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检索条件"任意字段=Symposium on Dynamics and Control of Process Systems"

共 1794 条记录，以下是1-10 订阅

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13Th IFAC symposium on dynamics and control of process systems, including Biosystems DYCOPS 2022

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IFAC-PapersOnLine 2022年第7期55卷 viii页

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12th IFAC symposium on dynamics and control of process systems, including Biosystems, DYCOPS 2019

12th IFAC Symposium on Dynamics and Control of Process Syste...

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12th IFAC symposium on dynamics and control of process systems, including Biosystems, DYCOPS 2019

The proceedings contain 168 papers. The topics discussed include: detection and estimation of moving obstacles for a UAV;optimal startup operation of a pressure swing adsorption;model reference based tuning for fractional-order 2 DoF PI controllers with a robustness consideration;extension of the DO-MPC development framework to real-time simulation studies;control of multi-chamber continuous fluidized bed spray granulation;a polytopic invariant set based iterative learning model predictive control;and advanced nonlinear multi-layer process control for autotrophic cultivations.

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Computational Model of Ta2O5/TaOx Memristors: Predicting Resistive Switching Behavior and Filament Growth dynamics for Enhanced Device control and Optimization 67

Computational Model of Ta2O5/TaOx Memristors: Predicting Res...

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67th IEEE International Midwest symposium on Circuits and systems (MWSCAS)

作者： Gooran-Shoorakchaly, Armin Sharif, Sarah Safura Banad, Yaser Mike Univ Oklahoma Sch Elect & Comp Engn Norman OK 73019 USA

ISBN: (纸本)9798350387186;9798350387179

Memristors have been suggested for various applications, including nonvolatile memory and neuromorphic systems. In contrast to traditional devices that rely purely on electron transport, memristors use resistive switching (RS), which is based on the redistribution of ions. Despite multiple experimental and modeling efforts to investigate the RS process, a full physical framework that can quantitatively explain the dynamic RS behavior remains difficult. A model that quantitatively outlines dynamic resistive switching, including set/reset cycling and retaining self-consistency from the initial formation process, has yet to be developed. This study presents a Ta2O5/TaOx device model that can accurately predict all crucial RS properties during the formation process and subsequent set and reset cycles. The simulation outcomes were also contrasted with experimental DC and pulse measurements in 1R structures and revealed excellent agreement.

关键词： memristor Ta2O5/TaOx oxygen vacancy resistive switching

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Optimal control of Mechanical Ventilators with Learned Respiratory dynamics 37

Optimal Control of Mechanical Ventilators with Learned Respi...

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37th International symposium on Computer-Based Medical systems (CBMS)

作者： Ward, Isaac R. Asmar, Dylan M. Arief, Mansur Mike, Jana Krystofova Kochenderfer, Mykel J. Stanford Univ Dept Aeronaut & Astronaut Stanford Intelligent Syst Lab Stanford CA 94305 USA Univ Calif San Francisco UCSF Weill Inst Neurosci Dept Pediat San Francisco CA 94143 USA

ISBN: (纸本)9798350384734;9798350384727

Deciding on appropriate mechanical ventilator management strategies significantly impacts the health outcomes for patients with respiratory diseases. Acute Respiratory Distress Syndrome (ARDS) is one such disease that requires careful ventilator operation to be effectively treated. In this work, we frame the management of ventilators for patients with ARDS as a sequential decision making problem using the Markov decision process framework. We implement and compare controllers based on clinical guidelines contained in the ARDSnet protocol, optimal control theory, and learned latent dynamics represented as neural networks. The Pulse Physiology Engine's respiratory dynamics simulator is used to establish a repeatable benchmark, gather simulated data, and quantitatively compare these controllers. We score performance in terms of measured improvement in established ARDS health markers (pertaining to improved respiratory rate, oxygenation, and vital signs). Our results demonstrate that techniques leveraging neural networks and optimal control can automatically discover effective ventilation management strategies without access to explicit ventilator management procedures or guidelines (such as those defined in the ARDSnet protocol).

关键词： optimal control neural networks artificial intelligence machine learning deep learning respiratory disease respiratory dynamics healthcare acute respiratory distress syndrome

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Fuzzy control of a Hydrodynamic process 23

Fuzzy Control of a Hydrodynamic Process

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23rd International symposium INFOTEH-JAHORINA (INFOTEH)

作者： Krcmar, Igor Maric, Petar Dalic, Velibor Rakic, Aleksandar Derajic, Bojan Univ Banja Luka Fac Elect Engn Banja Luka Bosnia & Herceg Univ Belgrade Sch Elect Engn Dept Signals & Syst Belgrade Serbia Tech Univ Berlin Fac Elect Engn & Comp Sci Berlin Germany

ISBN: (纸本)9798350329940;9798350307368

Hydrodynamic processes, part of many industrial and urban infrastructure systems, usually lack accurate models, hence introducing ambiguity in a controller design procedure. Operators and control engineers provide significant knowledge about successful control of hydrodynamic systems. Fuzzy controller, suitable to exploit and incorporate available knowledge, is designed for control of a benchmark coupled tank system. Successful fuzzy controller must cope with slow dynamics of the process, dead zone of an actuator, and unipolar control signal, when employed in reference tracking tasks. The designed fuzzy controller implements fuzzy inference system with 15 rules, two inputs and one output, as well as three adjustable gain parameters. The proposed controller is successful in reference tracking tasks and disturbance suppression. Experiments, organised as reference tracking tasks and implemented as computer simulations, support the analysis.

关键词： fuzzy controller hydrodynamic process dead zone unipolar control signal

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Model-based feedback control of filament geometry in extrusion-based additive manufacturing. 12

Model-based feedback control of filament geometry in extrusi...

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12th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Rojas, Carlos J. G. Portilla, Christian Ozkan, Leyla Eindhoven Univ Technol Eindhoven Netherlands Delft Univ Technol Delft Netherlands

control of extrusion-based printing is fundamental to improving the traditional open-loop operation of commercial machines. However, the literature on feedback control based on nozzle motion is still limited in comparison with the developments for extrusion dynamics. In this work, we propose a model-based control strategy where the effects of the nozzle speed on the filament width are described by a first-order model, and the model uncertainty is used for robust synthesis. The feedback employs the internal model controller (IMC) to get an approximate inversion of the dynamics and the IMC filter is tuned based on robust performance criteria. The controller was experimentally tested and provided satisfactory results, especially when the parameters were refined depending on the speed regime. Copyright (C) 2024 The Authors.

关键词： Additive manufacturing Time delay systems Advanced process control Robust control applications process control applications

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Gaussain process-based MPC for DC Motor Servo System with State Constraints and Load Disturbance 7

Gaussain Process-based MPC for DC Motor Servo System with St...

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7th International symposium on Autonomous systems, ISAS 2024

作者： Wang, Ning Qiu, Xiaojie Long, Changqing Chen, Jun Meng, Wenchao Zhejiang University College of Control Science and Engineering Hangzhou317000 China

ISBN: (纸本)9798350363173

The effectiveness of Model Predictive control (MPC) heavily depends on the precision of the model in accurately representing the dynamics of the plant. However, identifying such models can be challenging due to complex dynamics and unknown or changing disturbances. One strategy is to utilize a simple and fixed nominal model while learning the unknown deviation between this nominal model and the actual plant dynamics. Gaussian process (GP) regressions have demonstrated their value as a reliable tool for predicting disturbances and model mismatches, thereby facilitating their incorporation into MPC predictions. This paper introduces a framework for learning the dynamics of load disturbance in the DC motor servo system. We evaluate and compare the performance of the GP-MPC controller with that of a conventional MPC controller. Results indicate that the GP-MPC controller outperforms the conventional MPC controller in position servo performance during load disturbance while adhering to input saturation and state constraints. © 2024 IEEE.

关键词： Model predictive control

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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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CNN-based Temperature dynamics Approximation for Burning Rooms 12

CNN-based Temperature Dynamics Approximation for Burning Roo...

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12th IFAC symposium on Fault Detection, Supervision and Safety for Technical processes (SAFEprocess)

作者： Liebert, Artur Palani, Arulnambi Rensmeyer, Tim Breuer, Michael Niggemann, Oliver Helmut Schmidt Univ Hamburg Holstenhofweg 85 D-22043 Hamburg Germany

The chaotic and dynamic nature of heat transfer results is either time-consuming or inaccurate predictions of the temperature field in simulations. In particular, the simulation of burning buildings is complex and at the same time the key enabler in saving lives and keeping property damage to a minimum. Deep Learning Neural Networks are a possibility to speed up the simulation process and make real-time predictions of temperature behavior or even replace the simulation process as a whole in the long run. This paper proposes a novel procedure to create a convolutional neural network-based method CNN1D3D, that is capable of approximating the behavior of temperature in burning rooms. The data used for training is created with time intensive, numerical simulations. CNN1D3D's architecture consists of an convolution-based temporal and spatial encoding as well of a transposed convolution based decoder, that creates temperature predictions in real-time. The work shows a possibility for a distinct feature extraction for temporal and spatial features. It shows how solutions generated by simulations based on differential equations hold implicitly the necessary information needed for the method to recreate the context of the data set. This forms a basis for the abstraction onto further fluid dynamics applications. This work has several real-world applications and forms the basis for future rescue route calculations. The solution can be generalized on other applications with similar data structures. It provides the opportunity to capture the complexity of interdependencies and correlations in the field of fluid dynamics. 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： Forecasting Energy systems Environmental decision support systems Machine learning N-dimensional systems Neural networks

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Applying Sampling-Based Convex Relaxations to Dynamic process Models 12

Applying Sampling-Based Convex Relaxations to Dynamic Proces...

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12th IFAC symposium on Advanced control of Chemical processes (ADCHEM)

作者： Chui, Ho-Ching Khan, Kamil A. McMaster Univ Hamilton ON Canada

Convex relaxations are a crucial tool in methods for global optimization, but are challenging to construct for dynamic processes. In this article, we investigate combining two recent approaches for convex relaxation generation in new nontrivial ways, to aid global optimization of dynamic chemical process models. Specifically, we combine recent approaches for automatically generating convex relaxations for solutions of parametric ordinary differential equations with a recent sampling-based approach for tractably generating lower bounds of a convex relaxation. Copyright (C) 2024 The Authors.

关键词： Reachability analysis Convex relaxations dynamics and control

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