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Discrete-Time Nonlinear System Identification: A Fixed-Time Concurrent learning Approach

ieee TRANSACTIONS ON systems MAN CYBERNETICS-systems 2025年第3期55卷 1591-1599页

作者： Tatari, Farzaneh Niknejad, Nariman Modares, Hamidreza Drive Syst Design Inc Control Syst Farmington Hills MI 48335 USA Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA

This article develops a fixed-time identifier for modeling unknown discrete-time nonlinear systems without requiring the standard persistence of excitation (PE) condition. A data-driven update law based on a modified gradient descent (GD) update law is presented to learn the system parameters, which relies on the concurrent learning approach under which the recorded past data and the current data are employed concurrently. Fixed-time convergence guarantees are provided for the modified GD update law under the condition that the recorded data fulfills a rank condition, which is less restrictive than the standard PE condition. To guarantee fixed-time convergence, fixed-time Lyapunov analysis is leveraged. Compared to typical GD-based update laws, two main advantages of the presented approach are: 1) the modified GD update law guarantees fixed-time convergence instead of asymptotic convergence and 2) the convergence guarantee is provided under an easy-to-check rank condition rather than the standard PE condition, which is hard or even impossible to check online. Simulation results are provided to verify the obtained results.

关键词： Convergence Discrete-time systems Standards Real-time systems Nonlinear systems Estimation error Vectors Upper bound Uncertainty Parameter estimation Fixed-time identification nonlinear discrete-time systems unknown dynamics

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Design Principles for Model Generalization and Scalable AI Integration in Radio Access Networks

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ieee COMMUNICATIONS MAGAZINE 2025年第1期63卷 84-91页

作者： Soldati, Pablo Ghadimi, Euhanna Demirel, Burak Wang, Yu Gaigalas, Raimundas Sintorn, Mathias Ericsson AB Stockholm Sweden

This article emphasizes the pivotal role of model generalization in enhancing performance and enabling scalable AI integration within radio communications. We outline design principles for model generalization in three key domains: environment for robustness, intents for adaptability to system objectives, and control tasks for reducing AI-driven control loops. Adopting these principles can decrease the number of models deployed and increase adaptability in diverse radio communication environments. To address the challenges of model generalization in communication systems, we propose a learning architecture that leverages centralization of training and data management functionalities, combined with distributed data generation. We illustrate these concepts by designing a generalized link adaptation algorithm, demonstrating the benefits of our proposed approach.

关键词： Artificial intelligence Adaptation models data models Task analysis Training Robustness Uncertainty

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Impedance Profile Prediction for Grid-Connected VSCs With data-driven Feature Extraction

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ieee TRANSACTIONS ON POWER ELECTRONICS 2025年第2期40卷 3043-3061页

作者： Wu, Yang Wu, Heng Cheng, Li Zhou, Jianyu Zhou, Zichao Chen, Minjie Wang, Xiongfei Aalborg Univ AAU Energy DK-9220 Aalborg Denmark KTH Royal Inst Technol Div Elect Power & Energy Syst S-10044 Stockholm Sweden Princeton Univ Andlinger Ctr Energy & Environm Dept Elect & Comp Engn Princeton NJ 08544 USA Aalborg Univ Dept Energy DK-9220 Aalborg Denmark

data-driven approach is promising for predicting impedance profile of grid-connected voltage source converters (VSCs) under a wide range of operating points (OPs). However, the conventional approaches rely on a one-to-one mapping between operating points and impedance profiles, which, as pointed out in this article, can be invalid for multiconverter systems. To tackle this challenge, this article proposes a stacked-autoencoder-based machine learning framework for the impedance profile predication of grid-connected VSCs, together with its detailed design guidelines. The proposed method uses features, instead of OPs, to characterize impedance profiles, and hence, it is scalable for multiconverter systems. Another benefit of the proposed method is the capability of predicting VSC impedance profiles at unstable OPs of the grid-VSC system. Such prediction can be realized solely based on data collected during stable operation, showcasing its potential for rapid online state estimation. Experiments on both single-VSC and multi-VSC systems validate the effectiveness of the proposed method.

关键词： Impedance Power conversion Converters Impedance measurement Feature extraction Perturbation methods Voltage control Principal component analysis Power system stability Neurons grid-connected voltage source converter (VSC) impedance profile machine learning

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Supervised learning Framework for Identification of Inverter control Mode and Estimation of Inverter-Based-Resource & Load Parameters

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ieee TRANSACTIONS ON INDUSTRY APPLICATIONS 2025年第2期61卷 2846-2857页

作者： Rizvi, Syed Muhammad Hur Satti, Shaban Ghias Ayaz, Muhammad Washington State Univ Pullman WA 99164 USA Univ Engn & Technol Taxila Pakistan Ghulam Ishaq Khan Inst Topi Pakistan

Modern distribution networks are becoming increasingly active in nature owing to the rapid proliferation of Inverter-based-Resources (IBRs). Since these IBRs are capable of operating in multiple control modes, it is important for the utility operator to accurately identify the control modes and also estimate the associated parameters. This paper presents a three-pronged data-driven approach for the real-time control mode identification of inverter-based resources (IBRs) and parameter estimation of IBRs and aggregated load. The proposed approach utilizes voltages, and power data at micro-PMU reporting rate at the node under observation for IBR mode identification and parameter estimation. A real-time implementation framework for the three-pronged approach having an adaptive window selection algorithm is developed to track the control mode along with parameters in real-time. The identified windows from an adaptive window selection algorithm are utilized by the three-pronged approach for i) identification of control modes, ii) parameter estimation of IBRs, and iii) aggregated load parameter estimation. The paper also performs robustness analysis by considering different noise levels in the PMU measurements. Random forest, K-nearest neighbors, and Decision tree algorithms are utilized for each prong of the three-pronged approach with RF outperforming the other algorithms. The proposed approach developed in this work is tested in detail on the ieee 13-node distribution test system to demonstrate its effectiveness.

关键词： Distribution systems data-driven ML ML ZIP load estimation ZIP load estimation distributed energy resources distributed energy resources load parameter estimation load parameter estimation load parameter estimation

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A Unified Model for Large-Scale Inexact Fixed-Point Iteration: A Stochastic Optimization Perspective

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ieee TRANSACTIONS ON AUTOMATIC control 2025年第4期70卷 2435-2449页

作者： Hashemi, Abolfazl Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

Calculating fixed points of a nonlinear function is a central problem in many areas of science and engineering with applications ranging from the study of dynamical systems to optimization and game theory. Fixed-point iteration methods provide a simple way to calculate the fixed point of nonexpansive mappings and they have been studied extensively. Emerging applications, however, necessitate the study of fixed-point calculation under various perturbations. For instance, in the data-driven identification of dynamical systems, the learning is typically erroneous, which in turn impacts the subsequent fixed calculations, which itself is an essential step for control. Motivated by such settings, in this work, we establish a general mathematical modeling framework for the study of inexact fixed-point iteration (FPI) algorithms. In doing so, we leverage and extend the recent advances in the stochastic optimization literature to derive new methods and convergence analysis results. In particular, adopting this view enables us to present a unified mathematical model to study the impact of inexact computations in both expansive and nonexpansive scenarios, a new technical approach for the analysis of inexact FPI methods, and a new inexact FPI method, which under certain assumptions, enjoys a faster convergence rate than traditional FPI algorithms in the expansive case.

关键词： Stochastic processes Optimization Convergence Mathematical models control systems Complexity theory Vectors Signal processing algorithms Reinforcement learning Noise Gradient methods iterative algorithms iterative methods optimization methods optimization models

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Neural Fault Observer-Based On-Board Prognostics for Automotive Electric Power Steering systems

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ieee ACCESS 2025年 13卷 42312-42321页

作者： Kim, Gyuwon Won, Jongick Ko, Sangjin Lee, Jinhwan HL Mando Corp Global Res & Dev Ctr Seongnam 13486 South Korea

Electric power steering (EPS) systems form a fundamental unit in modern automotive vehicles, providing motor assistance to aid the driver's manual maneuvering. Preventing loss of assist (LoA) in advance is critical for EPS systems to mitigate fatal accidents and reduce maintenance costs. As part of the recent interest in intelligent software-defined vehicles (SDV), data-driven approaches have gained much attention to overcome the limitations of conventional fail-safe mechanisms and provide value-added maintenance strategies. While related works in the field have shown promising results, they are limited to proof-of-concept studies validated under simulation and test-bed environments. Here, we present a novel deep learning (DL)-based method to detect EPS performance degradation using experimental data acquired from a commercial vehicle' s controller area network (CAN) bus. Our approach initially proposes a neural fault observer model and its adversarial learning scheme to represent the EPS system's normal operating dynamics. We demonstrate that our proposed model can detect degradation levels down to ten percent from normal conditions under various driving scenarios based on an anomaly detection mechanism that outperforms baseline methods in quantitative and qualitative measures. Furthermore, we provide physically relevant intuitions of our closed-box model's inference mechanism based on its attention-based saliency map to strengthen the reliability aspect of our data-driven approach. Lastly, we demonstrate that a quantized model can operate in real-time on an automotive electronic control unit (ECU) device.

关键词： Motors Torque Wheels Observers Degradation controller area networks Sensors Vehicle dynamics Logic Generative adversarial networks Deep learning electric power steering (EPS) fault observer prognostics software-defined vehicle (SDV)

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Resilient control of Networked Microgrids Using Vertical Federated Reinforcement learning: Designs and Real-Time Test-Bed Validations

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ieee TRANSACTIONS ON SMART GRID 2025年第2期16卷 1897-1910页

作者： Mukherjee, Sayak Hossain, Ramij Raja Mohiuddin, Sheik M. Liu, Yuan Du, Wei Adetola, Veronica Jinsiwale, Rohit A. Huang, Qiuhua Yin, Tianzhixi Singhal, Ankit Pacific Northwest Natl Lab Richland WA 99354 USA Colorado Sch Mines Elect Engn Dept Golden CO 80401 USA Indian Inst Technol Delhi Elect Engn Dept New Delhi 110016 India

Improving system-level resiliency of networked microgrids against adversarial cyber-attacks is an important aspect in the current regime of increased inverter-based resources (IBRs). To achieve that, this paper contributes in designing a hierarchical control layer, in conjunction with the existing control layers, resilient to adversarial attack signals. Considering model complexities, unknown dynamical behaviors of IBRs, and privacy issues regarding data sharing in multi-party-owned microgrids, designing such a control layer is non-trivial. Here, to tackle these issues, a novel federated reinforcement learning (Fed-RL) method is proposed. To grasp the interconnected dynamics of networked microgrids, the paper develops Federated Soft Actor-Critic (FedSAC) algorithm following the vertical structure of implementing Fed-RL. Next, utilizing the OpenAI Gym interface, we built a custom set-up in GridLAB-D/HELICS co-simulation platform, named Resilient RL Co-simulation (ResRLCoSIM), to train the RL agents with ieee 123-bus benchmark comprising 3 interconnected microgrids. Finally, the learned policies in the simulation are transferred to the real-time hardware-in-the-loop (HIL) test-bed developed using the high-fidelity Hypersim platform. Experiments show that the simulator-trained RL controllers achieve desirable performance with the test-bed platform, validating the minimization of the sim-to-real gap.

关键词： Microgrids Inverters Resilience Real-time systems Voltage control Frequency control Reinforcement learning Networked microgrid federated reinforcement learning resiliency real-time simulation sim-to-real

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Iterative learning control Design for Iteration-Varying State-of-Charge Profiles of Electric Vehicle Batteries

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ieee TRANSACTIONS ON systems MAN CYBERNETICS-systems 2025年第1期55卷 805-816页

作者： Nguyen, Dinh Hoa Kyushu Univ Int Inst Carbon Neutral Energy Res WPI I2CNER Inst Math Ind Fukuoka Japan

In this research, a battery control method is proposed to handle the repetitive but nonidentical daily state-of-charge (SoC) profiles of electric vehicle (EV) batteries. The proposed method employs an iterative learning control (ILC) framework having a quadratic performance index with iteration-varying weighting matrices. This results in iteration-varying ILC control gains to better cope with iteration-varying SoC profiles. Moreover, input constraints representing the limits on the ranges of the charge and discharge currents are considered, leading to an iteration-varying constrained convex optimization problem. This optimization problem is solved to obtain the ILC control input update via resolving its Lagrange dual problem. Next, a data-driven method based on the dynamic mode decomposition (DMD) approach is proposed to predict the SoC profile in the next weekday based on the SoC profiles in the current and previous weekdays. The predicted SoC profile is then served as the reference for the ILC tracking controller. Finally, the proposed methods are verified through extensive numerical simulations for a synthetic case and for a realistic, benchmark driving pattern. In the simulations, different ways of selecting the iteration-varying weighting matrices are introduced and their control performances are compared. It is also shown that the proposed ILC control design outperforms conventional P-type and adaptive ILC controllers as well as the classical proportional-integral-derivative controller on the tracking of the SoC profile based on the considered realistic driving pattern.

关键词： Battery charging-discharging control electric vehicle (EV) iteration-varying references iterative learning control (ILC) Urban Dynamometer Driving Schedule (UDDS)

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Beyond Performance of learning control Subject to Uncertainties and Noise: A Frequency-Domain Approach Applied to Wafer Stages

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ieee/CAA Journal of Automatica Sinica 2025年第1期12卷 198-214页

作者： Fazhi Song Ning Cui Shuaiqi Chen Kai Zhang Yang Liu Xinkai Chen Jiubin Tan IEEE the Center of Ultra-precision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of Ultra-Precision Intelligent Instrumentation Ministry of Industry and Information Technology the State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangdong University of Technology the Center of Ultraprecision Optoelectronic Instrument Engineering Harbin Institute of Technology the Key Laboratory of UltraPrecision Intelligent Instrumentation Ministry of Industry and Information Technology the Department of Electronic and Information Systems Shibaura Institute of Technology

The increasingly stringent performance requirement in integrated circuit manufacturing, characterized by smaller feature sizes and higher productivity, necessitates the wafer stage executing a extreme motion with the accuracy in terms of nanometers. This demanding requirement witnesses a widespread application of iterative learning control(ILC), given the repetitive nature of wafer scanning. ILC enables substantial performance improvement by using past measurement data in combination with the system model knowledge. However, challenges arise in cases where the data is contaminated by the stochastic noise, or when the system model exhibits significant uncertainties, constraining the achievable performance. In response to this issue, an extended state observer(ESO) based adaptive ILC approach is proposed in the frequency *** being model-based, it utilizes only a rough system model and then compensates for the resulting model uncertainties using an ESO, thereby achieving high robustness against uncertainties with minimal modeling effort. Additionally, an adaptive learning law is developed to mitigate the limited performance in the presence of stochastic noise, yielding high convergence accuracy yet without compromising convergence speed. Simulation and experimental comparisons with existing model-based and data-driven inversion-based ILC validate the effectiveness as well as the superiority of the proposed method.

关键词： Extended state observer learning control model uncertainties motion control stochastic noise

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Guided Bayesian Optimization: data-Efficient controller Tuning With Digital Twin

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ieee TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 11304-11317页

作者： Nobar, Mahdi Keller, Jurg Rupenyan, Alisa Khosravi, Mohammad Lygeros, John Swiss Fed Inst Technol Automatic Control Lab CH-8092 Zurich Switzerland FHNW Automat Inst CH-5210 Windisch Switzerland Zurich Univ Appl Sci ZHAW Ctr AI CH-8401 Winterthur Zurich Switzerland Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands

This article presents the guided Bayesian optimization (BO) algorithm as an efficient data-driven method for iteratively tuning closed-loop controller parameters using a digital twin of the system. The digital twin is built using closed-loop data acquired during standard BO iterations, and activated when the uncertainty in the Gaussian Process model of the optimization objective on the real system is high. We define a controller tuning framework independent of the controller or the plant structure. Our proposed methodology is model-free, making it suitable for nonlinear and unmodelled plants with measurement noise. The objective function consists of performance metrics modeled by Gaussian processes. We utilize the available information in the closed-loop system to progressively maintain a digital twin that guides the optimizer, improving the data efficiency of our method. Switching the digital twin on and off is triggered by our data-driven criteria related to the digital twin's uncertainty estimations in the BO tuning framework. Effectively, it replaces much of the exploration of the real system with exploration performed on the digital twin. We analyze the properties of our method in simulation and demonstrate its performance on two real closed-loop systems with different plant and controller structures. The experimental results show that our method requires fewer experiments on the physical plant than Bayesian optimization to find the optimal controller parameters. Note to Practitioners-Industrial applications typically are difficult to model due to disturbances. Bayesian optimization is a data-efficient iterative tuning method for a black box system in which the performance can only be measured given the control parameters. Iterative measurements involve operational costs. We propose a guided Bayesian optimization method that uses all information flow in a system to define a simplified digital twin of the system using out-of-the-box methods. It is continuously u

关键词： Tuning Digital twins Bayes methods control systems Measurement Cost function Uncertainty learning control systems iterative methods database systems control systems optimization methods

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