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Learning-Based Predictive control of Switched Nonlinear systems via Koopman Modeling Approach

Journal of systems Science & Complexity 2025年第2期38卷 691-716页

作者： NING Zepeng FANG Xu LI Yibei XIE Lihua School of Electrical and Electronic Engineering Nanyang Technological University Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences

This paper proposes a data-driven learning-based approach to predictive control for switched nonlinear systems subject to state and control constraints and external stochastic disturbances. A switched Koopman modeling framework is developed, where a multi-mode neural network for state lifting is trained simultaneously with Koopman operators and state reconstruction matrices for all *** framework facilitates the construction of the switched linear Koopman model in a transformed space and effectively captures the dynamics of the original nonlinear system. A switched predictive control strategy is then designed to regulate the switched Koopman model with constrained states and control inputs against both the stochastic disturbances and the uncertainties introduced by the lifting neural network. The proposed control scheme ensures mean-square stability and guarantees boundedness during the online phase. Furthermore, boundedness analysis is performed to determine the bounded set of the original system state across all admissible switching sequences. The effectiveness of the proposed methodology is demonstrated through a case study of a gene regulatory network.

关键词： Data-driven machine learning model predictive control stochastic systems switched Koopman model switched nonlinear systems

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Time-Varying Formation Tracking control of Heterogeneous Multi-Agent systems With Intermittent Communications and Directed Switching Networks

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems control theory Autonomous systems Stability

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Mathematical Modeling of Photosynthetic Eukaryotic Microorganisms Using Metabolic Networks

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IFAC-PapersOnLine 2025年第1期59卷 397-402页

作者： M. Maton A. Vande Wouwer Systems Estimation Control and Optimization (SECO) Laboratory University of Mons Mons 7000 Belgium

Metabolic modeling is a powerful tool for understanding microbial metabolism and is particularly appealing to a wide range of applications, from biotechnology and medicine to environmental science and sustainability. In that context, the elaboration of metabolic networks is essential despite the challenges underlying their reconstruction. While the development of genome-scale networks is computationally costly, small networks are often oversimplified, limiting their use in industrial applications. For this purpose, this paper suggests a method to identify metabolic networks of intermediate size by combining biological knowledge and a series of constraint-based methods in an iterative strategy allowing the refinement of the network definition. The present study focuses on the mathematical modeling of photosynthetic eukaryotic organisms and leads to a detailed network including energy aspects such as the proton motive force. The procedure is effective, yielding promising results while metabolic analyses provide consistent predictive capabilities of the network, in concordance with existing studies.

关键词： Metabolic modeling metabolic flux analysis metabolic network photosynthetic organisms static regime

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Superhydrophobic ceramic membrane coupled with a biphasic solvent for efficient CO_(2)capture

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Green Energy & Environment 2025年第4期10卷 834-844页

作者： Kaili Xue Zhen Chen Xiaona Wu Heng Zhang Haiping Chen Junhua Li State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijing100084China Beijing Key Laboratory of Pollutant Monitoring and Control in Thermoelectric Production Process School of EnergyPower and Mechanical EngineeringNorth China Electric Power UniversityBeijing102206China MOE Key Laboratory of Resources and Environmental Systems Optimization College of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing102206China

An innovative strategy was proposed by integration of membrane contactor(MC)with biphasic solvent for efficient CO_(2) capture from flue *** accessible fly ash-based ceramic membrane(CM)underwent hydrophobic modification through silane grafting,followed by fluoroalkylsilane decoration,to prepare the superhydrophobic membrane(CSCM).The CSCM significantly improved resistance to wetting by the biphasic solvent,consisting of amine(DETA)and sulfolane(TMS).Morphological characterizations and chemical analysis revealed the notable enhancements in pore structure and hydrophobic chemical groups for the modified *** of wetting/bubbling behavior based on static wetting theory referred the liquid entry pressure(LEP)of CSCM increased by 20 kPa compared to pristine *** with traditional amine solvents,the biphasic solvent presented the expected phase *** experiments demonstrated that the CO_(2) capture efficiency of the biphasic solvent increased by 7%,and the electrical energy required for desorption decreased by 32%.The 60-h continuous testing and supplemental characterization of used membrane confirmed the excellent adaptability and durability of the *** study provides a potential approach for accessing hydrophobic ceramic membranes and biphasic solvents for industrial CO_(2) capture.

关键词： Carbon capture Membrane contactor Hydrophobic modification Membrane wetting Biphasic solvent

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Topology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of Things

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IEEE Internet of Things Journal 2025年第11期12卷 16298-16313页

作者： Li, Mengran Chen, Junzhou Yu, Chenyun Jiang, Guanying Zhang, Ronghui Shen, Yanming Song, Houbing Herbert Shenzhen Campus of Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen518107 China Baidu Inc Beijing100085 China Dalian University of Technology Key Laboratory of Intelligent Control and Optimization for Industrial Equipment Ministry of Education School of Electronic Information and Electrical Engineering Dalian116024 China Department of Information Systems Baltimore County BaltimoreMD21250 United States

With the advancement of information technology, the Social Internet of Things (SIoT) has fostered the integration of physical devices and social networks, deepening the study of complex interaction patterns. Text Attribute Graphs (TAGs) capture both topological structures and semantic attributes, enhancing the analysis of complex interactions within the SIoT. However, existing graph learning methods are typically designed for complete attributed graphs, and the common issue of missing attributes in Attribute Missing Graphs (AMGs) increases the difficulty of analysis tasks. To address this, we propose the Topology-Driven Attribute Recovery (TDAR) framework, which leverages topological data for AMG learning. TDAR introduces an improved pre-filling method for initial attribute recovery using native graph topology. Additionally, it dynamically adjusts propagation weights and incorporates homogeneity strategies within the embedding space to suit AMGs' unique topological structures, effectively reducing noise during information propagation. Extensive experiments on public datasets demonstrate that TDAR significantly outperforms state-of-the-art methods in attribute reconstruction and downstream tasks, offering a robust solution to the challenges posed by AMGs. The code is available at https://***/limengran98/TDAR. © 2014 IEEE.

关键词： Undirected graphs

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Immoral post detection using a one-dimensional convolutional neural network-based LSTM network

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Multimedia Tools and Applications 2025年 1-20页

作者： Saqia, Bibi Khan, Khairullah Rahman, Atta Ur Khan, Aurangzeb Jain, Deepak Kumar Department of Computer Science University of Science and Technology Bannu28100 Pakistan Riphah Institute of Systems Engineering Riphah International University Islamabad46000 Pakistan Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China Symbiosis Institute of Technology Symbiosis International University Pune India

In text mining and Natural Language Processing (NLP), extracting emotions from textual data is gaining rapid attraction. The proliferation of online content and the freedom of expression on social media platforms has compelled the advancement of more sophisticated methods for the automatic identification of unwanted content. The One-Dimensional Convolutional Neural Network (1D-CNN) performs best for feature extraction, while the Long Short-Term Memory Network (LSTM) is well-known for extracting sequential features and classification of textual data. This study proposes a novel solution by combining 1D-CNN with LSTM. Utilizing a context-aware model, this study aims to enhance the detection of immoral posts, including hate speech and other forms of immoral content on social media platforms. Various posts are extracted from social media, which are then processed using Glove word embedding models. It depicts the word representations in the text, which yields the form of a real-valued vector determining the words' meaning. The 1D convolution utilizes a filter window over the input time-series data to extract features. Relying on the acquired parameters of the filters, they act as feature extractors. This feature extraction technique allows the model to distinguish detailed patterns and nuances from the textual contents of posts. The sequential processing capabilities of the LSTM network enable an in-depth examination of the text, capturing temporal relationships and context, which is critical for successful content categorization. To validate the proposed model, we utilized a benchmark dataset, called the Twitter dataset. The proposed model achieved the highest accuracy of 95.37% and outperforms state-of-the-art works. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Model-based optimization for laboratory-Scale Upstream Processing in Monoclonal Antibody Production 11

Model-based Optimization for Laboratory-Scale Upstream Proce...

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11th Vienna International Conference on Mathematical Modelling, MATHMOD 2025

作者： Pimentel, Guilherme A. Garcia-Tenorio, Camilo Fekih-Salem, Radhouane Toussaint, Cédric Kanfoud, Ahmed Cornet, Thomas Helleputte, Thibault Boes, Adrien Marega, Riccardo Wouwer, Alain Vande Dewasme, Laurent Systems Estimation Control and Optimization Group University of Mons Belgium DNAlytics Ottignies-Louvain-la-Neuve Belgium Immunobiology Laboratory CER Groupe Belgium University of Tunis El Manar National Engineering School of Tunis LAMSIN Tunisia

The production of monoclonal antibodies (mAbs) is a complex pharmacological process involving upstream and downstream chains, focusing on mAbs production and purification, respectively. This paper presents a process optimization based on an end-To-end mechanistic model for the upstream process chain to enhance the production of mAbs. Upstream processing considers cell expansion, cell production, and primary harvesting steps by filtration. The optimization results offer users the optimal initial concentrations of the medium in the bioreactors, the duration of the cultures, the feed flow rate profiles, and the permeate flow profile in the filtration operation. Simulations incorporating interconnected reactors and process constraints led to optimized initial media concentrations and feed flow profiles, resulting in a 25% increase in mAb production compared to baseline conditions. Copyright © 2025 The Authors.

关键词： Monoclonal antibodies

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A Globally Convergent Method for Computing B-stationary Points of Mathematical Programs with Equilibrium Constraints

arXiv

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arXiv 2025年

作者： Nurkanovic, Armin Leyffer, Sven Systems Control and Optimization Laboratory Department of Microsystems Engineering University of Freiburg Germany Mathematics and Computer Science Division Argonne National Laboratory United States

This paper introduces a method that globally converges to Bstationary points of mathematical programs with equilibrium constraints (MPECs) in a finite number of iterations. B-stationarity is necessary for optimality and means that no feasible first-order direction improves the objective. Given a feasible point of an MPEC, B-stationarity can be certified by solving a linear program with equilibrium constraints (LPEC) constructed at this point. The proposed method solves a sequence of LPECs, which either certify B-stationarity or provide an active-set estimate for the complementarity constraints, and nonlinear programs (NLPs) - referred to as branch NLPs (BNLPs) - obtained by fixing the active set in the MPEC. A BNLP is more regular than the MPEC, easier to solve, and with the correct active set, its solution coincides with the solution of the MPEC. The method has two phases: the first phase identifies a feasible BNLP or certifies local infeasibility, and the second phase solves a finite sequence of BNLPs until a B-stationary point of the MPECis found. The paper provides a detailed convergence analysis and discusses implementation details. In addition, extensive numerical experiments and an open-source software implementation are provided. The experiments demonstrate that the proposed method is more robust and faster than relaxation-based methods, while also providing a certificate of B-stationarity without requiring the usual too-restrictive *** Codes 90C30, 90C33, 49M37, 65K10, 90C11 © 2025, CC BY.

关键词： Nonlinear programming

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A Data-Driven Adaptive control Approach for Enhancing the Dynamic Response of VSGs in Varying Grid Conditions

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IEEE Transactions on Power Delivery 2025年第3期40卷 1421-1433页

作者： Fahad, Shah She, Buxin Yin, Junjie Li, Fangxing Cui, Hantao Bo, Rui Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Senior Power Systems Engineer ETAP Irvine CA USA Optimization & Control Group Parcific Northwest National Laboratory Richland USA Department of Electrical Engineering and Computer Science University of Tennessee at Knoxville Knoxville TN USA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Conventionally, a virtual synchronous generator (VSG) is designed for islanded mode (IM) operation to meet specific operational requirements such as the rate of change of frequency (RoCoF). However, the operation of VSG designed for IM may not meet the operational and control criteria in grid connected mode (GCM) when the grid conditions vary. In addition, conventional VSG control technology does not consider the influence of the presynchronization scheme when connected to a weak grid, which degrades the RoCoF in IM. To overcome the aforementioned challenges, the proposed study presents a twin-delayed deep deterministic policy gradient (TD3) algorithm to improve the seamless transition performance of VSG from IM to GCM and vice versa. In the first step, the VSG-based power system model is used as a foundation for problem formulation of the proposed TD3 algorithm. Secondly, a reward function is designed according to the performance requirements, i.e., frequency and RoCoF requirements, of the VSG in order to guide the training of the agent in varying load, power reference, and grid conditions. Finally, the superiority of the proposed algorithm over existing methods is validated in MATLAB/SIMULINK and RTDS based real-time simulation environment. © 1986-2012 IEEE.

关键词： Deep reinforcement learning

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Model-based optimization for laboratory-Scale Upstream Processing in Monoclonal Antibody Production

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IFAC-PapersOnLine 2025年第1期59卷 403-408页

作者： Guilherme A. Pimentel Camilo Garcia-Tenorio Radhouane Fekih-Salem Cédric Toussaint Ahmed Kanfoud Thomas Cornet Thibault Helleputte Adrien Boes Riccardo Marega Alain Vande Wouwer Laurent Dewasme Systems EstimationControl and Optimization Group University of Mons Belgium DNAlytics Ottignies-Louvain-la-Neuve Belgium Immunobiology Laboratory CER Groupe Belgium University of Tunis El Manar National Engineering School of Tunis LAMSIN Tunisia

The production of monoclonal antibodies (mAbs) is a complex pharmacological process involving upstream and downstream chains, focusing on mAbs production and purification, respectively. This paper presents a process optimization based on an end-to-end mechanistic model for the upstream process chain to enhance the production of mAbs. Upstream processing considers cell expansion, cell production, and primary harvesting steps by filtration. The optimization results offer users the optimal initial concentrations of the medium in the bioreactors, the duration of the cultures, the feed flow rate profiles, and the permeate flow profile in the filtration operation. Simulations incorporating interconnected reactors and process constraints led to optimized initial media concentrations and feed flow profiles, resulting in a 25% increase in mAb production compared to baseline conditions.

关键词： process optimization monoclonal antibodies upstream processing mechanistic modeling

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