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19th ifac symposium on System Identification, SYSID 2021 - Proceedings

19th IFAC Symposium on System Identification, SYSID 2021 - P...

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19th ifac symposium on System Identification, SYSID 2021

The proceedings contain 139 papers. The topics discussed include: model order selection in robust-control-relevant system identification;learning models of model predictive controllers using gradient data;data-based model of an omnidirectional mobile robot using gaussian processes;cooperative system identification via correctional learning;identification of output-error models and an iterative optimization algorithm to size fast ancillary services for grid frequency control;on the identification of social cognitive theory models and closed-loop intervention simulations using hybrid model predictive control;identifiability of linear dynamic networks through switching modules;optimal allocation of excitation and measurement for identification of dynamic networks;and Hankel matrix-based Mahalanobis distance for fault detection robust towards changes in process noise covariance.

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Physics-derived covariance functions for machine learning in structural dynamics 19

Physics-derived covariance functions for machine learning in...

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19th ifac symposium on System Identification (SYSID)

作者： Cross, Elizabeth J. Rogers, Timothy J. Univ Sheffield Dynam Res Grp Sheffield S Yorkshire England

This paper attempts to bridge the gap between standard engineering practice and machine learning when modelling stochastic processes. For a number of physical processes of interest, derivation of the (auto)covariance is achievable. This paper suggests their use as priors in a standard Gaussian process regression as a means of enhancing predictive capability in situations where they are reflective of the process of interest. A covariance function of a linear oscillator under random load is derived and used in a regression context to predict the displacements of a vibratory system. A simulation case study is used to demonstrate the enhancement over a standard Gaussian process regression model. Copyright (C) 2021 The Authors.

关键词： Bayesian methods mechanical and aerospace estimation grey-box modelling physics-informed machine learning time-series modelling stochastic systems

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Risk Averse Model Predictive control of Bioreactors 13th

Risk Averse Model Predictive Control of Bioreactors

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

作者： Bhonsale, Satyajeet Descamps, Maurits Sbarciog, Mihaela Iuliana Sopasakis, Pantelis Van Impe, Jan Katholieke Univ Leuven Dept Chem Engn BioTeC Technol Campus GhentGebroeders Smetstr 1 B-9000 Ghent Belgium Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Ctr Intelligent Autonomous Mfg Syst Belfast Antrim North Ireland

Uncertainty is inherent in bioprocess modelling and control. Typically, uncertainties are handled using either the stochastic approach or the robust approach. Recently, the risk-averse approach, i.e., an interpolation between the stochastic and worst-case robust approach is gaining popularity. Risk-averse formulations are very useful in avoiding conservative solutions while still handling high-effect, low-probability events. In the bioreactor case considered in this paper, one such high effect low probability event is wash off caused by high feed rate or low inlet substrate concentration. A risk-averse, risk-constrained model predictive control formulation is proposed in this paper. The dynamic optimisation problem to be solved at every measurement instance is formulated using AV@R type risk objective. Similarly, probabilistic chance constraints are approximated by an AV@R-type risk constraints. The problem is then solved using the conic duality of the risk measure and an epigraphical decomposition of the nested multistage problem. Copyright (C) 2022 The Authors.

关键词： uncertainty risk-averse optimisation model predictive control bioreactor control

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Fitting nonlinear models to continuous oxygen data with oscillatory signal variations via a loss based on Dynamic Time Warping 13th

Fitting nonlinear models to continuous oxygen data with osci...

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

作者： Aizpuru, Judit Kemmer, Annina Karolin Kim, Jong Woo Born, Stefan Neubauer, Peter Bournazou, Mariano N. Cruz Barz, Tilman Tech Univ Berlin Chair Bioproc Engn Str 17 Juni 135 D-10623 Berlin Germany DataHow AG Zurichstr 137 CH-8600 Dubendorf Switzerland AIT Austrian Inst Technol GmbH Ctr Energy Giefingasse 2 A-1210 Vienna Austria

High throughput experimental systems play an important role in bioprocess development, as they provide an efficient way of analysing different experimental conditions and perform strain discrimination in previous phases to the industrial scale production. In the millilitre scale, these systems are combinations of parallel mini-bioreactors, liquid handling robots and automated workflows for data handling and model based operation. For successfully monitoring cultivation conditions and improving the overall process quality by model-based approaches, a proper model identification is crucial. However, the quality and amount of measurements makes this task challenging considering the complexity of the bio-processes. The Dissolved Oxygen Tension is often the only measurement which is available online, and therefore, a good understanding of the errors in this signal is important for performing a robust estimation. Some of the expected errors will provoke uncertainties in the time-domain of the measurement, and in those cases, the common Weighted Least Squares estimation procedure can fail providing good results. Moreover, these errors will have even a larger effect in the fed-batch phase where bolus feeding is applied, as this generates fast dynamic responses in the signal. In the present work, an insilico study of the performance of Weighted Least Squares estimator is analysed when the expected time-uncertainties are present in the oxygen signal. As an alternative, a loss based on the Dynamic Time Warping measure is proposed. The results show how this latter procedure outperforms the former reconstructing the oxygen signal, and in addition, returns less biased parameter estimates. Copyright (C) 2022 The Authors.

关键词： Dissolved Oxygen Tension Nonlinear system identification Dynamic time warping Estimation and control in biological systems Time series modelling

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A lab equipped with industrial DCS for different control engineering courses 13

A lab equipped with industrial DCS for different control eng...

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

作者： Balke, Carina Voeltzer, Klaas Dittmar, Rainer West Coast Univ Appl Sci Heide Germany

The paper describes the setup and operation of a control education lab which is used in different courses at both the undergraduate and graduate levels in electrical engineering and building systems programs. Its main characteristic is the equipment with several industrial-scale Distributed control systems (DCS) and the use of commercial software for its design and operation. Although procurement and maintenance costs are relatively high, the use of the lab across different study programs and courses results in high lab utilization and reasonable cost per individual lab. Some of the student projects are described in detail, among them the design of a DCS integrated Model Predictive control (MPC) solution. The lab setup reduces the necessary system-specific training time for students and leads to a more efficient lab organization. Students appreciate the use of industry-related installation since it helps them to carry out a smooth transition from academia to industry. Copyright (C) 2022 The Authors.

关键词： Distributed control System control engineering lab course curriculum student project

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Coordinated control design for steering and torque-vectoring in Model-Free control structure 11th

Coordinated control design for steering and torque-vectoring...

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

作者： Hegedus, Tamas Fenyes, Daniel Nemeth, Balazs Szabo, Zoltan Gaspar, Peter Inst Comp Sci & Control SZTAKI Eatvos Lorand Res Network ELKH Budapest Hungary Budapest Univ Technol & Econ Dept Control Transportat & Vehicle Syst Stoczek U 2 H-1111 Budapest Hungary

Model-Free control (MFC) approach is a novel technique to handle nonlinearities and uncertainties in control systems in order to provide enhanced performance level. The design of MFC is based on an ultra-local model, which is an approximation of the dynamics of the controlled system for a short period of time. This structure also involves a model-based robust control, which guarantees the accurate tracking performance of the closed-loop system. In this paper, a novel technique is proposed for designing the robust control for taking into account the varying characteristics of the ultra-local model. A novel modeling method is presented, which combines the original plant of the controlled system and the ultra-local model. Then, a robust control design is proposed, by which the tracking performance and the robustness of the closed-loop system can be guaranteed. The design steps and effectiveness of the proposed control strategy is demonstrated through a vehicle-oriented control problem using the high-fidelity simulation software, CarMaker. Copyright (C) 2022 The Author.

关键词： model-free control torque vectoring automated vehicles robust control

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Actuator fault-tolerant iterative learning control of the magnetic brake system 11th

Actuator fault-tolerant iterative learning control of the ma...

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

作者： Patan, Krzysztof Patan, Maciej Univ Zielona Gora Inst Control & Computat Engn Ul Szafrana 2 PL-65419 Zielona Gora Poland

Design of fault-tolerant control for nonlinear systems is usually focused on maximizing some criteria defining the level of robustness with respect to potential faults without significant degradation of the system performance. Therefore, strong interest is produced by compromise approaches which would generate decent control quality for the broadest possible class of potential faulty scenarios. Here, an approach is proposed with the application to magnetic brake system making use of the repetitive character of the control task. The concept of iterative learning control driven by measurement data is utilized to properly update the control signal in order to adapt to possible actuator fault states. A learning controller is adopted build on a mixture of neural networks for various operating points. Thus, it is able to adapt to changing working conditions of the device. Moreover, using the procedure employing an ensemble of inverse models, actuator faults can be successfully accommodated. The paper provides the complete iterative learning procedure including the system identification, fault estimation and fault-tolerant control. The numerical example on the reference tracking problem for magnetic brake system is discussed, taking into account various faulty scenarios. Copyright (C) 2022 The Authors.

关键词： Fault-tolerant control iterative learning control fault accommodation neural-network models actuators identification abrupt faults

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Real-Time Estimation of Lung Model Parameters and Breathing Effort During Assisted Ventilation 11th

Real-Time Estimation of Lung Model Parameters and Breathing ...

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

作者： Schauer, Thomas Simanski, Olaf Tech Univ Berlin Control Syst Grp D-10587 Berlin Germany Univ Appl Sci Technol Business & Design Hsch Wismar Computat Engn & Automat Grp CEA D-23966 Wismar Germany

The estimation of lung mechanics' parameters and the patient's residual volitional breathing effort is a prerequisite to adjust the parameters of assisted ventilation in a patient-individual manner. A real-time capable approach is investigated that estimates the resistance and compliance of a first-order lung model in conjunction with the intrapleural pressure in real-time. Latter is a measure for the patient's breathing effort. A signal generator model in the form of a Radial Basis Function (RBF) network is assumed for the intrapleural pressure. The Gaussian basis functions are periodic with the breathing cycle duration. This approach does not restrict the signal form of the patient-driven pressure curve. Recursive Least Squares (RLS) with selective forgetting is employed to consider the different dynamics of the estimated model parameters. A time-discrete version of the lung model is used for RLS. Computer simulations reveal that the approach is feasible and that selective forgetting is necessary to obtain satisfactory estimation results. Copyright (C) 2021 The Authors.

关键词： System Identification Assisted Mechanical Ventilation Input Estimation biomedical Technology Real-Time Estimation Simulation Study

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Linking process and metabolic modelling for the estimation of carbon flux distribution in Corynebacterium glutamicum growth in spent sulfite liquor 13th

Linking process and metabolic modelling for the estimation o...

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

作者： Lira-Parada, Pedro A. Sinner, Peter Kohlstedt, Michael Kager, Julian Wittmann, Christoph Herwig, Christoph Bar, Nadav Norwegian Univ Sci & Technol Dept Chem Engn N-7491 Trondheim Norway Saarland Univ Inst Syst Biotechnol Campus A1-5 D-66123 Saarbrucken Germany Tech Univ Wien Inst Chem Environm & Biosci Engn Gumpendorfer Str 1a A-1060 Vienna Austria

Process monitoring in microbial cultures became feasible thanks to the development of accurate measurement devices, including in-situ probes to monitor biomass growth, oxygen, carbon dioxide and sugar consumption. In comparison, estimating the metabolic fluxes of the cell factories still rely on analysis based on an under-determined set of equations, and requires an expensive and time consuming methods of verification. This problem intensifies in the presence of complex substrates, in which different sugars are utilized in parallel by the cell factories. In the present study, a growth experiment of Corynebacterium glutamicum in spent sulfite liquor was studied. The bioprocess was monitored during batch and fed-batch phases, and a parameter estimation routine was conducted to define a process model and the corresponding uptake rates. A tracking optimization algorithm minimized the error between the measured process fluxes and the equivalent fluxes of the elementary flux modes. The results indicate that the optimization technique obtained a set of elementary modes that are closer to reality than the computed from the metabolic analysis. Taken together, we show that an online estimation of metabolic flux distribution of C. glutamicum based on a set of process measurement signals was possible with an optimization function that links the process and metabolic model. The procedure can be complementary to the sophisticated and expensive C-13 NMR experimental analytical technique. Copyright (C) 2022 The Authors.

关键词： Bioprocess control microbial culture soft sensors parameter estimation optimization model identification

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Detection of Different COVID-19 Pneumonia Phenotypes with Estimated Alveolar Collapse and Overdistention by Bedside Electrical Impedance Tomography 11th

Detection of Different COVID-19 Pneumonia Phenotypes with Es...

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

作者： Chen, Rongqing Lovas, Andras Benyo, Balazs Moller, Knut Furtwangen Univ Inst Tech Med Jakob Kienzle Str 17 Villingen Schwenningen Germany Kiskunhalas Semmelweis Hosp Dept Anaesthesiol & Intens Therapy Dr Monszpart L U 1 H-6400 Kiskunhalas Hungary Budapest Univ Technol & Econ Dept Control Engn & Informat Technol Magyar Tudosok Krt 2 H-1117 Budapest Hungary

COVID-19 induced acute respiratory distress syndrome (ARDS) could have two different phenotypes, which was reported to have different response and outcome to the typical ARDS positive end-expiration pressure (PEEP) treatment. The identification of the different phenotypes in terms of the recruitability can help improve the patient outcome. In this contribution we conducted alveolar overdistention and collapse analysis with the long term electrical impedance tomography monitoring data on two severe COVID-19 pneumonia patients. The result showed different patient reactions to the PEEP trial, revealed the progressive change in the patient status, and indicted a possible phenotype transition in one patient. It might suggest that EIT can be a practical tool to identify phenotypes and to provide progressive information of COVID-19 pneumonia. Copyright (C) 2021 The Authors.

关键词： biomedical imaging systelits decision support systems for the control of physiological and clinical variables control of voluntary movements respiration locomotion

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