检索结果-内蒙古大学图书馆

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

作者： Fliess, Michel Join, Cedric Moussa, Kaouther Djouadi, Seddik M. Alsager, Mohamed W. Ecole Polytech LIX CNRS UMR 7161 F-91128 Palaiseau France Univ Lorraine CRAN CNRS UMR 7039 BP 239 F-54506 Vandoeuvre Les Nancy France Univ Grenoble Alpes CNRS Grenoble INP GIPSA Lab F-38000 Grenoble France Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN 37996 USA AL IEN 7 Rue Maurice Barres F-54330 Vezelise France

We present some "in silico" experiments to design combined chemo- and immunotherapy treatment schedules. We introduce a new framework by combining flatness-based control, which is a model-based setting, along with model-free control. The flatness property of the used mathematical model yields straightforward reference trajectories. They provide us with the nominal open-loop control inputs. Closing the loop via model-free control allows to deal with the uncertainties on the injected drug doses. Several numerical simulations illustrating different case studies are displayed. We show in particular that the considered health indicators are driven to the safe region, even for critical initial conditions. Furthermore, in some specific cases there is no need to inject chemotherapeutic agents. Copyright (C) 2021 The Authors.

关键词： biomedical control cancer nonlinear control fault accommodation flatness-based control model-free control shooting method

来源：评论

学校读者我要写书评

暂无评论

Model Order Selection in Robust-control-Relevant System Identification 19

Model Order Selection in Robust-Control-Relevant System Iden...

引用

19th ifac symposium on System Identification (SYSID)

作者： Tacx, Paul de Rozario, Robin Oomen, Tom Eindhoven Univ Technol Control Syst Technol Grp Eindhoven Netherlands

Robust control allows for guaranteed performance for a range of candidate models. The aim of this paper is to investigate the role of model complexity in the identification of model sets for robust control. A key point is that model quality and model complexity should be evaluated with respect to the control goal. Regularization using a worst-case control criterion in conjunction with a specific model uncertainty structure allows robust control of multivariable systems using accurate models with low complexity. Simulations confirm that the model order should be selected in view of the control objectives. Overall, the framework allows for systematic identification of model sets for robust control. Copyright (C) 2021 The Authors.

关键词： Identification for control Robust control Motion control Mechatronic systems Frequency domain identification Identification and control methods Order selection

来源：评论

学校读者我要写书评

暂无评论

Online Hypermodel-based Path Planning for Feedback control of Tissue Denaturation in Electrosurgical Cutting 11th

Online Hypermodel-based Path Planning for Feedback Control o...

引用

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

作者： El-Kebir, Hamza Lee, Yongseok Berlin, Richard Benedetti, Enrico Giulianotti, Pier C. Chamorro, Leonardo P. Bentsman, Joseph Univ Illinois Dept Aerosp Engn Urbana IL 61801 USA Univ Illinois Dept Mech Sci & Engn Urbana IL 61801 USA Carle Hosp Trauma Surg Urbana IL 61801 USA UI Hlth Dept Surg Chicago IL 60612 USA Univ Illinois Div Minimally Invas Gen & Robot Surg Chicago IL 60612 USA

The first closed-loop control of electrosurgical power satisfying a specified tissue damage bound along the desired tissue dissection path is presented. The damage is represented by the 82 degrees C isotherm corresponding to the admissible tissue denaturation front position in relation to that of the cutting probe tip. The front location is assessed in real time through the infrared temperature readings of the 40 degrees C isotherm tightly related to the emerging denaturing patch size around the moving probe tip. A control-oriented denaturing hypermodel and its recasting into a form amenable for use in a moving-horizon locally linear model predictive control law are presented. The optimal control action is determined by solving a compound model predictive control problem that targets a number of active one-dimensional domains. This model is obtained from an offline trained nonlinear autoregressive model with exogenous input. To enforce the safety constraints, a supervisor system precedes the path planning control law. This system prevents excessive denaturation by excluding certain system moves, and determines system termination conditions. We experimentally demonstrate the system's performance in two different line-cutting tasks on ex vivo porcine tissue with a desired denaturation front. Copyright (C) 2021 The Authors.

关键词： Medical robots and systems autonomous robotic surgery model-based planning

来源：评论

学校读者我要写书评

暂无评论

LPVcore: MATLAB Toolbox for LPV modelling, Identification and control 19

LPVcore: MATLAB Toolbox for LPV Modelling, Identification an...

引用

19th ifac symposium on System Identification (SYSID)

作者： den Boef, Pascal Cox, Pepijn B. Toth, Roland Drebble Horsten 1 NL-5612 AX Eindhoven Netherlands Eindhoven Univ Technol Control Syst Grp POB 513 NL-5600 MB Eindhoven Netherlands Inst Comp Sci & Control Syst & Control Lab Kende U 13-17 H-1111 Budapest Hungary TNO Radar Technol POB 96864 NL-2509 JG The Hague Netherlands

This paper describes the LPVcottE software package for MATLAB developed to model, simulate, estimate and control systems via linear parameter-varying (LPV) input-output (JO), state-space (SS) and linear fractional (LFR) representations. In the LPVcottE toolbox, basis affine parameter-varying matrix functions are implemented to enable users to represent LPV systems in a global setting, i.e., for time-varying scheduling trajectories. This is a key difference compared to other software suites that use a grid or only LFR-based representations. The paper contains an overview of functions in the toolbox to simulate and identify JO, SS and LFR representations. Based on various prediction-error minimization methods, a comprehensive example is given on the identification of a DC motor with an unbalanced disc, demonstrating the capabilities of the toolbox. The software and examples are available on www. 1pvcore. net. Copyright CO (C) 2021 The Authors.

关键词： Linear parameter-varying systems software tools system identification

来源：评论

学校读者我要写书评

暂无评论

On the Uncertainty modelling for Linear Continuous-Time systems Utilising Sampled Data and Gaussian Mixture Models 19

On the Uncertainty Modelling for Linear Continuous-Time Syst...

引用

19th ifac symposium on System Identification (SYSID)

作者： Orellana, Rafael Coronel, Maria Carvajal, Rodrigo Delgado, Ramon A. Escarate, Pedro Aguero, Juan C. Univ Tecn Federico Santa Maria Elect Engn Dept Valparaiso Chile Univ Los Andes Sch Elect Engn Merida Venezuela Univ Newcastle Sch Elect Engn & Comp Callaghan NSW Australia Univ Austral Chile Inst Elect & Elect Valdivia Chile

In this paper a Maximum Likelihood estimation algorithm for model error modelling in a continuous-time system is developed utilising sampled data and a Stochastic Embedding approach. Orthonormal basis functions are used to model both the continuous-time nominal model and the error-model. The stochastic properties of the error-model distribution are defined by using a Gaussian mixture model. For the estimation of the nominal model and the error-model distribution we develop a technique based on the Expectation-Maximization algorithm using sampled data from independent experiments. The benefits of our proposal are illustrated via numerical simulations. (C) Copyright 2021 The Authors.

关键词： Continuous-time model Discrete-time model Gaussian mixture model Maximum Likelihood Stochastic embedding

来源：评论

学校读者我要写书评

暂无评论

Understanding “Just-in-Time” States in Behavioral Interventions using System Identification and Data Science Methods ⁎

引用

ifac-PapersOnLine 2024年第15期58卷 568-573页

作者： Mohamed El Mistiri Sarasij Banerjee Daniel E. Rivera Predrag Klasnja Junghwan Park Eric Hekler Control Systems Engineering Lab Arizona State University Tempe AZ 85281 USA Division of Biomedical and Health Informatics School of Information University of Michigan Ann Arbor MI 48109 USA Center for Wireless & Population Health Systems University of California San Diego (UCSD) 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.

关键词： eHealth nonlinear system identification time series modelling filtering smoothing

来源：评论

学校读者我要写书评

暂无评论

p-Safety and Stability 24

<i>p</i>-Safety and Stability

引用

24th International symposium on Mathematical Theory of Networks and systems (MTNS)

作者： Bujorianu, Manuela L. Wisniewski, Rafael Boulougouris, Evangelos Univ Strathclyde Maritime Safety Res Ctr Dept Naval Architecture Ocean & Marine Engn Glasgow Lanark Scotland Aalborg Univ Sect Automat & Control DK-9220 Aalborg Denmark

This paper focuses on the formal integration of stability and safety of complex systems. We present the initial developments of a holistic modelling framework, which will be flexible enough to allow us to formulate the integration of safety and stability problems and the design of decision and control algorithms for the maintenance of the appropriate levels of safety and stability required by the system specifications. Copyright (C) 2021 The Authors.

关键词： p-Safety stability Markov models transition semigroup Green kernel barrier certificates

来源：评论

学校读者我要写书评

暂无评论

Optimization of Low Dose Metronomic Therapy based on Pharmacological Parameters 11th

Optimization of Low Dose Metronomic Therapy based on Pharmac...

引用

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

作者： Drexler, Daniel Andras Kovacs, Levente Obuda Univ Physiol Controls Res Ctr Budapest Hungary

Therapeutic optimization is a promising direction of computer aided medicine. Optimization of chemotherapy based on mathematical models can result in lower doses, fewer side effects, a smaller chance of acquired drug resistance and more efficient personification. We explore model-based chemotherapy optimization for high frequency low dose therapies with impulsive inputs. We keep the drug level over a specified value using the minimal value of injection doses. We generate therapy for population mean parameters acquired from identification based on mice experiments. We carry out in silico trials based on the results of the individual fits from the identification process and test the therapy generated for the population mean parameters. The results show that therapy optimization based on population mean parameters can be used to generate therapy for the individuals and results in a solution close to the optimal one without using specific knowledge about the individual. Copyright (C) 2021 The Authors.

关键词： chemotherapy optimization biomedical control impulsive control positive system compartment system

来源：评论

学校读者我要写书评

暂无评论

Identification of the nonlinear steering dynamics of an autonomous vehicle 19

Identification of the nonlinear steering dynamics of an auto...

引用

19th ifac symposium on System Identification (SYSID)

作者： Rodonyi, G. Beintema, G., I Toth, R. Schoukens, M. Pup, D. Kisari, A. Vigh, Zs Koros, P. Soumelidis, A. Bokor, J. Szechenyi Istvan Univ Res Ctr Vehicle Ind SZE JKK Egyet Ter 1 H-9026 Gyor Hungary Inst Comp Sci & Control SZTAKI Syst & Control Lab Budapest Hungary Eindhoven Univ Technol Control Syst Eindhoven Netherlands

Automated driving applications require accurate vehicle specific models to precisely predict and control the motion dynamics. However, modern vehicles have a wide array of digital and mechatronic components that are difficult to model, manufactures do not disclose all details required for modelling and even existing models of subcomponents require coefficient estimation to match the specific characteristics of each vehicle and their change over time. Hence, it is attractive to use data-driven modelling to capture the relevant vehicle dynamics and synthesise model-based control solutions. In this paper, we address identification of the steering system of an autonomous car based on measured data. We show that the underlying dynamics are highly nonlinear and challenging to be captured, necessitating the use of data-driven methods that fuse the approximation capabilities of learning and the efficiency of dynamic system identification. We demonstrate that such a neural network based subspace-encoder method can successfully capture the underlying dynamics while other methods fall short to provide reliable results. Copyright (C) 2021 The Authors.

关键词： Nonlinear system identification vehicle dynamics artificial neural networks nonparametric modelling

来源：评论

学校读者我要写书评

暂无评论

Estimating insulin sensitivity after exercise using an Unscented Kalman Filter 11th

Estimating insulin sensitivity after exercise using an Unsce...

引用

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

作者： Deichmann, Julia Kaltenbach, Hans-Michael Swiss Fed Inst Technol Dept Biosyst Sci & Engn Basel Switzerland Life Sci Zurich Grad Sch Zurich Switzerland

Insulin sensitivity is an important physiological parameter for determining insulin requirements for patients with type 1 diabetes. In addition to being highly variable between patients, insulin sensitivity increases substantially during exercise and stays elevated for several hours during subsequent recovery. We propose an unscented Kalman filter for estimating insulin sensitivity from continuous glucose monitoring data that does not require the underlying model to capture exercise and relies on average values for patient-specific parameters. Using in silico full-day simulations including exercise and meals, we study how adjusting insulin doses for elevated insulin sensitivity could decrease the risk of hypoglycemia after exercise and improve time-in-range and related metrics. Copyright (C) 2021 The Authors.

关键词： diabetes management clinical guidelines decision support biological and medical system modelling quantification of physiological parameters insulin sensitivity exercise

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：