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On the Verification of Distributed control for Multi Job Shop Assignment Problem in Smart Manufacturing System 18

On the Verification of Distributed Control for Multi Job Sho...

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18th ifac symposium on Information control Problems in Manufacturing (INCOM)

作者： Somma, Andrea Antons, Oliver Petrillo, Alberto Santini, Stefania Murino, Teresa Univ Naples Federico II Dept Chem Mat & Ind Engn Ple Tecchio I-80125 Naples Italy Otto von Guericke Univ D-39106 Magdeburg Germany Univ Naples Federico II Dept Elect Engn & Informat Technol Naples Italy

The increasing need of product individualization and rapid demand fluctuation leads industrial manufacturers to leveraging new control solutions for production planning and control. Among these, distributed control approaches for the multi job shop scheduling problem could be a promising solution to make smart manufacturing systems more responsive to the market. In this context, this paper aims at investigating the effectiveness and the benefits of distributed control approaches for solving the multi job shop assignment problem on a real-world smart manufacturing process involving the production of high-vacuum solar panels. The validation is carried out by leveraging Cyber-Physical systems modelling and a digital model-based procedure which, interacting with the physical plant, provides the proper job assignment in order to optimize the overall makespan and work-in-progress in a fully-distributed fashion. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Smart Manufacturing Planning and control Multi-Job Shop Scheduling Distributed control Digital-Model-Based Simulation

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Machine learning multi-step-ahead modelling with uncertainty assessment 12

Machine learning multi-step-ahead modelling with uncertainty...

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12th ifac symposium on Advanced control of Chemical Processes (ADCHEM)

作者： Costa, Erbet Almeida Rebello, Carine Menezes Santana, Vinicius Viena Nogueira, Idelfonso B. R. Norwegian Univ Sci & Technol Dept Chem Engn Trondheim Norway

This study presents a strategy for multi-step-ahead identification of robust machine learning (ML). Hence, we focus on the disparity between standard single-step prediction models and the requirement for multi-step forecasting, which is crucial for Model Predictive and Optimization schemes. This work explores how the proposed muti-step-ahead strategy can diminish the prediction uncertainty compared to the traditional single-step approach. The paper evaluates the multi-step identification with uncertainty assessment in different model architectures, including those based on recursive neural networks. A key aspect of the analysis is the application of these models to a polymerization reactor, a standard benchmark in algorithm evaluation. The results reveal that multi-step recursive models significantly reduce prediction uncertainty compared to single-step models, particularly when feedback mechanisms are involved. This study highlights the advantages of multi-step models and their potential benefits for control and optimization schemes. Copyright (C)2024 The Authors. This is an open access article under the CC BY-NC-ND license (htips://***/licenses/by-nc-nd/4.0/)

关键词： Machine Learning Assisted Modeling Estimation and Robust Estimation Model Predictive control

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Requirements Structure for System Requirements Formal modelling, Verification and Validation 18

Requirements Structure for System Requirements Formal Modell...

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18th ifac symposium on Information control Problems in Manufacturing (INCOM)

作者： Bacquet, Cyril Marange, Pascale Bonjour, Eric Kerbrat, Alain Univ Lorraine Lab ERPI 8 Rue Bastien Lepage F-54000 Nancy France Univ Lorraine Lab CRAN Fac Sci & Technol Campus Aiguillettes BP 70239 F-54506 Vandoeuvre Les Nancy France Airbus 26 Chem Espeissiere F-31300 Toulouse France

Requirements engineering (RE) is essential for system design because incorrect or incomplete RE can lead to misunderstandings, gaps, and mistakes that can negatively affect projects. Higher-quality requirements can reduce errors. However, verification and validation (V&V) of requirements qualities is challenging. Formal modeling enables automation for early V&V of large requirements sets. The use of patterns improves requirements quality, but gaps still exist in formal modeling and V&V methodologies. This paper presents a system requirements conceptual model with requirements writing patterns that enable the systems requirements modeling and V&V, along with a comparative analysis with existing concepts in the literature. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Requirements Boilerplates Pattern Model Verification Validation

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Weighted Least-Squares PARSIM 20

Weighted Least-Squares PARSIM

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20th ifac symposium on System Identification (SYSID)

作者： He, Jiabao Rojas, Cristian R. Hjalmarsson, Hakan KTH Royal Inst Technol Sch Elect Engn & Comp Sci Div Decis & Control Syst S-10044 Stockholm Sweden

Subspace identification methods (SIMs) have proven very powerful for estimating linear state-space models. To overcome the deficiencies of classical SIMs, a significant number of algorithms has appeared over the last two decades, where most of them involve a common intermediate step, that is to estimate the range space of the extended observability matrix. In this contribution, an optimized version of the parallel and parsimonious SIM (PARSIM), PARSIM(opt), is proposed by using weighted least-squares. It not only inherits all the benefits of PARSIM but also attains the best linear unbiased estimator for the above intermediate step. Furthermore, inspired by SIMs based on the predictor form, consistent estimates of the optimal weighting matrix for weighted least-squares are derived. Essential similarities, differences and simulated comparisons of some key SIMs related to our method are also presented. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Subspace identification ARX model Markov parameters weighted least-squares

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Supply chain modelling and LARG performance evaluation under severe disruptions: the case of the fast-food industry 18

Supply chain modelling and LARG performance evaluation under...

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18th ifac symposium on Information control Problems in Manufacturing (INCOM)

作者： Rinaldi, Marta Petrillo, Vincenzo Fera, Marcello Caterino, Mario Bottani, Eleonora Macchiaroli, Roberto Univ Campania Lugi Vanvitelli Dept Engn Aversa CE Italy Univ Salerno Dept Ind Engn Fisciano SA Italy Univ Parma Dept Engn & Architecture Parma Italy

the LARG paradigm involves and combines different perspectives: Lean, Agile, Green, and Resilience. These four perspectives are recognized as a suitable response to the strong demand fluctuations and characteristics of modern variables and unpredictable markets. Their integration into modern supply chains is also crucial from a strategic point of view. This study develops a simulation model useful for measuring and evaluating the LARG performance of a supply chain operating in the fast-food industry. Different configurations have been tested varying the number of players and the target service level. Then, different scenarios with distortions have been compared. Results show that the service level influences the LARG performance regardless of the kind of disruption. The supply chain structure can affect LARG performance when the demand increases, while it seems not relevant if the demand decreases. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： supply chain discrete event simulation lean agile resilient green food disruption

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Space-Filling Input Design for Nonlinear State-Space Identification 20

Space-Filling Input Design for Nonlinear State-Space Identif...

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20th ifac symposium on System Identification (SYSID)

作者： Kiss, Mate Toth, Roland Schoukens, Maarten Eindhoven Univ Technol Control Syst Grp Eindhoven Netherlands Inst Comp Sci & Control Syst & Control Lab Budapest Hungary

The quality of a model resulting from (black-box) system identification is highly dependent on the quality of the data that is used during the identification procedure. Designing experiments for linear time-invariant systems is well understood and mainly focuses on the power spectrum of the input signal. Performing experiment design for nonlinear system identification on the other hand remains an open challenge as informativity of the data depends both on the frequency-domain content and on the time-domain evolution of the input signal. Furthermore, as nonlinear system identification is much more sensitive to modeling and extrapolation errors, having experiments that explore the considered operation range of interest is of high importance. Hence, this paper focuses on designing space-filling experiments i.e., experiments that cover the full operation range of interest, for nonlinear dynamical systems that can be represented in a state-space form using a broad set of input signals. The presented experiment design approach can straightforwardly be extended to a wider range of system classes (e.g., NARMAX). The effectiveness of the proposed approach is illustrated on the experiment design for a nonlinear mass-spring-damper system, using a multisine input signal. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licneses/by-nc-nd/4.0/)

关键词： Experiment Design Nonlinear System Identification State-Space

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Calendering Process MPC using recursive DMDc 20

Calendering Process MPC using recursive DMDc

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20th ifac symposium on System Identification (SYSID)

作者： Hamdan, Taleb Bou Mercere, Guillaume Dairay, Thibault Coirault, Patrick Poitiers Univ Lab Automat & Informat Syst Poitiers France Manufacture Francaise Pneumat Michelin Clermont Ferrand France Univ Paris Saclay CNRS ENS Paris Saclay Ctr Borelli Gif Sur Yvette France

Ensuring high quality of various layers within a tire demands efficient process control. Rubber calendering does not depart from this important evolution of the tire machinery world. Among the numerous variables and parameters involved in calendering, specific attention is given here to the model predictive control of the rubber temperature. This focus is driven by two key factors: the highly nonlinear dynamics of rubber temperature and the stringent temperature constraints inherent in manufacturing processes. Our rubber temperature control solution combines a constrained MPC algorithm with a time-varying model generated using a recursive form of the well-established Dynamic Mode Decomposition with control (DMDc) method. As demonstrated subsequently, this online approach produces a reduced-order, time-varying state-space representation capable of accurately approximating the nonlinear dynamics of rubber temperature. The entire algorithm has been successfully tested using simulated data derived from a high fidelity simulator replicating a calendering process. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licneses/by-nc-nd/4.0/)

关键词： data driven model learning recursive identification model reduction model predictive control

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Insulin on Board safety constraint effect in a CHoKI-based MPC for Artificial 12

Insulin on Board safety constraint effect in a CHoKI-based M...

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12th ifac symposium on Biological and Medical systems (BMS)

作者： Sonzogni, Beatrice Maria Manzano, Jose Previdi, Fabio Ferramosca, Antonio Univ Bergamo Dept Management Informat & Prod Engn Bergamo Italy Univ Loyola Andalucia Dept Engn Dos Hermanas 41704 Spain

This work presents a learning-based Model Predictive control (MPC) algorithm for the artificial pancreas able to autonomously manage basal insulin injections in type 1 diabetic patients. The main goal is to keep the blood glucose levels within the euglycemic range (70-180 mg/dL), trying to avoid hypoglycemia. To prevent this event, additional constraints are added that consider the Insulin On Board (JOB). The data collection and the testing of the proposal are performed on the virtual patients of the FDA-accepted UVA/Padova simulator. The final results seem promising since the proposed controller reduces the time in hypoglycemia with respect to the standard constant basal insulin therapy. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Artificial Pancreas MPC Learning-Based control

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Identification of Low Order systems in a Loewner Framework 20

Identification of Low Order Systems in a Loewner Framework

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20th ifac symposium on System Identification (SYSID)

作者： Honarpisheh, Arya Singh, Rajiv Miller, Jared Sznaier, Mario Northeastern Univ ECE Dept Boston MA 02115 USA MathWorks Inc 1 Apple Hill Dr Natick MA 01760 USA Swiss Fed Inst Technol Automat Control Lab IfA Dept Informat Technol & Elect Engn D ITET Phys Str 3 CH-8092 Zurich Switzerland

This paper considers the problem of non-parametric identification of low-order models from time-domain experimental data using a combination of Caratheodory Fejer and Loewner-based interpolation, followed by a Loewner matrix Balanced Reduction (LBR) step. As we show in the paper, the Loewner matrix is an estimator for the trace norm of a system, playing a role similar to the one played by the Hankel matrix. However, utilizing Zolotarev numbers to establish decay rate bounds for singular values reveals that the decay of singular values in the Loewner matrix is considerably faster than that in the Hankel matrix. Thus, Loewner-based methods yield lower order systems, with the same error bound, than comparable ones based on Hankel matrices. The effectiveness of our method is demonstrated through a numerical example. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0)

关键词： Low-rank Approximation Loewner Matrix Hankel Matrix Balanced Reduction Subspace Methods Linear systems

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Baseline Results for Selected Nonlinear System Identification Benchmarks 20

Baseline Results for Selected Nonlinear System Identificatio...

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20th ifac symposium on System Identification (SYSID)

作者： Champneys, Max Beintema, Gerben I. Toth, R. Schoukens, Maarten Rogers, Timothy J. Univ Sheffield Dynam Res Grp Sheffield S Yorkshire England Eindhoven Univ Technol Control Syst Grp Eindhoven Netherlands Inst Comp Sci & Control Syst & Control Lab Budapest Hungary

Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting improvements or extensions to existing methods. It is natural, therefore, to consider how one might choose between these competing models. Benchmark datasets provide one clear way to approach this question. However, to make meaningful inference based on benchmark performance it is important to understand how well a new method performs comparatively to results available with well-established methods. This paper presents a set of ten baseline techniques and their relative performances on five popular benchmarks. The aim of this contribution is to stimulate thought and discussion regarding objective comparison of identification methodologies. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licneses/by-nc-nd/4.0/)

关键词： Nonlinear System Identification Benchmarks NARX Nonlinear State-Space

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