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Smart Agricultural Technology 2025年 11卷

作者： Ramesh Arvind Naagarajan Stefan Streif Chair of Automatic Control and System Dynamics Chemnitz University of Technology Chemnitz Germany Department of Bioresources Fraunhofer Institute for Molecular Biology and Applied Ecology Giessen Germany

As climate change and resource scarcity threaten global food security, greenhouse systems are becoming critical for sustainable agriculture. Advanced control, such as Model Predictive control (MPC), effectively regulates temperature, humidity, and CO 2 to enhance crop growth and resource efficiency. However, the widespread adoption of such advanced control systems is limited by their lack of interpretability, as growers struggle to understand complex control decisions, particularly during rapid environmental changes. In this work, a Natural Language Generation (NLG) interface has been developed to bridge this gap and transform MPC control decisions into clear, actionable explanations for greenhouse growers. This interface integrates Large Language Models (LLMs) with greenhouse control systems and mathematical constraints, providing a step toward making AI-driven agriculture more accessible. This integration addresses the need for interpretable AI systems in modern agricultural applications. The system allows growers to interact with the control system, query decisions, and receive contextually relevant explanations through Retrieval Augmented Generation (RAG) mechanisms and instruction prompting techniques. The Adaptive RAG (ARAG) framework was evaluated using semantic similarity, information retrieval, and contextual relevance metrics, demonstrating a 12.1% improvement in BERTScore, over baseline methods. These results highlight the system's ability to deliver accurate, well-structured explanations without compromising control performance. By improving the interpretability and accessibility of AI-powered greenhouse automation, this research advances the development of sustainable greenhouse practices that can adapt to the challenges of climate change and resource scarcity. The proposed system represents a significant step toward transforming traditional greenhouse control into more interpretable solutions for modern agriculture.

关键词： AI Artificial Intelligence API Application Programming Interface ARAG Adaptive Retrieval Augment Generation DNN Deep Neural Networks EVT Extreme Value Theorem GPT Generative Pre-Trained Transformers LLM Large Language Models ML Machine Learning NLG Natural Language Generation NLP Natural Language Processing NMPC Nonlinear Model Predictive control RAG Retrieval Augment Generation Artificial Intelligence Instructional prompting Large language models Model predictive control Natural language generation Retrieval augmented generation

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Optimization of Nostoc sp. biomass and protein composition in high-density cultivator based on DoE

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Algal Research 2025年 89卷

作者： Lucato, Veronica Ihadjadene, Yob Sut, Stefania Sforza, Eleonora Krujatz, Felix Department of Industrial Engineering DII University of Padova Via Marzolo 9 Padova 35131 Italy Institute of Natural Materials Technology Technische Universität Dresden Dresden 01069 Germany Chair of Automatic Control & System Dynamics Chemnitz University of Technology Chemnitz 09126 Germany Department of Pharmaceutical and Pharmacological Sciences University of Padova Via Marzolo 5 Padova 35131 Italy biotopa gGmbH – Center for Applied Aquaculture & Bioeconomy Radeberg 01454 Germany

High-density (HD) cultivation systems are a valuable alternative to improve the productivity of phototrophic microorganisms. They are designed to address the major challenges such as low cell densities, light attenuation, and inefficient gas exchange, offering innovative solutions. This work investigates the HD cultivation of the nitrogen-fixing cyanobacterium Nostoc sp. PCC 7120 under diazotrophic conditions, employing a hydrophobic membrane system for efficient CO₂ and N₂ transfer. A design of experiment (DoE) approach is applied to evaluate the influence of key operating variables, including light intensity, photoperiod, and phosphorus concentration, on biomass productivity (P_X) and nitrogen fixation (P_N). Mathematical models, describing the interactions between these process variables and growth responses are developed and validated, enabling the optimization of cultivation conditions to maximise productivity. Remarkable biomass productivity of 1.53 g_X L⁻¹ d⁻¹ was obtained in this system, under diazotrophic conditions. P_X was largely affected by light intensity, which should be supplied continuously, aligning with the ability of heterocystous species like Nostoc sp. of simultaneously perform photosynthesis and N-fixation. Additionally, phosphorus concentration was a critical parameter that must be precisely controlled to prevent substrate limitation or inhibition, with an optimal value of 34.1 mg_P L⁻¹. The produced biomass was further characterised with a focus on its nitrogen-rich molecules, particularly proteins, and nutritional value, assessed through essential amino acid content. The results showed that protein content was improved (53.4 %) at 280 μmol m⁻² s⁻¹, 25.5 mg_P L⁻¹_, and 18 h light period, with essential amino acid ranging between 11 % and 15 %. © 2025 The Authors

关键词： Amino acid profile Biomass productivity Nitrogen-fixing cyanobacteria Protein production Response surface methodology

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Feedforward control design under input constraints for a tubular reactor model

Feedforward control design under input constraints for a tub...

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IEEE Conference on Decision and control

作者： Peter Wieland Thomas Meurer Knut Graichen Michael Zeitz Institute for Systems Theory and Automatic Control University of Stuttgart Germany Chair of System Theory and Automatic Control University of Saarland Saarbrucken Germany Institute for System Dynamics University of Stuttgart Germany

ISBN: (纸本)1424401704

This paper presents a new early-lumping approach of feedforward control design for setpoint change scenarios applied to a tubular reactor modeled by a nonlinear parabolic diffusion-convection-reaction equation. The approach is based on Galerkin's method to obtain a finite-dimensional model and on a recently developed approach to feedforward control design for nonlinear systems under input constraints. The considered finite-time transition between equilibrium points is treated as a two-point boundary value problem. Simulation results for different sets of model parameters illustrate the applicability of the approach

关键词： control design Inductors Moment methods Nonlinear equations Nonlinear dynamical systems Nonlinear systems Differential equations Partial differential equations Force control Trajectory

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Modeling and Nonlinear control of an Electrohydraulic Closed-Center Power-Steering system

Modeling and Nonlinear Control of an Electrohydraulic Closed...

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IEEE Conference on Decision and control

作者： W. Kemmetmuller A. Kugi S. Muller Chair of System Theory and Automatic Control University of Saarland Saarbruecken Germany Vehicle Dynamics Control Systems BMW Group Munich Germany

This paper deals with the mathematical modeling and the nonlinear control of an electrohydraulic closed-center power-steering system. The system under consideration is characterized by its high energetic efficiency at a full electric power-steering functionality. Based on a nonlinear mathematical model of the system, a flatness-based controller for the servo actuator is designed. Afterwards, an interpretation of the overall steering control system as a mechanical impedance matching problem yields a controller with good performance and robust behavior. Finally, measurement results on a test stand and in a test car show the usefulness of the proposed control approach.

关键词： Power system modeling Nonlinear control systems Electrohydraulics Mathematical model control systems Testing control system synthesis Servomechanisms Actuators Impedance matching

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Observer design for a railway running gear with independently rotating wheels

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IFAC-PapersOnLine 2019年第15期52卷 325-330页

作者： Alexander Keck Christoph Schwarz Thomas Meurer Institute of System Dynamics and Control German Aerospace Center Münchener Str. 20 82234 Oberpfaffenhofen-Wessling Germany Chair of Automatic Control Faculty of Engineering Kiel University Kaiserstraße 2 24143 Kiel Germany

The German Aerospace Center bundles its railway research in the long-term project “Next Generation Train” (NGT). The NGT is a high speed train concept in light-weight design and double-deck configuration. To reduce wheel and rail wear and to enhance the passenger capacity, a mechatronic running gear with independently rotating wheels (IRW) is designed. This configuration requires an advanced control of the lateral dynamics in order to fully exploit the potential in minimizing wear and noise. In terms of a model-based control design, the controller performance highly depends on the estimated lateral position of the running gear relative to the track. In a practical environment it is difficult to directly measure this displacement of the wheel-pair. Therefore this article deals with the question which sensor configuration enables an appropriate estimation of the lateral displacement and is suitable for observer design. Hence, an observability analysis and an observer synthesis to estimate the lateral position for the nonlinear system of the 1:5 scaled hardware running gear is carried out. For validation purposes three estimators are implemented at the real-time environment of the testbed and the estimation errors of the observer configurations are compared.

关键词： Nonlinearity Observability Observers Extended Kalman filters Railways

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Observability and Identifiability Analyses of Process Models for Agricultural Anaerobic Digestion Plants

Observability and Identifiability Analyses of Process Models...

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International Conference on Process control (PC)

作者： Simon Hellmann Arne-Jens Hempel Stefan Streif Sören Weinrich Biochemical Conversion Department DBFZ Deutsches Biomasseforschungszentrum gGmbH Leipzig Germany Saxon University of Cooperative Education Glauchau Germany Chair of Automatic Control and System Dynamics Chemnitz University of Technology Chemnitz Germany Faculty of Energy. Building Services. Environmental Engineering Munster University of Applied Sciences Münster Germany

Dynamic operation of anaerobic digestion plants requires advanced process monitoring and control. Different simplifications of the Anaerobic Digestion Model No. 1 (ADM1) have been proposed recently, which appear promising for model-based process automation and state estimation. As a fundamental requirement, observability and identifiability of these models are analyzed in this work, which was pursued through algebraic and geometric analysis. Manual algebraic assessment was successful for small models such as the ADM1-R4 and simplified versions of the ADM1-R3, which were derived in this context. However, for larger model classes the algebraic approach showed to be insufficient. By contrast, the geometric approach, implemented in the STRIKE GOLDD toolbox, allowed to show observability for more complex models (including ADM1-R4 and ADM1-R3), employing two independent algorithms. The present study lays the groundwork for state observer design, parameter estimation and advanced control resting upon ADM1-based models.

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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