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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51-60 订阅

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Model Predictive control for Tumor Growth: Detection of Deviations and Therapeutic Implications 12

Model Predictive Control for Tumor Growth: Detection of Devi...

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

作者： Hernandez-Rivera, Andres Velarde, Pablo Zafra-Cabeza, Ascension Maestre, Jose M. Univ Seville Syst Engn & Automat Dept Seville Spain Univ Loyola Andalucia Dept Engn Seville Spain

Recent proposals for tumor reduction employ mathematical modeling and control methodologies to design improved drug administration schemes. This research aims to enhance chemotherapy treatment in mice by employing optimal control theories and fault detection algorithms. Firstly, a model predictive control (MPC) approach combined with sequential quadratic programming (SQP) is proposed to handle the non-linearities of the dynamics of chemotherapy, and second, a fault-tolerant scheme is introduced in the control loop for identifying abnormalities considered to be faults during the treatment and launching reconfiguration actions. Integrating these methodologies aids in the early detection of discrepancies between actual and expected outcomes, facilitating prompt and effective corrective actions. Therefore, this could improve the effectiveness of the chemotherapeutic cycle, reduce side effects, and potentially increase the success rate of treatments. Simulation results demonstrate that this combined approach provides a significantly more effective and secure therapeutic strategy. Copyright (c) 2024 The Authors.

关键词： fault-tolerance model predictive control non-linear system chemotherapy

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Design of Cost-Aware Open-Loop controller For Non-Linearity Compensation Without Embedded Sensors Using Computer Vision 20

Design of Cost-Aware Open-Loop Controller For Non-Linearity ...

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20th Latin American Robotics symposium (LARS) / 15th Brazilian symposium on Robotics (SBR) / 14th Workshop on Robotics in Education (WRE)

作者： Sousa, Rodrigo Passos Santos Franco, Daniel Augusto Farias, Weslley Alves Santos, David Oliveira de Araujo Menezes, Lazaro Alberto Molina, Lucas Univ Fed Sergipe Elect Engn Dept Sao Cristovao Brazil Univ Fed Campina Grande Elect Engn Dept Campina Grande Paraiba Brazil

ISBN: (纸本)9798350315387

This paper presents a methodology of identification to dynamic modeling for designing an open-loop controller that linearizes the relationship between linear and angular velocities, in the stationary state, of a differential robot with respect to motor speed commands. The proposed approach is particularly valuable to make a nonlinearity compensation for low-cost robots that lack onboard sensing capabilities. By utilizing visual measurements obtained from cameras, i.e. third-person, the sensorless identification process enables the design of an open-loop controller without the need for embedded sensors. This cost-effective solution facilitates the application of linear dynamic controllers in robotic systems where precise control and linear dynamics are required, even in scenarios with limited resources. The proposed methodology offers a practical and accessible solution for enhancing the capabilities of low-cost robots and expanding their range of applications.

关键词： open-loop controller differential robot identification process nonlinearity compensation third-person

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Predicting nonlinear dynamics of a gas-lift oil production system through hybrid decomposition-recurrent neural networks models 12

Predicting nonlinear dynamics of a gas-lift oil production s...

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

作者： de O. e Souza, Gustavo Trierweiler, Jorge O. Farenzena, Marcelo Univ Fed Rio Grande do Sul Grp Intensificat Modeling Simulat Control & Optim Chem Engn Dept BR-90040060 Porto Alegre RS Brazil

The present paper introduces an innovative approach integrating recurrent neural networks, static models, and signal decomposition into base and residual behavior components for system nonlinear dynamic modeling and identification. The proposed methodology divides a nonlinear single-input single output gas-lift oil production system into base response and residual components, assessing the first with a first-order dynamic model with variable gain and the latter with an Encoder-Decoder (E-D) GRU model. The study evaluates the methodology under various conditions, including noiseless and noisy data and scenarios with unmeasured disturbance. The percentage of stationary residues and the normalized root mean-squared error (NRMSE) are applied to assess the model's performance. Overall, the proposed methodology demonstrates significant effectiveness, with NRMSE lower than 5% and percentages of stationary residues ranging from 90 % to 100 % across the study scope. The results stand out when compared to the direct application of E-D GRU model without decomposition, where the percentage of stationary residues was equal to 83%, and the training mean squared error was 10 times higher than that of noiseless scenarios. Copyright (C) 2024 The Authors.

关键词： nonlinear systems recurrent neural networks machine learning hybrid models chemical engineering applications oil and gas production system

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A two-layer NN framework for modeling agro-hydrological systems 7

A two-layer NN framework for modeling agro-hydrological syst...

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7th IEEE International symposium on Advanced control of Industrial processes (AdCONIP)

作者： Huang, Zhiyinan Liu, Jinfeng Huang, Biao Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 1H9 Canada

ISBN: (数字)9781665471749

ISBN: (纸本)9781665471749

Water scarcity is an urgent issue to be resolved and improving irrigation water-use efficiency through closed-loop control is essential. However, the first-principle laws that describe the complex agro-hydrological dynamics pose challenges in control applications. In this work, we propose a 2-layer neural network (NN) framework to approximate the complex nonlinear dynamics of the agro-hydrological system to reduce the computational cost of the first-principle model. The multi-step ahead open-loop prediction performance of the proposed framework is compared with a benchmark long-short-term-memory (LSTM) NN model, indicating the effectiveness of the proposed framework.

关键词： Irrigation Computational modeling process control Artificial neural networks Predictive models Benchmark testing Nonlinear dynamical systems

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Online Optimal Tuning of Relay Feedback controllers for a class of Second-Order systems with Limited Measurements 13th

Online Optimal Tuning of Relay Feedback Controllers for a cl...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Hampe, Marc Leonow, Sebastian MOnnigmann, Martin Ruhr Univ Bochum D-44801 Bochum Germany

We propose a novel, self-optimizing relay feedback control for a class of second-order systems. The proposed controller achieves a control performance that is close to that of continuous (e.g., PID) control, but it requires only very limited process information, acquired from a two-valued signal of a simple binary sensor. The smooth output of the proposed controller results in reduced energy consumption and wear of the actuator. Copyright (C) 2022 The Authors.

关键词： Adaptive control Second-order systems Relay control process control Discontinuous control

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Distributed Dynamic Event-triggered Consensus control for Multiple Euler-Lagrange systems under Digraphs 16th

Distributed Dynamic Event-triggered Consensus Control for Mu...

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16th IFAC symposium on Large Scale Complex systems - Theory and Applications (LSS)

作者： Wei, Minfeng Zang, Yunge Zhang, Fengdi Beijing Inst Technol Beijing Peoples R China Beijing Aerosp Automat Control Inst Beijing Peoples R China Natl Key Lab Sci & Technol Aerosp Intelligence Co Beijing Peoples R China

In this paper, the distributed consensus control of multiple Euler-Lagrange dynamics under digraphs via the dynamic event-triggering mechanism is studied. By designing the upper-level reference model and introducing an auxiliary variable, a novel distributed dynamic event-triggered consensus control protocol is proposed, which allows to separate neighboring information interaction from local dynamics control and thus simplifies the design process. Owing to this treatment, consensus of agents can be reached without continuous state transmission and Zeno behavior. Finally, numerical simulation is given to illustrate the effectiveness of theoretical results. Copyright (c) 2022 The Authors. This is an open access article under the CC BY-NC-ND License (http://***/licenses/by-nc-nd/4.0/)

关键词： Multi-agent systems Euler-Lagrange dynamics distributed event-triggered consensus control dynamic triggering mechanism

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Meta-Reinforcement Learning for Adaptive control of Second Order systems 7

Meta-Reinforcement Learning for Adaptive Control of Second O...

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7th IEEE International symposium on Advanced control of Industrial processes (AdCONIP)

作者： McClement, Daniel G. Lawrence, Nathan P. Forbes, Michael G. Loewen, Philip D. Backstrom, Johan U. Gopaluni, R. Bhushan Univ British Columbia Dept Chem & Biol Engn Vancouver BC Canada Univ British Columbia Dept Math Vancouver BC Canada Honeywell Proc Solut N Vancouver BC Canada Backstrom Syst Engn Ltd N Vancouver BC Canada

ISBN: (数字)9781665471749

ISBN: (纸本)9781665471749

Meta-learning is a branch of machine learning which aims to synthesize data from a distribution of related tasks to efficiently solve new ones. In process control, many systems have similar and well-understood dynamics, which suggests it is feasible to create a generalizable controller through meta-learning. In this work, we formulate a meta reinforcement learning (meta-RL) control strategy that takes advantage of known, offline information for training, such as a model structure. The meta-RL agent is trained over a distribution of model parameters, rather than a single model, enabling the agent to automatically adapt to changes in the process dynamics while maintaining performance. A key design element is the ability to leverage model-based information offline during training, while maintaining a model-free policy structure for interacting with new environments. Our previous work has demonstrated how this approach can be applied to the industrially-relevant problem of tuning proportional-integral controllers to control first order processes [1]. In this work, we briefly reintroduce our methodology and demonstrate how it can be extended to proportional-integral-derivative controllers and second order systems.

关键词： Training Adaptation models PI control Heuristic algorithms process control Reinforcement learning System identification

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Applicability Study of Model-Free Reinforcement Learning Towards an Automated Design Space Exploration Framework

Applicability Study of Model-Free Reinforcement Learning Tow...

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2023 IEEE symposium Series on Computational Intelligence, SSCI 2023

作者： Hoffmann, Patrick Gorelik, Kirill Ivanov, Valentin Corporate Research & Advance Eng. Robert Bosch GmbH Renningen Germany Technische Universitiat Ilmenau Department of Mechanical Engineering Ilmenau Germany

ISBN: (纸本)9781665430654

Design space exploration is a crucial aspect of engineering and optimization, focused on identifying optimal design configurations for complex systems with a high degree of freedom in the actor set. It involves systematic exploration while considering various constraints and requirements. One of the key challenges in design space exploration is the need for a control strategy tailored to the particular design. In this context, reinforcement learning has emerged as a promising solution approach for automatically inferring control strategies, thereby enabling efficient comparison of different designs. However, learning the optimal policy is computationally intensive, as the agent determines the optimal policy through trial and error. The focus of this study is on learning a single strategy for a given design and scenario, enabling the evaluation of numerous architectures within a limited time frame. The study also highlights the importance of plant modeling considering different modeling approaches to effectively capture the system complexity on the example of vehicle dynamics. In addition, a careful selection of an appropriate hyperparameter set for the reinforcement learning algorithm is emphasized to improve the overall performance and optimization process. © 2023 IEEE.

关键词： Electric Vehicle Intelligent control Reinforcement Learning Transportation and Vehicle systems

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16th International symposium on Advanced Vehicle control, AVEC 2024

16th International Symposium on Advanced Vehicle Control, AV...

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16th International symposium on Advanced Vehicle control, AVEC 2024

ISBN: (纸本)9783031703911

The proceedings contain 138 papers. The special focus in this conference is on Advanced Vehicle control. The topics include: Study on Linear Actuators for Intake and Exhaust systems of Internal Combustion Engines (Analytical Consideration of Thrust Characteristics by Permanent Magnet Array);drifting Maneuver Investigation via Phase Plane Analysis of Experimental Data;effects of External Tire Heating on Rolling Resistance Energy Consumption;bifurcation Analysis of a Nonlinear Vehicle Model on Banked Road;regenerative Brake Blending in Electric Hypercars: Benchmarking and Implementation;a Method for Obtaining Reference Friction Values for Validation of Road Friction Estimation Algorithms;computing the Lateral Compliance of the Racing Line Using Trajectory Optimization;state Estimation and Sensorimotor Noise in a Driver Steering Model with a Gaussian process Internal Model;energy Efficiency Oriented Robust Model Predictive Stability control for Autonomous Electric Vehicles;profile Generation of Cooperative Driving in Urban Intersections for Energetic Evaluation;energetic Impact of Urban Cooperative Driving on the Example of Intersections;speed control in the Presence of Road Obstacles: A Comparison of Model Predictive control and Reinforcement Learning;a Geometric Electric Motor Model for Optimal Vehicle Family Design;road Friction Adaptive Lateral control of Automated Vehicles with Differential Braking;model-Free Automated Reversing of Articulated Heavy Goods Vehicles;nonlinear dynamics of a controlled Two-Wheeled Trailer;yaw Stability control of Vehicles Using a Slip Polytope Validated with Real Tests;Study on Accident-Avoidance Mechanism in Driver-Vehicle System When Activating Level-2 ADAS;motion control of a 6 × 4 Heavy Vehicle: Autonomous Collision Avoidance Using Integrated Chassis control.

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control of a microfluidic separation process governed by the Zweifach-Fung effect 13th

Control of a microfluidic separation process governed by the...

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13th IFAC symposium on dynamics and control of process systems, including Biosystems (DYCOPS)

作者： Petit, Nicolas PSL Univ MINES Paris Tech Ctr Automat & Syst 60 Bd St Michel F-75272 Paris France

This paper presents a microfluidic process of separation of a fluid containing particles. The control input is the ratio of flowrates in the two daughter channels of a bifurcation, which due to the Zweifach-Fung effect, has a direct but nonlinear effect on the particle volume fractions in the channels. The dead-volumes of the device and its capillary tubings cause an input-varying delay of hydraulic type. The paper presents several control problems of practical importance and formulates some concise problem statements for future research along with first simulation reults. Copyright (C) 2022 The Authors.

关键词： Microfluidics device integration technologies process control applications control of micro- and nano-systems control of particulate processes delay systems non-smooth and discontinuous optimal control problems

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