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data-driven control of Linear Parabolic Systems Using Koopman Eigenstructure Assignment

IEEE TRANSACTIONS ON AUTOMATIC control 2025年第1期70卷 665-672页

作者： Deutscher, Joachim Ulm Univ Inst Measurement Control & Microtechnol D-89081 Ulm Germany

This article considers the data-driven stabilization of linear boundary controlled parabolic PDEs by making use of the Koopman operator. For this, a Koopman eigenstructure assignment problem is solved, which amounts to determining a feedback of the Koopman open-loop eigenfunctionals assigning a desired finite set of closed-loop Koopman eigenvalues and eigenfunctionals to the closed-loop system. It is shown that the designed controller only needs a finite number of open-loop Koopman eigenvalues and modes of the state. They are determined by extending the classical Krylov-dynamic mode decomposition (DMD) to parabolic systems. For this, only a finite number of pointlike outputs and their temporal samples, as well as temporal samples of the inputs, are required, resulting in a data-driven solution to the eigenstructure assignment problem. Exponential stability of the closed-loop system in the presence of small Krylov-DMD errors is verified. An unstable diffusion-reaction system demonstrates the new data-driven controller design technique for distributed-parameter systems.

关键词： Eigenvalues and eigenfunctions Generators Closed loop systems State feedback Aerospace electronics Systematics Spectral analysis data-driven control Koopman operator modal approach parabolic systems state feedback

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data-driven control With Prescribed-Time Convergence for Discrete-Time Nonlinear Systems

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 4961-4971页

作者： Li, Yijia Ou, Xianhua Chen, Peng He, Xiongxiong Jiang, Qianru Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China

In this paper, the issue of data-driven prescribed-time convergence control for certain types of discrete-time nonlinear systems is investigated. The dynamic linearization technique and the discrete prescribed-time gain-scheduled feedback function have been combined to create a model-free adaptive prescribed time control strategy. To keep the steady-state error within a bespoke steady-state error band, the controller is switched to a steady-state controller once the system has completed its prescribed time control task. Theoretically, it is demonstrated that the error dynamics can gradually compress from the initial error to the steady-state error band during the initial and prescribed time periods. Furthermore, all of the rule designs in this study are data-driven and solely rely on system input and output data. Manipulator experiments and numerical modeling demonstrate the viability of the proposed strategy. Note to Practitioners-The motivation for this paper stems from the need to design a prescribed-time control method for the industrial robot trajectory tracking control tasks. Prescribed-time control method offers a potential to improve both the speed and coordination of industrial robot trajectory tracking. However, due to the increasing complexity of the robot structure and operating environment, it has brought great difficulties to the use of model-based prescribed-time control. To address this issue, a data-driven prescribed-time control approach is proposed, the controller design and convergence proof through mathematical derivations are also provided. Finally, the effectiveness of the proposed control scheme is validated through numerical simulations and industrial robotic manipulator experiments. This research contributes a viable and efficient time-optimal control method for trajectory-tracking tasks in the realm of industrial robotics.

关键词： control systems Convergence Steady-state Nonlinear systems Adaptation models data models Observers data-driven control prescribed-time convergence model-free adaptive control (MFAC) sliding mode control (SMC)

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data-driven control for Continuous-Time Delayed Systems Under Denial-of-Service Attacks and Unknown Measurement Noise

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2025年第6期55卷 3814-3823页

作者： Zou, Yi Tian, Engang Chen, Huwei Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China Jiangsu Aerosp Power Machinery & Elect Co Ltd Technol Dev Ctr Taizhou 225300 Peoples R China

This article proposes a novel data-driven approach for studying the stabilization of continuous-time delayed systems (CDSs) with both unknown model parameters and denial-of-service (DoS) attacks. Due to the effects of DoS attacks, such controlled CDSs can be viewed as a switched system with two modes: a stable mode during dormant intervals and an unstable one during attack intervals. Because of the switching character of the CDS with DoS attacks, some well-known data-driven control methods can not be used directly. To tackle this issue, a new relationship between the dormancy and activation periods of DoS attacks is built, which offers a more reasonable description of DoS attacks. Additionally, in the presence of measurement noise, a novel lemma (Lemma 3) is introduced to establish a data-based representation of CDSs using noisy measurement data. Without the need of explicit system parameter identification, stabilization criteria and control gain matrix are derived solely based on the input-state data. Finally, the effectiveness and good performance of the proposed method are illustrated through a numerical example and a power system.

关键词： Noise measurement Denial-of-service attack control systems Symmetric matrices Noise Switched systems Delay effects Asymptotic stability Vectors Power system stability Continuous-time system data-driven control Denial-of-Service (DoS) attacks noisy data time delay

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Cascade MFAC-based data-driven control for aircraft in 4D trajectory tracking

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TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND control 2025年第6期47卷 1217-1224页

作者： Jiang, Gaoyang Zhao, Yuandi Hou, Zhongsheng Civil Aviat Univ China Coll Air Traff Management Tianjin 300300 Peoples R China Qingdao Univ Sch Automat Qingdao Peoples R China

This study aims to explore a trajectory tracking control approach in four-dimensional (4D) trajectory operation by employing cascade model-free adaptive control (MFAC) technique. The primary objective is to tackle the complexities associated with modeling aircraft mechanisms in the context of tracking control. By utilizing measurable input and output data, an equivalent dynamic linearized data model of the aircraft is constructed. Subsequently, position and speed controllers are developed based on MIMO-CFDL-MFAC and SISO-CFDL-MFAC, respectively. These controllers are then integrated to form an active-follower control system. Finally, a numerical simulation was conducted to track the entire 4D trajectory, encompassing climb, cruise, and descent stages, using historical data from flight CX8176 as a reference trajectory. Unlike previous researches, the proposed method eliminates the requirement for an aircraft mechanism model in the controller design, and it is verified using real flight data as a reference trajectory. The findings demonstrate that the cascade MFAC method can effectively minimize trajectory tracking errors, while also offering advantages in terms of energy conservation and emission reduction.

关键词： Air transportation trajectory tracking model-free adaptive control four-dimensional trajectory data-driven control

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When Sampling Works in data-driven control: Informativity for Stabilization in Continuous Time

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IEEE TRANSACTIONS ON AUTOMATIC control 2025年第1期70卷 565-572页

作者： Eising, Jaap Cortes, Jorge Swiss Fed Inst Technol Dept Informat Technol & Elect Engn CH-8092 Zurich Switzerland Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA

This article introduces a notion of data informativity for stabilization tailored to continuous-time signals and systems. We establish results comparable to those known for discrete-time systems with sampled data. We justify that additional assumptions on the properties of the noise signals are needed to understand when sampled versions of continuous-time signals are informative for stabilization, thereby introducing the notions of square Lipschitzness and total bounded variation. This allows us to connect the continuous and discrete domains, yielding sufficient conditions to synthesize a stabilizing controller for the true continuous-time system on the basis of sampled data. Simulations illustrate our results.

关键词： Noise Trajectory Noise measurement Particle measurements Atmospheric measurements Time measurement Linear systems data-driven control linear systems sampling methods system identification

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Type-3 Fuzzy data-driven control of Heterogeneous Multi-Agent Systems

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IEEE ACCESS 2025年 13卷 32306-32319页

作者： Liang, Yaqing Tian, Man-Wen Du, Changdong Alattas, Khalid A. Fekih, Afef Mohammadzadeh, Ardashir Nanjing Agr Univ Sch Econ & Management Nanjing 210095 Peoples R China Guangdong Univ Sci & Technol Sch Management Dongguan 523083 Peoples R China Shenyang Univ Technol Sch Artificial Intelligence Shenyang 110870 Peoples R China Univ Jeddah Dept Comp Sci & Artificial Intelligence Jeddah 23218 Saudi Arabia Univ Louisiana Lafayette Dept Elect & Comp Engn Lafayette LA 70504 USA Sakarya Univ Fac Engn Dept Elect & Elect Engn TR-54050 Sakarya Turkiye

This study investigates the data-based consensus problem of discrete-time heterogeneous multi-agent systems (MASs) through the development of distributed protocols. A type-3 (T3) fuzzy logic system (FLS) is presented to effectively model the inherent nonlinearities present in MASs, allowing for more accurate representation of agent dynamics. Utilizing a Lyapunov stability approach alongside a linear matrix inequalities (LMIs) framework, the sufficient conditions are derived for designing the data-driven distributed control protocols. The results ensure that the consensus error dynamics are asymptotically stable, thereby guaranteeing the achievement of consensus among agents. Furthermore, the optimal linear quadratic regulator (LQR) problem is addressed within the context of heterogeneous MASs, providing an additional layer of control performance optimization. Also, the design procedure is analyzed under conditions of noisy data, which is critical for real-world applications where sensor inaccuracies are prevalent. To validate the theoretical findings, extensive simulations are conduced that showcase the effectiveness and robustness of the proposed methods. The implementation is demonstrated on a network comprising four multiple autonomous underwater vehicles (AUVs), hyper-chaotic systems, and various configurations of discrete-time MASs. The results highlight the advantages of suggested data-driven control approach, emphasizing its practicality and efficiency in achieving consensus in complex multi-agent environments. This work paves the way for future research in robust control strategies for heterogeneous physical and financial systems operating under uncertainty. For example, by the use of the suggested data-driven approach to achieve consensus among heterogeneous agents, the financial institutions can enhance their decision-making processes, and optimize performance under uncertainty.

关键词： Uncertainty Linear matrix inequalities Vehicle dynamics Noise measurement Fuzzy logic Protocols Predictive control Quality control Optimization Synchronization data-driven control heterogeneous multi-agent physical and financial systems distributed protocols LQR problem noisy data

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Constant Reference Tracking in data-driven control

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2025年第2期23卷 611-619页

作者： Umar, Abdul Aris Kim, Jung-Su Seoul Natl Univ Sci & Technol Dept Elect & Informat Engn Seoul 01811 South Korea

Although the data-driven control design techniques have gotten attention recently, few results address the tracking problem using data-based control formulations. Considering this, this paper presents a data-driven control method to drive the output of a system to its desired constant reference without using any model knowledge. To this end, first, this paper shows how to take the integral action into account for the data-driven control design for the reference tracking problem. Second, a closed-loop representation of the data-driven tracking controller with integral action is derived. Then, based on the representation, linear matrix inequality (LMI) conditions are derived for gain selection. The effectiveness of the proposed method is demonstrated using the speed control of a DC motor. Thanks to the integral action property, it is shown that the tracking error remains zero under constant disturbance affecting the system.

关键词： data-driven control integral action linear system robust control tracking problem

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Meta-learning for model-reference data-driven control

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AUTOMATICA 2025年 172卷

作者： Busetto, Riccardo Breschi, Valentina Formentin, Simone Politecn Milan Dipartimento Elettron Bioingn & Informaz Via Ponzio 34-5 Milan Italy Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands

One-shot direct model-reference control design techniques, like the Virtual Reference Feedback Tuning (VRFT) approach, offer time-saving solutions for calibrating fixed-structure controllers. Nonetheless, such methods are known to be highly sensitive to the quality of data, often requiring long and costly experiments to attain acceptable closed-loop performance. These features might prevent the widespread adoption of such techniques, especially in low-data regimes. In this paper, we argue that the inherent similarity of many industrially relevant systems may come at hand, offering additional information from plants that are similar (yet not equal) to the system one aims to control. Assuming that this supplementary information is available, we propose a novel, direct design approach that leverages data from similar plants, the knowledge of controllers calibrated on them, and the corresponding closed-loop performance to enhance model-reference control design. By constructing the new controller as a combination of the available ones, our approach exploits all the available priors following a meta-learning philosophy, ensuring non-decreasing performance. An extensive numerical analysis supports our claims, highlighting the effectiveness of the proposed method in achieving performance comparable to iterative approaches, while retaining the efficiency of one-shot direct data-driven methods like VRFT. (c) 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Meta-learning data-driven control Virtual reference feedback tuning Model-reference control

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data-driven LPV control for Harmonic Disturbance Rejection in a Hybrid Isolation Platform

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IEEE TRANSACTIONS ON control SYSTEMS TECHNOLOGY 2025年

作者： Klauser, Elias Karimi, Alireza CSEM SA CH-2002 Neuchatel Switzerland Ecole Polytech Fed Lausanne Lab Automat CH-1015 Lausanne Switzerland

A novel approach for linear parameter-varying (LPV) controller synthesis for adaptive rejection of frequency-varying sinusoidal disturbances is proposed. Only the frequency response data of a linear time-invariant (LTI) multiple-input-multiple-output (MIMO) system are used to design the LPV controller that stabilizes the system for arbitrarily fast variation of the disturbance frequencies. Global stability is achieved thanks to the specific structure of the LPV controller and the use of integral quadratic constraints (IQCs) to represent the frequency variations. The LPV controller is designed by convex optimization in the frequency domain. A hybrid microvibration damping platform (MIVIDA) for space applications is considered for experimental validation of the proposed method. An LPV controller for rejection of unknown frequency-varying sinusoidal disturbances is designed and implemented on the real system. Experimental results demonstrate the effectiveness of the proposed approach in asymptotically rejecting disturbances and ensuring closed-loop stability against arbitrarily fast variations in disturbance frequencies.

关键词： Frequency-domain analysis Stability analysis Linear systems control design Adaptation models Uncertainty control systems Processor scheduling Power system stability Perturbation methods Adaptive control convex optimization data-driven control frequency-domain identification for control linear parameter-varying (LPV) systems

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Observer-Based Attack Detection and data-driven Resilient control for Heterogeneous Nonlinear Nonaffine Mass Under FDI Attacks

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INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR control 2025年第3期35卷 1079-1090页

作者： Yang, Chen Che, Wei-Wei Qingdao Univ Sch Automat Shandong Key Lab Ind Control Technol Qingdao Peoples R China Northeastern Univ Coll Informat Sci & Engn State Key Lab Synthet Automat Proc Ind Shenyang Peoples R China

The attack detection and data-driven resilient control problem for heterogeneous nonlinear nonaffine multi-agent systems (MASs) in the presence of intermittent false data injection (FDI) attacks is investigated in this article. First, an attack detection and isolation mechanism based on the distributed observer are designed to determine the attack signals and isolate the attacked communication links in time. Then, in order to solve the problem of the unknown system model, the dynamic linearization method is used to convert the nonlinear nonaffine MASs into an equivalent linear data model. Further, based on the linear data model, the data-driven resilient controller with the attack isolation mechanism is designed to achieve the leader-following tracking control objective. Finally, the simulation result illustrates the performance of the proposed scheme with comparisons.

关键词： attack detection and isolation mechanism data-driven control false data injection attack robustness security control

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