IFAC-PapersOnLine最新文献_第4页

Improving Process Monitoring via Dynamic Multi-Fidelity Modeling 通过动态多保真度建模改进过程监控

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.145

Rastislav Fáber , Marco Vaccari , Riccardo Bacci di Capaci , Karol Ľubušký , Gabriele Pannocchia , Radoslav Paulen

引用次数: 0

Data-driven material removal rate estimation in bonnet polishing process 阀盖抛光过程中数据驱动的材料去除率估计

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.147

Michal Darowski , Muhammad Faisal Aftab , David Walker , Hongyu Li , Christian W. Omlin

{"title":"Data-driven material removal rate estimation in bonnet polishing process","authors":"Michal Darowski , Muhammad Faisal Aftab , David Walker , Hongyu Li , Christian W. Omlin","doi":"10.1016/j.ifacol.2025.07.147","DOIUrl":"10.1016/j.ifacol.2025.07.147","url":null,"abstract":"<div><div>Bonnet polishing is an ultra-precision polishing technique used for manufacturing components utilized in optics, electronics, and Scientific instrumentation, where sub-nanometer accuracy is required. However, the process is not fully deterministic and requires multiple process-metrology iterations. In modern computer numerically controlled (CNC) machines, polishing is performed by moderating the bonnet tool dwell time at each location based on the input parameters and material removal rate (MRR). While the MRR is typically treated as constant once established, it continuously evolves due to the process’s dynamic nature and changing conditions. This variability in MRR impacts the convergence of the polishing process, necessitating repeated surface processing and resulting in increased manufacturing time and cost. In this work, we present a data-driven approach to estimate the amount of material removed during the pre-polishing routine in bonnet polishing. The estimations are based on the force exerted by the bonnet tool on a polished surface along the three dimensions. Measurements were obtained using a bespoke force table with load sensors across three axes, mounted on the Zeeko IRP600 machine table. The results demonstrate the Effectiveness of this data-driven approach for estimating MRR, achieving a mean absolute error of 0.0541 µm and a mean absolute percentage error of 5.89% across the test set.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 6","pages":"Pages 211-216"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hierarchical multimode dynamic process monitoring scheme and its application to the Tennessee Eastman process⁎ 一种分层多模式动态过程监控方案及其在田纳西伊士曼过程中的应用[j]

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.148

Jiaorao Wang , Lishuai Li , S. Joe Qin

{"title":"A hierarchical multimode dynamic process monitoring scheme and its application to the Tennessee Eastman process⁎","authors":"Jiaorao Wang , Lishuai Li , S. Joe Qin","doi":"10.1016/j.ifacol.2025.07.148","DOIUrl":"10.1016/j.ifacol.2025.07.148","url":null,"abstract":"<div><div>Multimode characteristics commonly exist in modern industrial processes. Previous multi-model approaches treat steady states and transitions separately. However, identifying each mode is often tedious, generally achieved through clustering, requiring operators to tune hyperparameters extensively. As practitioners prefer a concise and easily implemented approach for multimode dynamic process monitoring, we initially propose a hierarchical scheme to simplify the modeling process while enhancing monitoring performance. Our method iteratively constructs dynamic models in a hierarchical, monitoring-oriented manner without mode partition. It offers three advantages. Firstly, modeling is directly conducted following a hierarchical structure driven by monitoring indexes, which is more concise and ensures monitoring performance. Secondly, by eliminating mode partition, only three hyperparameters, such as model order and the termination condition, need to be decided by humans. This significantly reduces human labour and facilitates the applicability of the proposed method across various processes. Lastly, by focusing on dynamic characteristics rather than steady-state and transitional modes, our method reduces the number of required models for a given process, resulting in a simpler multi-model structure that still ensures monitoring performance.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 6","pages":"Pages 217-222"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Symmetric Kullback Leibler divergence-based design of experiments with estimation of unspecified values 基于非指定值估计的对称Kullback Leibler散度实验设计

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.197

Brijesh Kumar , Mani Bhushan

{"title":"Symmetric Kullback Leibler divergence-based design of experiments with estimation of unspecified values","authors":"Brijesh Kumar , Mani Bhushan","doi":"10.1016/j.ifacol.2025.07.197","DOIUrl":"10.1016/j.ifacol.2025.07.197","url":null,"abstract":"<div><div>In this work, we propose a Symmetric Kullback Leibler divergence (SKLD)-based approach for optimal Design of Experiments (DOE) along with estimation of unspecified values in the design of experiments data matrix. Using SKLD as optimality criteria as opposed to various existing alphabetic optimality criteria, facilitates the incorporation of end-user desired performance of estimates. For the case when experimental noise is Gaussian and uncorrelated, the proposed approach results in a Mixed Integer Non-Linear Programming (MINLP) problem. This problem is NP-hard to solve. Hence, a novel heuristic solution strategy is also proposed which solves the proposed problem iteratively and sequentially. In particular, the MINLP problem is split into two sub-problems: (i) Non-Linear Programming (NLP) problem: to estimate optimal unspecified values, and (ii) Non-Linear Integer Programming (IP) problem: to obtain optimal DOE. These two subproblems are solved sequentially and iteratively until convergence is reached. The proposed solution strategy guarantees the decreasing behaviour of SKLD value. The efficacy of the proposed solution strategy is tested on an illustrative example and a Material synthesis problem, and performance is compared with Fedorov exchange algorithm, Forward Greedy search algorithm, and some of the popular MINLP solvers available in GAMS environment. Results demonstrate that the proposed solution approach outperforms most other methods.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 6","pages":"Pages 510-515"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automatic design of robust model predictive control of a bioreactor via Bayesian optimization⁎ 基于贝叶斯优化的生物反应器鲁棒模型预测控制自动设计

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.115

Tobias Brockhoff , Moritz Heinlein , Georg Hubmann , Stephan Lütz , Sergio Lucia

{"title":"Automatic design of robust model predictive control of a bioreactor via Bayesian optimization⁎","authors":"Tobias Brockhoff , Moritz Heinlein , Georg Hubmann , Stephan Lütz , Sergio Lucia","doi":"10.1016/j.ifacol.2025.07.115","DOIUrl":"10.1016/j.ifacol.2025.07.115","url":null,"abstract":"<div><div>Model predictive control (MPC) is an advanced control strategy that can deal with general nonlinear systems and constraints but relies on accurate predictions given by a dynamic model. To satisfy constraints and improve performance despite imperfect models, robust MPC methods can be formulated. Multi-stage MPC is a robust MPC method based on the formulation of scenario trees. The resulting optimization problems can be large, as the number of scenarios considered in the tree results from the combinations of all possible uncertainties. For systems with many uncertainties, as it is the case in bioprocesses, the optimization problems become rapidly intractable. To solve this issue, heuristics are typically used to select the most relevant uncertain parameters and their range of uncertainty. In this paper, we propose a two-step approach to obtain a systematic design of multi-stage MPC controllers: First, the key uncertain parameters are extracted based on the parametric sensitivities. Second, Bayesian optimization is employed for tuning of the range of uncertainties. The approach is applied to a bioreactor simulation study. The proposed approach can avoid constraint violations that are otherwise obtained by standard MPC while being less conservative than a manually-tuned robust controller.</div></div>","PeriodicalId":37894,"journal":{"name":"IFAC-PapersOnLine","volume":"59 6","pages":"Pages 19-24"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144830787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Surrogate modeling and control optimization of batch crystallization process of β form LGA β型LGA间歇结晶过程的代理建模与控制优化

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.169

Bo Song , Tao Liu , Mingyan Zhao , Yan Cui , Yuanjun Li

引用次数: 0

A flow rate soft sensor for pumps with complex characteristics 一种用于复杂特性泵的流量软传感器

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.07.172

Sebastian Leonow, Qi Zhang, Martin Mönnigmann

引用次数: 0

Stochastic Model Predictive Control of Blood Glucose Levels using Probabilistic Meal Anticipation⁎ 使用概率进食预期的随机模型预测控制血糖水平

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI: 10.1016/j.ifacol.2025.06.009

Mohammad Ahmadasas , Mate Siket , Mudassir M. Rashid , Ali Cinar , Mustafa Bilgic