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Data-Driven Estimation of Infinitesimal Generators of Stochastic systems ⁎

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IFAC-PapersOnLine 2021年第5期54卷 277-282页

作者： Ameneh Nejati Abolfazl Lavaei Sadegh Soudjani Majid Zamani Department of Electrical Engineering Technical University of Munich Germany Institute for Dynamic Systems and Control ETH Zurich Switzerland School of Computing Newcastle University United Kingdom Department of Computer Science University of Colorado Boulder USA and LMU Munich Germany

This paper is concerned with a data-driven approach for the estimation of infinitesimal generators of continuous-time stochastic systems with unknown dynamics. We first approximate the infinitesimal generator of the solution process via a set of data collected from trajectories of the unknown system. The approximation utilizes both time discretization and sampling from the solution process. Assuming proper continuity assumptions on dynamics of the system, we then quantify the closeness between the infinitesimal generator and its approximation while providing a priori guaranteed confidence bound. We demonstrate that both the time discretization and the number of data play significant roles in providing a reasonable closeness precision. Moreover, for a fixed size of data, variance of the estimation converges to infinity when the time discretization parameter goes to zero. The formulated error bound shows how to pick proper data size and time discretization jointly to prevent this counter-intuitive behavior. The proposed results are demonstrated on a case study.

关键词： Continuous-time stochastic systems Data-driven estimation Infinitesimal generators

Data-Driven Distributionally Robust control for Interacting Agents under Logical Constraints

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arXiv 2025年

作者： Kordabad, Arash Bahari Vlahakis, Eleftherios E. Lindemann, Lars Gros, Sebastien Dimarogonas, Dimos V. Soudjani, Sadegh The Max Planck Institute for Software Systems Kaiserslautern Germany The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden The Thomas Lord Department of Computer Science the Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles United States Trondheim Norway

In this paper, we propose a distributionally robust control synthesis for an agent with stochastic dynamics that interacts with other agents under uncertainties and constraints expressed by signal temporal logic (STL). We formulate the control synthesis as a chance-constrained program (CCP) with STL specifications that must be satisfied with high probability under all uncertainty tubes induced by the other agents. To tackle the CCP, we propose two methods based on concentration of measure (CoM) theory and conditional value at risk (CVaR) and compare the required assumptions and resulting optimizations. These approaches convert the CCP into an expectation-constrained program (ECP), which is simpler to solve than the original CCP. To estimate the expectation using a finite set of observed data, we adopt a distributionally robust optimization (DRO) approach. The underlying DRO can be approximated as a robust data-driven optimization that provides a probabilistic under-approximation to the original ECP, where the probability depends on the number of samples. Therefore, under feasibility, the original STL constraints are satisfied with two layers of designed confidence: the confidence of the chance constraint and the confidence of the approximated data-driven optimization, which depends on the number of samples. We then provide details on solving the resulting robust data-driven optimization numerically. Finally, we compare the two proposed approaches through case studies. Copyright © 2025, The Authors. All rights reserved.

关键词： Constrained optimization

Fixed-Time Virtual Inertia control for Frequency Stability in Islanded Mgs

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Fixed-Time Virtual Inertia Control for Frequency Stability i...

IEEE Industry Applications Society Annual Meeting (IAS)

作者： Mohamed Zaery Hossam Ali Syed Muhammad Amrr S.M. Suhail Hussain Vijay Vittal Mohammad A. Abido K.A.CARE Energy Research & Innovation Center King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Department of Electrical Engineering Faculty of Energy Engineering Aswan University Aswan Egypt School of Electrical Arizona State University Computer and Energy Engineering Tempe AZ USA Control & Instrumentation Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia Interdisciplinary Research Center for Sustainable Energy Systems King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Electrical Engineering Department King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals (KFUPM) Dhahran Saudi Arabia

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

This paper introduces a novel control strategy, Fixed-time Virtual Inertia (Fxt-VI), designed to enhance frequency stability in islanded microgrids (MGs) that typically lack synchronous generation. Unlike traditional power systems, which rely on physical inertia, MGs necessitate inertia emulation due to the integration of fluctuating renewable energy sources. A key feature of the proposed Fxt-VI control strategy is its ability to quickly resolve frequency deviations within a predefined time frame, regardless of the initial system conditions. This ensures accelerated and predictable stabilization times. A thorough analysis using Lyapunov-based method is developed to validate the stability of the designed control strategy within a fixed time. Simulations in an islanded microgrid setup, including various energy sources such as thermal, wind, solar power plants, and energy storage systems, demonstrate the efficacy of the proposed Fxt-VI controller. Moreover, this paper provides a comparative evaluation of the proposed FxT-VI controller in relation to robust $\mathrm{H}-\infty$ and optimal PI controllers. The results show that it not only achieves quicker stabilization but also maintains lower frequency deviations compared to traditional methods, as assessed through specific performance metrics.

关键词： Time-frequency analysis Renewable energy sources Simulation Microgrids Power system stability Stability analysis Time factors Thermal stability Frequency control Power generation

Two-layer nonlinear control of DC-DC buck converters with meshed network topology

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arXiv 2021年

作者： Baldivieso-Monasterios, Pablo R. Sadabadi, Mahdieh Konstantopoulos, George C. Department of Automatic Control & Systems Engineering University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom Department of Electrical and Computer Engineering University of Patras Patras26500 Greece

In this paper, we analyse the behaviour of a buck converter network that contains arbitrary, up to mild regularity assumptions, loads. Our analysis of the network begins with the study of the current dynamics;we propose a novel Lyapunov function for the current in closed-loop with a bounded integrator. We leverage on these results to analyse the interaction properties between voltages and bounded currents as well as between node voltages and to propose a two-layer optimal controller that keeps network voltages within a compact neighbourhood of the nominal operational voltage. We analyse the stability of the closed loop system in two ways: one considering the interconnection properties which yields a weaker ISS type property and a second that contemplates the network in closed loop with a distributed optimal controller. For the latter, we propose a novel distributed way of controlling a Laplacian network using neighbouring information which results in asymptotic stability. We demonstrate our results in a meshed topology network containing 6 power converters, each converter feeding an individual constant power load with values chaning arbitrarily within a pre-specified range. © 2021, CC BY-SA.

关键词： Asymptotic stability

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

Fast quantized average consensus over static and dynamic directed graphs

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arXiv 2021年

作者： Rikos, Apostolos I. Hadjicostis, Christoforos N. Johansson, Karl H. Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

In this paper we study the distributed average consensus problem in multi-agent systems with directed communication links that are subject to quantized information flow. Specifically, we present and analyze a distributed averaging algorithm which operates exclusively with quantized values (i.e., the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and relies on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). The main idea of the proposed algorithm is that each node (i) models its initial state as two quantized fractions which have numerators equal to the node’s initial state and denominators equal to one, and (ii) transmits one fraction randomly while it keeps the other stored. Then, every time it receives one or more fractions, it averages their numerators with the numerator of the fraction it stored, and then transmits them to randomly selected out-neighbors. We characterize the properties of the proposed distributed algorithm and show that its execution, on any static and strongly connected digraph, allows each agent to reach in finite time a fixed state that is equal (within one quantisation level) to the average of the initial states. We extend the operation of the algorithm to achieve finite-time convergence in the presence of a dynamic directed communication topology subject to some connectivity conditions. Finally, we provide examples to illustrate the operation, performance, and potential advantages of the proposed algorithm. We compare against state-of-the-art quantized average consensus algorithms and show that our algorithm’s convergence speed significantly outperforms most existing protocols. © 2021, CC BY.

关键词： Multi agent systems

A Survey of Model Predictive control Methods for Traffic Signal control

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IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 623-640页

作者： Bao-Lin Ye Weimin Wu Keyu Ruan Lingxi Li Tehuan Chen Huimin Gao Yaobin Chen IEEE the School of Mechanical and Electrical Engineering Jiaxing University the State Key Laboratory of Industrial Control Instituteof Cyber-Systems and Control Technology Zhejiang University the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis Mechanical Engineering and Mechanics Ningbo University

Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

关键词： Autonomous vehicles coordination control mixed integer programming model predictive control system decomposition traffic flow models traffic signal control

Event-triggered Observability: A Set-membership Perspective

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arXiv 2022年

作者： Zheng, Kaikai Shi, Dawei Chen, Tongwen State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 1H9 Canada

This work attempts to discuss the observability of linear time-invariant systems with event-triggered measurements. A new notion of observability, namely, ǫ-observability is defined with parameter ǫ, which relates to the worst-case performance of inferring the initial state based on not only the received measurement but also the implicit information in the event-triggering conditions at no-event instants. A criterion is developed to test the proposed ǫ-observability of discrete-time linear systems, based on which an iterative event-triggered set-membership observer is designed to evaluate a set containing all possible values of the state. The proposed set-membership observer is designed as the outer approximation of the ellipsoids predicted based on previous state estimates and the ellipsoids inferred by fusing the received measurement and communication conditions, which is optimal in the sense of trace at each step and is proved to be asymptotically bounded. The efficiency of the proposed event-triggered set-membership state observer is verified by numerical experiments. © 2022, CC BY.

关键词： State estimation

A novel framework employing deep multi-attention channels network for the autonomous detection of metastasizing cells through fluorescence microscopy

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arXiv 2023年

作者： Mamalakis, Michail Macfarlane, Sarah C. Notley, Scott V. Gad, Annica K.B. Panoutsos, George Insigneo Institute for in-silico Medicine University of Sheffield Sheffield United Kingdom Department of Infection Immunity and Cardiovascular Disease Department of Computer science Sheffield United Kingdom Department of Psychiatry Cambridge University Cambridge United Kingdom Department of Oncology and Metabolism The Medical School University of Sheffield Sheffield United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Madeira Chemistry Research Centre University of Madeira Funchal Portugal Department of Oncology-Pathology Karolinska Institutet Stockholm Sweden

We developed a transparent computational large-scale imaging-based framework that can distinguish between normal and metastasizing human cells. The method relies on fluorescence microscopy images showing the spatial organization of actin and vimentin filaments in normal and metastasizing single cells, using a combination of multiattention channels network and global explainable techniques. We test a classification between normal cells (Bj primary fibroblast), and their isogenically matched, transformed and invasive counterpart (BjTertSV40TRasV12). Manual annotation is not trivial to automate due to the intricacy of the biologically relevant features. In this research, we utilized established deep learning networks and our new multi-attention channel architecture. To increase the interpretability of the network - crucial for this application area - we developed an interpretable global explainable approach correlating the weighted geometric mean of the total cell images and their local GradCam scores. The significant results from our analysis unprecedently allowed a more detailed, and biologically relevant understanding of the cytoskeletal changes that accompany oncogenic transformation of normal to invasive and metastasizing cells. We also paved the way for a possible spatial micrometre-level biomarker for future development of diagnostic tools against metastasis (spatial distribution of vimentin). © 2023, CC BY-SA.

关键词： Cells