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Symbolic neural networks for automated covariate modeling in a mixed-effects framework 12

Symbolic neural networks for automated covariate modeling in...

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

作者： Sundell, Jesper Wahlquist, Ylva Soltesz, Kristian Lund Univ Dept Automat Control Lund Sweden

Mixed-effects models are used to describe the inter-patient variability in drugs. Modeling of these variabilities include both fixed and random effects. Fixed effects relate covariates such as age and weight to compartment volumes and clearances, whereas random effects account for unexplained variability. Traditionally, the development of fixed effects models is an inefficient process where covariate relationships are evaluated in a step-wise manner. In this study, we implemented a symbolic neural network (SNN) to automate the development of a fixed effects model and used it to develop a population pharmacokinetic model for propofol. With the SNN, we can find covariate relationships that are traditionally not evaluated. Then, we apply random effects and estimate parameters in the standard mixed-effects modeling framework. Our final model shows comparable predictive performance to a published model for propofol, despite having fewer covariates and model parameters. 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： Neural networks mixed-effects modeling pharmacokinetics pharmacometrics covariate models

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Understanding "Just-in-Time" States in Behavioral Interventions using System Identification and Data Science Methods 20

Understanding "Just-in-Time" States in Behavioral Interventi...

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20th ifac symposium on System Identification (SYSID)

作者： El Mistiri, Mohamed Banerjee, Sarasij Rivera, Daniel E. Klasnja, Predrag Park, Junghwan Hekler, Eric Arizona State Univ Control Syst Engn Lab Tempe AZ 85281 USA Univ Michigan Sch Informat Div Biomed & Hlth Informat Ann Arbor MI 48109 USA Univ Calif San Diego Ctr Wireless & Populat Hlth Syst La Jolla CA 92093 USA

Insufficient physical activity (PA) is highly prevalent in society, despite its negative impact on personal health. Effective interventions are clearly needed. However, improvements to behavioral interventions face many challenges, from noisy and missing measurements to inadequate understanding of the dynamics of behavior change in context. In this paper, we present a comprehensive, data-driven, system identification approach aimed at overcoming challenges to better understand the dynamics of PA behavior change, which in turn can improve the efficacy of these interventions. The proposed approach consists of an innovative input signal design (aimed at providing informative data sets to study the concept of "just-in-time" dynamics), Singular Spectrum Analysis (SSA;for noise reduction and exploring the separability of the measured output signal), and Model-on-Demand (MoD) estimation, a hybrid data-driven modeling approach, which allows identifying dynamics under changing operating conditions. The proposed approach is evaluated on data for a representative participant from the JustWalk JITAI study. The results demonstrate significant potential of the methodology in enhancing the understanding of the dynamics of behavior change in context. 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： eHealth nonlinear system identification time series modelling filtering and smoothing

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Finite-frequency fuzzy fault-tolerant static output feedback H8control for Diesel engine air-path system 12

Finite-frequency fuzzy fault-tolerant static output feedback...

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12th ifac symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2024

作者： El-Amrani, A. Noura, H. El Adel, E.M. Ouladsine, M. Avenue Escadrille Normandie-Niemen Cedex 20 MarseilleF-13397 France

This paper focuses on the design of H8 finite-frequency (FF) fault-tolerant static output feedback (FTSOFC) problem of Diesel engine air-path system with consideration of external disturbances and actuator faults. Initially, a Diesel engine air-path nonlinear model is described by Takagi-Sugeno (T-S) fuzzy model based descriptor approach. The aim is to regulate intake and exhaust manifold pressures to the desired reference pressures by controlling the Geometry Turbine (VGT) and Exhaust Gas Recirculation (EGR) valves. Then, the robust integrator-based control strategy is developed to track the desired reference signals despite the presence of disturbances and actuator faults. By using the extended Generalized Kalman Yakubovich Popov (GKYP) lemma, Lyapunov functions and independent slack matrices, sufficient conditions are established to ensure both the good tracking of reference pressures and the prescribed H8 performance with FF domain of the fuel flow variation and actuator faults. Finally, simulation results are given to demonstrate the effectiveness of the proposed approach. © 2024 The Authors. This is an open access article under the CC BY-NC-ND license.

关键词： Diesel engines

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Model-Based control Algorithm For Lung and Diaphragm Protective Ventilation 12

Model-Based Control Algorithm For Lung and Diaphragm Protect...

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

作者： Lindup, K. Padula, F. Bertoni, M. Latronico, N. Visioli, A. Curtin Univ Sch Elect Engn Comp & Math Sci Perth WA Australia Curtin Univ Curtin Ctr Optimisat & Decis Sci Perth WA Australia Univ Brescia Radiol Sci & Publ Hlth Dept Med & Surg Specialties Brescia Italy Spedali Civili Brescia Brescia Italy Univ Brescia Dept Mech & Ind Engn Brescia Italy

This paper introduces an algorithm that aims to achieve Lung and Diaphragm Protective Ventilation (LDPV) by periodically adjusting the ventilator pressure support. The pressure support is adjusted using a feedback control system that considers periodic estimations of patient effort and lung stress via measurements of the Flow Index and of the Static Driving Pressure. The algorithm developed yields promising results when tested in-silico using a single compartment model;the algorithm is able to achieve and maintain defined LDPV targets in response to applied step changes in patient effort and system mechanics. 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： Mechanical ventilation Assisted ventilation Lung and diaphragm protective ventilation Flow index Pressure support

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A bias-variance perspective of data-driven control 20

A bias-variance perspective of data-driven control

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20th ifac symposium on System Identification (SYSID)

作者： Colin, Kevin Ju, Yue Bombois, Xavier Rojas, Cristian R. Hjalmarsson, Hakan KTH Royal Inst Technol Ctr Adv Bio Prod AdBIOPRO Stockholm Sweden KTH Royal Inst Technol Div Decis & Control Syst Stockholm Sweden Univ Lyon Ecole Cent Lyon UMR CNRS 5005 Lab Ampere Lyon France CNRS Paris France

Data-driven control, the task of designing a controller based on process data, finds application in a wide range of disciplines and the topic has been intensively studied over more than half a century. The main purpose of this contribution is to elucidate on the commonalities between data-driven control and parameter estimation. In particular, we discuss the bias-variance trade-of, i.e. rather than aiming for the optimal controller one should aim for a constrained version, that may be characterized by tunable parameters, corresponding to hyperparameters in parameter estimation. As a result we shift attention from indirect vs direct data driven control by highlighting the important role played by (complete) minimal sufficient statistics. To keep technicalities at a minimum, still capturing the essential features of the problem, we consider the problem of minimizing the expected control cost for a quadratic open loop control problem applied to a finite impulse response system. In a Gaussian white noise setting, the maximum-likelihood parameter estimate constitutes a complete minimal sufficient statistic which allows us to focus on controllers that are functions of this model estimate without loss of statistical accuracy. We make a systematic study of three different controller structures and two different tuning techniques and illustrate their behaviours numerically. Copyright (c) 2024 The Authors.

关键词： Data-driven control Bayes control Regularization and Kernel Methods

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Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks 20

Physics-Guided State-Space Model Augmentation Using Weighted...

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20th ifac symposium on System Identification (SYSID)

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

Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical information. However, for a classical PGNN, the penalization of the physics-guided part is at the output level, which leads to a conservative result as systems with highly similar state-transition functions, i.e. only slight differences in parameters, can have significantly different time-series outputs. Furthermore, the classical PGNN cost function regularizes the model estimate over the entire state space with a constant trade-off hyperparameter. In this paper, we introduce a novel model augmentation strategy for nonlinear state-space model identification based on PGNN, using a weighted function regularization (W-PGNN). The proposed approach can efficiently augment the prior physics-based state-space models based on measurement data. A new weighted regularization term is added to the cost function to penalize the difference between the state and output function of the baseline physics-based and final identified model. This ensures the estimated model follows the baseline physics model functions in regions where the data has low information content, while placing greater trust in the data when a high informativity is present. The effectiveness of the proposed strategy over the current PGNN method is demonstrated on a benchmark 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： System Identification Physics-Guided Neural Networks State Space

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Generative Shopfloor Layout Design: Challenges and Proposed modelling Approach 18

Generative Shopfloor Layout Design: Challenges and Proposed ...

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

作者： Cohen, Yuval Aperstein, Yehudit Tel Aviv Afeka Acad Coll Engn IL-69988 Tel Aviv Israel

This paper explores the transformative impact of generative design principles on layout design and organization. The paper provides insights into the practical use of generative design in layout environments, emphasizing the optimization of spatial arrangements, workflow efficiency, and human machine collaboration. The paper presents a novel generative approach proposed for optimizing manufacturing shop floor layouts, utilizing a three-stage process that integrates Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). The first stage focuses on exploration and learning using VAE to generate a diverse pool of solutions, guided by a fitness profile encompassing characteristics such as travel distance, adjacency score, space utilization, ergonomic score, and aesthetics. The second stage employs GANs for intensive layout improvements, introducing a competitive training process where the generator aims to produce layouts surpassing the quality of previous iterations. The third stage fine-tunes the best layouts identified by the GAN.A much more detailed model is required, and a VAE with higher resolution than the one used in the first phase. The manuscript presents a comprehensive methodology, paving the way for future research into dynamic layout adaptation in manufacturing settings. 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： Shopfloor design Plant Layout Generative design Layout design GAN

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SYSDYNET - A MATLAB App and Toolbox for Dynamic Network Identification 20

SYSDYNET - A MATLAB App and Toolbox for Dynamic Network Iden...

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20th ifac symposium on System Identification (SYSID)

作者： Van den Hof, Paul M. J. Shi, Shengling Weerts, Harm H. M. Cheng, Xiaodong Ramaswamy, Karthik R. Dankers, Arne G. Dreef, H. J. (Mannes) Fonken, Stefanie J. M. Steentjes, Tom R. V. Meijer, Job B. T. Eindhoven Univ Technol Dept Elect Engn Control Syst Grp Eindhoven Netherlands

Identification in interconnected systems requires the handling of phenomena that go beyond the classical open-loop and closed-loop type of identification problems. Over the last decade a comprehensive theory has been developed for addressing identification problems in linear dynamic networks, formulated in a module framework, where the network structure is characterized by a directed graph in which nodes are signals and links are transfer functions. The resulting methods and approaches have been collected in a MATLAB App and Toolbox, supported by an attractive graphical user interface that provides an interactive workflow for manipulating the structural properties of dynamic networks, applying basic network operations like immersion and module invariance testing, and for investigating network/module generic identifiability and selecting appropriate predictor model inputs and outputs. The workflow supports the allocation of external excitation signals (actuation) and measured node signals (sensing) so as to achieve generic identifiability and provide consistent estimation of target modules. The Toolbox includes algorithms for actual network simulation and identification. 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： System identification identifiability dynamic networks interconnected systems

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Optimal experiment design for multivariable system identification using simultaneous excitation 20

Optimal experiment design for multivariable system identific...

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20th ifac symposium on System Identification (SYSID)

作者： Sigurdsson, Gunnar Isaksson, Alf J. Lundh, Michael Hjalmarsson, Hakan Munusamy, Sudhakar ABB AB Corp Res Vasteras Sweden KTH Royal Inst Technol Stockholm Sweden ABB Global Ind & Serv Pvt Ltd Bangalore Karnataka India

Having an accurate model of a system is essential for many applications today, especially those related to advanced process control (APC). When executing an industrial delivery project of APC, often significant time is spent performing experiments on the real process to identify a model. By designing higher quality experiments the time needed on site carrying out experiments can be reduced, saving both resources and engineering efforts. This paper investigates optimal input design to minimize the time needed to identify a linear time-invariant multi-variable system fulfilling certain requirements on the model accuracy. This is an extension of previous work which focused on sequential excitation on one input signal at the time. Here, however, the effects of using combined simultaneous and sequential excitation is investigated. The resulting non-convex optimization problem is relaxed using binary decision variables. An important feature of the approach is that the experiment is carried out in closed-loop using a model predictive controller with zone constraints to further guarantee that the output constraints are not violated. Simulation results indicate that there are many cases where using combined simultaneous and sequential excitation outperforms the previous sequential approach. 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： System identification Simultaneous excitation Experiment design Model Predictive control Optimization Multi-variable systems

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Data-Driven control of Highly Interactive systems using 3DoF Model-On-Demand MPC: Application to a MIMO CSTR 20

Data-Driven Control of Highly Interactive Systems using 3DoF...

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20th ifac symposium on System Identification (SYSID)

作者： Banerjee, Sarasij Khan, Owais El Mistiri, Mohamed Nandola, Naresh N. Rivera, Daniel E. Arizona State Univ Sch Engn Matter Transport & Energy Control Syst Engn Lab Tempe AZ 85287 USA Siemens Technol Princeton NJ 08540 USA

This paper presents a Three-Degree-of-Freedom Model Predictive control (3DoF MPC) framework based on Multi-Input-Multi-Output (MIMO) "Model-on-Demand" (MoD) estimation. MoD is a data-centric weighted regression algorithm that generates local models over adaptively varying neighborhoods of changing operating conditions. The 3DoF formulation enables individualized tuning of parameters relating to setpoint tracking and measured and unmeasured disturbance rejection. Online estimation of system dynamics using MIMO MoD and augmentation with the 3DoF MPC structure allows the generation of control laws based on efficient locally linear approximations of system nonlinearities. This paper evaluates the framework through a case study involving a nonlinear MIMO Continuous Stirred Tank Reactor (CSTR) model. The MIMO CSTR system is highly interactive, making data-driven estimation and control notably more challenging than its SISO counterpart. The generation of an informative database using modified "zippered" multisines is presented. The paper concludes with a case study demonstrating the effectiveness of 3DoF MoD MPC in achieving constrained MIMO control of reactor concentration and temperature in the presence of disturbances through a flexible and intuitive approach. 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-based control nonlinear system identification time-varying systems model predictive control process control

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