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ProcessBERT: A Pre-trained Language Model for Judging Equivalence of Variable Definitions in Process Models 13th

ProcessBERT: A Pre-trained Language Model for Judging Equiva...

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13th ifac symposium on Dynamics and control of Process systems, including Biosystems (DYCOPS)

作者： Kato, Shota Kanegami, Kazuki Kano, Manabu Kyoto Univ Dept Syst Sci Sakyo Ku Yoshida Honmachi Kyoto 6068501 Japan

Digital twins are expected to play a pivotal role in digital transformation. Although process informatics has attracted much attention, physical models are essential to realizing the digital twins. However, building a physical model of an industrial process takes much toil. We aim to facilitate the physical model building by developing an automated physical model building AI, named AutoPMoB, which performs five tasks: 1) retrieving documents about a target process from literature databases, 2) converting the format of each document to HTML format, 3) extracting information required for building a physical model from the documents, such as variables, equations, and experimental data, 4) judging the equivalence of the information extracted from different documents, and 5) reorganizing the information to output a desired physical model. This study focuses on task 4, especially judging the equivalence of variable definitions, i.e., whether two noun phrases represent the same variable. We created a large-scale corpus consisting of papers on chemical engineering, and built ProcessBERT, which is a domain-specific language model pre-trained on the corpus. We proposed a method for judging the equivalence of variable definitions based on ProcessBERT. When judging the equivalence, our proposed method first uses ProcessBERT to obtain the embeddings of the variable definitions. Then, the method calculates the cosine similarity between the embeddings. The method judges that the two definitions are equivalent when the similarity is larger than a threshold. Our proposed method judged the equivalence with higher accuracy than the method based on original BERT and SciBERT. Copyright (C) 2022 The Authors.

关键词： Artificial intelligence Physical models Process models modelling systems engineering Natural languages

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Hybrid Model Predictive control for Hybrid Electric Vehicle Energy Management Using an Efficient Mixed-Integer Formulation 13th

Hybrid Model Predictive Control for Hybrid Electric Vehicle ...

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13th ifac symposium on Dynamics and control of Process systems, including Biosystems (DYCOPS)

作者： Jung, Hyein Oh, Tae Hoon Park, Hyun Min Lee, Heeyun Lee, Jong Min Seoul Natl Univ Chem & Biol Engn Dept Seoul South Korea Hyundai Motor Co Electrifcat Control Dev Team 1 Hwaseong Si 18280 South Korea

This paper proposes to use hybrid model predictive control (HMPC) for energy management in hybrid electric vehicle (HEV) using an efficient formulation. HEV has two sources of energy - electric motor and internal combustion engine (ICE) - allowing it an additional degree of freedom to optimize the ratio between the use of two energy sources. HEV energy management is crucial to exploit its potential to reduce fuel consumption and emissions. However, it is challenging to achieve the optimal solution along with the fast dynamics of HEVs. In this paper, instead of using a nonlinear, computationally expensive dynamic model of HEV, a piecewise afne (PWA) model is used to depict its behavior, implemented with multiple linear regression. The validation of the developed model is taken with standard drive cycles. Based on the PWA model, an optimal control problem of HMPC was formulated as mixed integer linear programming (MILP). Conventionally, mixed logical dynamical (MLD) system has been used in the process control field, including early studies of HEV control. This paper applies Big-M formulation which is efficient in its problem size and tight inequality relation for integer variables. The performance of the HMPC controller was examined in a simulated environment based on MATLAB/Simulink HEVP2 application. As a result, HMPC shows superior control performance than the equivalent consumption minimization strategy (ECMS). Copyright (c) 2022 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： Model predictive control Dynamic modelling andsimulation for control andoperation Process control

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Customized Analytic Center Cutting Plane Methods for the Discrete-Time Integral Quadratic Constraint Problem 10th

Customized Analytic Center Cutting Plane Methods for the Dis...

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10th ifac symposium on Robust control Design (ROCOND)

作者： Abou Jaoude, Dany Farhood, Mazen Amer Univ Beirut Dept Mech Engn Beirut Lebanon Virginia Tech Dept Aerosp & Ocean Engn Blacksburg VA 24061 USA

This paper deals with the design of efficient algorithms for solving analysis problems formulated in the integral quadratic constraint (IQC) framework. The oracle for the discrete-time IQC problem is coupled with the analytic center cutting plane method (ACCPM), and the implementation of the ACCPM is customized to leverage promising features of the oracle such as the possibility of generating strictly separating hyperplanes between the candidate solution and feasible set and the possibility of generating more than one separating hyperplane. Such customizations may be needed if the ACCPM is to be used as part of recursive IQC synthesis algorithms for practical problems. Numerical examples are generated to compare various versions of the ACCPM, namely, with single center cuts, single deep cuts, and two center cuts. Copyright (C) 2022 The Authors. paper deals with the design of efficient algorithms for solving analysis problems formulated in the integral quadratic constraint (IQC) framework. The oracle for the discretetime IQC problem is coupled with the analytic center cutting plane method (ACCPM), and the implementation of the ACCPM is customized to leverage promising features of the oracle such as the possibility of generating strictly separating hyperplanes between the candidate solution and feasible set and the possibility of generating more than one separating hyperplane. Such customizations may be needed if the ACCPM is to be used as part of recursive IQC synthesis algorithms for practical problems. Numerical examples are generated to compare various versions of the ACCPM, namely, with single center cuts, single deep cuts, and two center cuts. Copyright (C) 2022 The Authors.

关键词： Integral quadratic constraints robustness analysis analytic center cutting plane method discrete-time control

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Advantages of flexible aircraft model based FDI 11th

Advantages of flexible aircraft model based FDI

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11th International-Federation-of-Automatic-control (ifac) symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Patartics, Balint Vanek, Balint Inst Comp Sci & Control Kende U 13-17 H-1111 Budapest Hungary

Model based Fault Detection and Isolation (FDI) in aerospace have received considerable attention and numerous algorithms are flying on-board commercial aircraft. Most of these algorithms are based on local models of aircraft components or rigid body aircraft models. On the other hand the ever more pressing requirement of performance and fuel efficiency improvement makes aircraft structures more lightweight and flexible. Actuator fault detection especially ailerons in the wing has to account for these flexible effects to provide the required performance. Within the article we show the trend how decreasing detection time necessitates the use of flexible dynamics based FDI, and show how detection and isolation performance are changing. Moreover, the article also highlights the need of robust filter synthesis methods, since a nominal H-infinity synthesis has significantly lower performance in worst-case situation than a robust mu synthesis based filter. The methods are demonstrated on a flexible aircraft demonstrator platform simulation model from the FLiPASED *** (c) 2022 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： fault detection flexible aircraft robust filtering

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A new method for fault detection in a free model context 11th

A new method for fault detection in a free model context

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11th International-Federation-of-Automatic-control (ifac) symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)

作者： Ziane, M. Ait Join, C. Pera, M. C. Steiner, N. Yousfi Benne, M. Damour, C. Univ Reunion Energy Lab Rue Cassin F-97715 St Denis France Univ Bourgogne Franche Comte FCLAB FEMTO ST Instiute CNRS Rue Thierry Mieg F-90010 Belfort France Univ Lorraine CRAN CNRS UMR 7039 BP 239 F-54506 Vandoeuvre Les Nancy France

This paper presents a new method of fault detection based on residual signal generation. Most of the existing diagnostic methods that use the residual to detect a failure are often based on the knowledge of the system model. The developed method does not require a precise knowledge or deep information about the system model. It is based on the reconstruction of the system output via an ultra-local model and a model-free controller. The reconstructed/estimated output is used to build the residual signal which is the fault indicator. Several simulation tests have been performed to evaluate the potential of the proposed approach for fault diagnosis. A fault on an actuator of the system is simulated in linear, non-linear and multi-input non-linear case studies. The simulation results reveal that the fault is successfully detected for all these systems under a noisy environment. Copyright (C) 2022 The Authors.

关键词： Fault detection Actuator fault Model Free control Model Free Fault Detection

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Direct Discretized Kernel Identification for Continuous Agglomeration Processes 13th

Direct Discretized Kernel Identification for Continuous Aggl...

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13th ifac symposium on Dynamics and control of Process systems, including Biosystems (DYCOPS)

作者： Otto, Eric Maksakov, Anton Duerr, Robert Palis, Stefan Kienle, Achim Otto von Guericke Univ Automat Modelling Univ Pl 2 D-39106 Magdeburg Germany Max Planck Inst Dynam Complex Tech Syst Proc Synth & Dynam Sandtorstr 1 D-39106 Magdeburg Germany Magdeburg Stendal Univ Appl Sci Engn Math Breitscheidstr 2 D-39114 Magdeburg Germany Natl Res Univ Moscow Power Engn Inst Krasnokazarmennaya Ulitsa 14 Moscow 111250 Russia

Continuous particle agglomeration processes are important size-enlargement unit operations applied in the food, pharmaceutical and agricultural industry. For the improvement of these processes predictive mathematical models are of utmost importance. A widely applied modeling framework is the population balance equation, where the agglomeration kinetics are described by the so-called agglomeration kernel. The identification of functions describing these kinetics has turned out to be a challenging task. Therefore, this article deals with identifying such a kernel function by minimizing the L2-residual between experimentally obtained particle size distributions and simulations. The application of a stochastic gradient descent algorithm with automatic differentiation for minimization allows for the direct identification of the high-dimensional matrix representing the discretized kernel function. The comparison between the simulated and measured size distribution shows that the identified kernel is able to accurately describe the evolution of the particle size distribution. The algorithm presented in this contribution can be applied to a variety of similar processes and the identified kernels can be used in process optimization and automation applications. Copyright (c) 2022 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： Process modeling andidentification Fluidized bed processes Agglomeration processes Agglomeration kernel Optimization based identification

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Constrained learning for model predictive control in asymptotically constant reference tracking tasks 19

Constrained learning for model predictive control in asympto...

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19th ifac symposium on System Identification (SYSID)

作者： Matschek, Janine Himmel, Andreas Findeisen, Rolf Otto Von Guericke Univ Inst Automat Engn Magdeburg Germany

There is a steadily increasing demand for full and partial autonomous operation of systems. One way to achieve autonomy for systems is the fusion of classical control approaches with methods from machine learning and artificial intelligence. We consider machine learning approaches to learn unknown or partially known references to increase the autonomy and performance of control systems for reference trajectory tracking. To improve learning and provide guarantees, we incorporate system properties such as constraints and the system dynamics in the learning algorithm. In particular, Gaussian processes are used to support a model predictive control scheme that exploits the predicted learned reference. Recursive feasibility and stability is established, and improved performance is illustrated considering a chemical process and asymptotically constant references. Copyright (C) 2021 The Authors.

关键词： Machine learning model predictive control Gaussian processes learning-supported control

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Abstraction in teaching ways of control engineering to support the understanding of mathematics behind Industry 4.0-a Hungarian approach 13

Abstraction in teaching ways of control engineering to suppo...

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13th ifac symposium on Advances in control Education (ACE)

作者： Tar, Jozsef Varga, Bence Galambos, Peter Szucs, Laszlo Siket, Mate Varga, Arpad Eigner, Gyorgy Kovacs, Levente Obuda Univ Physiol Controls Res Ctr Becsi St 96-B H-1034 Budapest Hungary Obuda Univ Bejczy Antal Ctr Intelligent Robot Becsi St 96-B H-1034 Budapest Hungary Obuda Univ John von Neumann Fac Informat Biomat & Appl Artificial Intelligence Inst Becsi St 96-B H-1034 Budapest Hungary Obuda Univ Appl Informat & Appl Math Doctoral Sch Becsi St 96-B H-1034 Budapest Hungary Obuda Univ Kalman Kando Fac Elect Engn Tavaszmezo St 15-17 H-1084 Budapest Hungary

Modern technologies, as well as the technologies that will emerge in the near future, pose a great challenge to researchers and educators in education and educational methodology. Teaching the intuitive use of mathematical tools, developing the ability to abstract, using appropriate theoretical and practical models and demonstration tools are all required to equip engineering students with the right mindset and practice to meet the challenges of the modern era. In this paper, we present the theoretical and practical tools that are used in daily practice in the context of control engineering to help engineering students develop their mindset. First, the development of an appropriate mathematical toolbox and mindset is introduced, followed by the development of abstraction skills during the teaching process. Interdisciplinary, mechanical and robotic models and tools are used to educate students about the complexity of engineering. The paper will start from mechanics, then present an interdisciplinary example in the field of blood glucose control, and, in addition, highly nonlinear demonstration tools will be presented, and a mobile robot platform for education, developed in collaboration with engineering students, will be presented, which is suitable for teaching in the fields of sensor fusion, artificial intelligence and others. Copyright (C) 2022 The Authors.

关键词： control education Teaching curricula developments for control and other engineers control education using laboratory equipment Continuing control education in industry Nonlinear system identification Stability of nonlinear systems Robotics technology Estimation and control in biological systems

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Stripping Torque Model for Bone Screws 11th

Stripping Torque Model for Bone Screws

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11th ifac symposium on Biological and Medical systems (BMS)

作者： Wilkie, Jack Docherty, Paul D. Moeller, Knut Hsch Furtwangen Inst Tech Med Villingen Schwenningen Germany Univ Canterbury Ctr Bioengn Christchurch New Zealand

Correct torquing of bone screws is critical for positive patient outcomes in orthopaedic surgery. Under- or over-tightening screws can lead to thread stripping or screw loosening, leading to implant/fixation failure and potential tissue damage or disability. It has been proposed that an automated torque-limiting smart-screwdriver may be able to use model-based methods to determine the properties of bone as screws are inserted, and then use these to determine the optimal tightening torque and provide a torque-indication or -limitation to enforce this limit. Previous work focused on identifying the material properties from sensor data, but this paper will address the unanswered question of torque-limit prediction. Here we have developed a simple model of screw thread stripping. This model is based on the assumption that overtightening the screw will shear a cylindrical section of the underlying material. This simple assumption is augmented with a stress concentration factor dependant on the screw geometry. This model was tested against experimental stripping-torque data. We found that without the stress-concentration factor the model produced predicted torques with a strong linear relationship to the experimental values (R-2 = 0.98), however the magnitude of the predictions was 2-3 times too high. Including the stress concentration factor brought these predictions into the range of the experimental values, but the strong linear relationship from before was disrupted (R-2 = 0.80). Overall, this approach is promising for optimal torque prediction, but needs more thorough testing with a range of materials and screws, and has room for improvement with the stress-concentration factor. Copyright (C) 2021 The Authors.

关键词： modelling biomedical control Orthopaedic surgery Bone screws Biomechanics

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Data Informativity for the Closed-Loop Identification of MISO ARX systems 19

Data Informativity for the Closed-Loop Identification of MIS...

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19th ifac symposium on System Identification (SYSID)

作者： Colin, Kevin Bako, Laurent Bombois, Xavier KTH Royal Inst Technol Div Decis & Control Syst Stockholm Sweden Univ Lyon Lab Ampere Ecole Cent Lyon UMR CNRS 5005 Lyon France Ctr Natl Rech Sci CNRS Paris France

In the Prediction Error identification framework, it is crucial that the collected data are informative with respect to the chosen model structure to get a consistent estimate. In this work, we focus on the data informativity property for the identification of multi-inputs single-output ARX systems in closed-loop and we derive a necessary and sufficient condition to verify if a given multisine external excitation combined with the feedback introduced by the controller yields informative data with respect to the chosen model structure. Copyright (C) 2021 The Authors.

关键词： System Identification Data Informativity Prediction Error Method Consistency

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