 8-bit multipliers of an FPGA Cyclone II and the power consumption estimated is only <span><math><mrow><mn>190</mn><mspace></mspace><mi>m</mi><mi>W</mi></mrow></math></span>.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572468","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

Data Science and Model Predictive Control: 数据科学与模型预测控制

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-11-01 DOI: 10.1016/j.jprocont.2024.103327

Marcelo M. Morato , Monica S. Felix

{"title":"Data Science and Model Predictive Control:","authors":"Marcelo M. Morato , Monica S. Felix","doi":"10.1016/j.jprocont.2024.103327","DOIUrl":"10.1016/j.jprocont.2024.103327","url":null,"abstract":"<div><div>Model Predictive Control (MPC) is an established control framework, based on the solution of an optimisation problem to determine the (optimal) control action at each discrete-time sample. Accordingly, major theoretical advances have been provided in the literature, such as closed-loop stability and recursive feasibility certificates, for the most diverse kinds of processes descriptions. Nevertheless, identifying <em>good</em>, trustworthy models for complex systems is a task heavily affected by uncertainties. As of this, developing MPC algorithms <strong>directly from data</strong> has recently received a considerable amount of attention over the last couple of years. In this work, we review the available <strong>data-based MPC</strong> formulations, which range from reinforcement <strong>learning</strong> schemes, <strong>adaptive</strong> controllers, and novel solutions based on behavioural theory and <strong>trajectory</strong> representations. In particular, we examine the recent research body on this topic, highlighting the main features and capabilities of available algorithms, while also discussing the fundamental connections among approaches and, comparatively, their advantages and limitations.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572464","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

Adaptive error feedback regulation for a 1-D anti-stable wave equation subject to harmonic disturbances 受谐波干扰的一维反稳定波方程的自适应误差反馈调节

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-10-28 DOI: 10.1016/j.jprocont.2024.103324

Shuangxi Huang , Qing-Qing Hu

引用次数: 0

Enhanced predictive PDF control of stochastic distribution systems with neural network compensation and its application 采用神经网络补偿的随机配电系统的增强型预测性 PDF 控制及其应用

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-10-24 DOI: 10.1016/j.jprocont.2024.103328

Ping Zhou, Xiaoyang Sun, Lixiang Zhang, Mingjie Li

{"title":"Enhanced predictive PDF control of stochastic distribution systems with neural network compensation and its application","authors":"Ping Zhou, Xiaoyang Sun, Lixiang Zhang, Mingjie Li","doi":"10.1016/j.jprocont.2024.103328","DOIUrl":"10.1016/j.jprocont.2024.103328","url":null,"abstract":"<div><div>Compared to the conventional control algorithms that use mean and variance as indicators, predictive probability density function (PDF) control can effectively handle the output PDF control problem of non-Gaussian stochastic distribution systems. However, the existing predictive PDF control method does not consider the construction error between output PDF and weight, thus the control performance is still unsatisfactory. Therefore, this paper proposes a new Enhanced Predictive PDF control method (En-PDF) to improve the output PDF control performance of stochastic distribution systems. The proposed method mainly consists of two parts: the predictive PDF control part and the neural network compensation control part aiming to reduce the bias of output PDF. First, the Radical Basis Functions (RBFs) are used to approximate the PDF of the stochastic systems output, and then a prediction model representing the relationship between input and weight is established using the subspace identification algorithm to design the predictive PDF control for the stochastic systems. Next, the Kullback-Leibler (KL) divergence is used to measure the similarity between the output PDF and the set PDF, combined with the weight error and compensation to design a new performance index. Based on this, the parameters of the neural network are adjusted using the gradient descent algorithm to obtain the optimal compensation, and the stability and tracking performance of the proposed algorithm are analyzed using inductive reasoning method. Finally, the predictive PDF control input with the compensation work together on the controlled plant to achieve high-performance control of the output PDF of non-Gaussian stochastic distribution systems. Both simulation experiments and physical control experiments validate the effectiveness and superiority of the proposed method.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528045","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

Fixed-time active fault-tolerant control for a class of nonlinear systems with intermittent faults and input saturation 一类具有间歇性故障和输入饱和的非线性系统的固定时间主动容错控制

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-10-24 DOI: 10.1016/j.jprocont.2024.103319

Xuanrui Cheng, Ming Gao, Li Sheng, Yongli Wei

{"title":"Fixed-time active fault-tolerant control for a class of nonlinear systems with intermittent faults and input saturation","authors":"Xuanrui Cheng, Ming Gao, Li Sheng, Yongli Wei","doi":"10.1016/j.jprocont.2024.103319","DOIUrl":"10.1016/j.jprocont.2024.103319","url":null,"abstract":"<div><div>In this paper, the problem of fixed-time active fault-tolerant control is studied for systems with sector-bounded nonlinearities, intermittent faults, and input saturation. Since intermittent faults appear and disappear randomly, a fixed-time scheme is considered in the active fault-tolerant control algorithm, composed of detection, isolation, estimation, and the control unit. Utilizing homogeneity-based observers, the fixed-time state estimation is available in the presence of unknown but bounded disturbances, and a fault diagnosis unit is proposed. An input saturation compensator is introduced to analyze the effect of input saturation, and its auxiliary variables are used in the reconfigurable control law. The fault-tolerant controller, which is constructed via the information provided by the fault diagnosis unit and saturation compensator, has two switching modes. As a consequence, intermittent faults are compensated via the designed active fault-tolerant control method and the system reaches practical stability with the entire convergence time bounded in a fixed time. Finally, the example of a heat control system is exploited to demonstrate the effectiveness of the developed active fault-tolerant control scheme.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528048","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

Pre-connected and trainable adjacency matrix-based GCN and neighbor feature approximation for industrial fault diagnosis 用于工业故障诊断的基于邻接矩阵和邻近特征近似的预连接和可训练邻接矩阵

IF 3.3 2区计算机科学

Journal of Process Control Pub Date : 2024-10-24 DOI: 10.1016/j.jprocont.2024.103320

Hao-Yang Qing, Ning Zhang, Yan-Lin He, Qun-Xiong Zhu, Yuan Xu

{"title":"Pre-connected and trainable adjacency matrix-based GCN and neighbor feature approximation for industrial fault diagnosis","authors":"Hao-Yang Qing, Ning Zhang, Yan-Lin He, Qun-Xiong Zhu, Yuan Xu","doi":"10.1016/j.jprocont.2024.103320","DOIUrl":"10.1016/j.jprocont.2024.103320","url":null,"abstract":"<div><div>Industrial fault diagnosis methods based on graph convolution network (GCN) becomes a hot topic for its great feature extraction ability to multivariate time-series data. However, GCNs ignore inter-sample temporality when constructing the adjacency matrix (AM), leading to low prediction accuracy. A novel fault diagnosis method based on pre-connected and trainable AM-based GCN and neighbor feature approximation (PTGCN-FA) is proposed at the node-level task. Firstly, PTGCN-FA introduces the temporal nearest neighbors into spatial nearest neighbors to pre-connect and construct the AM. Then, the AM is trained only where the samples are connected, which makes the best weights obtained and reduces the time complexity of the model. Finally, after the GCN layers, the trained AM is introduced into the approximation of features, which are neighbors in the original sample space. Two process industry cases are carried out, and the simulation results including diagnosis accuracy, confusion matrix, study to the ratio of labeled data and an ablation experiment verify PTGCN-FA has more efficient and accurate diagnostic performance than related methods. Additionally, the analysis of the temporal neighborhood weight parameter shows that the performance of fault diagnosis can be improved by considering both temporal and spatial information between samples.</div></div>","PeriodicalId":50079,"journal":{"name":"Journal of Process Control","volume":null,"pages":null},"PeriodicalIF":3.3,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142528046","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

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":null,"pages":null},"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