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

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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data-driven Optimal control via Linear Programming: Boundedness Guarantees

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IEEE TRANSACTIONS ON AUTOMATIC control 2025年第3期70卷 1683-1697页

作者： Falconi, Lucia Martinelli, Andrea Lygeros, John Univ Padua Dept Informat Engn I-35131 Padua Italy Swiss Fed Inst Technol Automat Control Lab CH-8092 Zurich Switzerland

The linear programming (LP) approach is, together with value iteration and policy iteration, one of the three fundamental methods to solve optimal control problems in a dynamic programming setting. Despite its simple formulation, versatility, and predisposition to be employed in model-free settings, the LP approach has not enjoyed the same popularity as the other methods. The reason is the often poor scalability of the exact LP approach and the difficulty to obtain bounded solutions for a reasonable amount of constraints. We mitigate these issues here, by investigating fundamental geometric features of the LP and developing sufficient conditions to guarantee finite solutions with minimal constraints. In the model-free context, we show that boundedness can be guaranteed by a suitable choice of dataset and objective function.

关键词： data-driven control linear programming (LP) linear programming (LP) linear programming (LP) Approximate dynamic programming (ADP) Approximate dynamic programming (ADP) Approximate dynamic programming (ADP) Approximate dynamic programming (ADP) Approximate dynamic programming (ADP) data-driven control linear programming (LP) optimal control

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Decentralized Robust data-driven Predictive control for Smoothing Mixed Traffic Flow

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年第2期26卷 2075-2090页

作者： Shang, Xu Wang, Jiawei Zheng, Yang Univ Calif San Diego Dept Elect & Comp Engn San Diego CA 92093 USA Univ Michigan Dept Civil & Environm Engn Ann Arbor MI 48109 USA

In a mixed traffic with connected automated vehicles (CAVs) and human-driven vehicles (HDVs), data-driven predictive control of CAVs promises system-wide traffic performance improvements. Yet, most existing approaches focus on a centralized setup, which is computationally unscalable while failing to protect data privacy. The robustness against unknown disturbances has not been well addressed either, causing safety concerns. In this paper, we propose a decentralized robust DeeP-LCC (data-EnablEd Predictive Leading Cruise control) approach for CAVs to smooth mixed traffic. In particular, each CAV computes its control input based on locally available data from its involved subsystem. Meanwhile, the interaction between neighboring subsystems is modeled as a bounded disturbance, for which appropriate estimation methods are proposed. Then, we formulate a robust optimization problem and present its tractable computational solutions. Compared with the centralized formulation, our method greatly reduces computation complexity with better safety performance, while naturally preserving data privacy. Extensive traffic simulations validate its wave-dampening ability, safety performance, and computational benefits.

关键词： Safety Predictive control Computational modeling Optimization Estimation data privacy Cruise control Robustness Computational efficiency Vehicle dynamics Connected vehicles mixed traffic data-driven control model predictive control (MPC) decentralized control

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Robust data-driven control for nonlinear systems using the Koopman operator*

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IFAC-PapersOnLine 2023年第2期56卷 2257-2262页

作者： Robin Strässer Julian Berberich Frank Allgöwer University of Stuttgart Institute for Systems Theory and Automatic Control Germany

data-driven analysis and control of dynamical systems have gained a lot of interest in recent years. While the class of linear systems is well studied, theoretical results for nonlinear systems are still rare. In this paper, we present a data-driven controller design method for discrete-time control-affine nonlinear systems. Our approach relies on the Koopman operator, which is a linear but infinite-dimensional operator lifting the nonlinear system to a higher-dimensional space. Particularly, we derive a linear fractional representation of a lifted bilinear system representation based on measured data. Further, we restrict the lifting to finite dimensions, but account for the truncation error using a finite-gain argument. We derive a linear matrix inequality based design procedure to guarantee robust local stability for the resulting bilinear system for all error terms satisfying the finite-gain bound and, thus, also for the underlying nonlinear system. Finally, we apply the developed design method to the nonlinear Van der Pol oscillator.

关键词： Koopman operator data-driven control robust control learning nonlinear systems

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An Adaptive data-driven-Based control for Voltage control Loop of Grid-Forming Converters in Variable Inertia MGs

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IEEE ACCESS 2024年 12卷 58143-58155页

作者： Pinthurat, Watcharakorn Kongsuk, Prayad Surinkaew, Tossaporn Marungsri, Boonruang Rajamangala Univ Technol Tawan Ok Dept Elect Engn Chanthaburi Campus Chanthaburi 22210 Thailand King Mongkuts Inst Technol Ladkrabang Fac Engn Sch Int & Interdisciplinary Engn Bangkok 10520 Thailand Suranaree Univ Technol Sch Elect Engn Nakhon Ratchasima 30000 Thailand

In the transition towards sustainable energy systems, microgrid (MG) plays a pivotal role, especially in the context of variable-inertia MGs that integrate renewable energy sources (RESs) and distributed energy resources (DERs). Maintaining stable voltage control within such grids is imperative for reliable operation. This paper presents an adaptive data-driven control technique for the voltage control loop of grid-forming converters in variable-inertia MGs. The primary objective is to enhance control performance while accommodating the unpredictable nature of renewable energy sources. The approach utilizes advanced data-driven algorithms to continuously monitor and adjust control parameters based on real-time grid conditions. This adaptability allows for effective management of varying inertia and load demand, ensuring optimal grid performance. The data-driven nature of the approach enables self-adaptability, making it suitable for the dynamic MG environment. This new approach is a noteworthy advancement in controlling RESs and DERs to maintain stable voltage in MGs, without needing precise knowledge of the MG's parameters. Simulation tests and real-world examples confirm that the adaptive data-driven control method effectively optimizes voltage control in MGs with variable inertia, especially those with a high presence of RESs and DERs.

关键词： Microgrid control voltage control renewable energy sources data-driven control variable-inertia MGs sustainable energy systems

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Improved data-driven Adaptive control Structure Against Input and Output Saturation

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INTERNATIONAL JOURNAL OF control AUTOMATION AND SYSTEMS 2024年第10期22卷 2981-2989页

作者： Asadi, Yasin Farsangi, Malihe Maghfouri Rezaei, Mohammad Hadi Shahid Bahonar Univ Kerman Dept Elect Engn Kerman Iran Yazd Univ Dept Elect Engn Yazd Iran

This article presents an improved data-driven adaptive control structure to address the problem of input and output saturation in unknown nonlinear systems with multiple inputs and multiple outputs. In the suggested structure, a virtual model of the controlled system is initially built utilizing a multi-layered group method of data handling neural network. The control signal is then applied to this virtual model to predict the output before being applied to the system. If the predicted output is saturated, the control signals are readjusted to prevent saturation and are then applied to the system. By using this proposed structure, the performance of model-free adaptive control against input/output saturation phenomena is improved and the occurrence of saturation is prevented. Based on Lyapunov's theory, the stability of the suggested structure is proven. The controller has been applied to an interconnected three-tank system and a subway train which results clearly illustrate the advantages of the suggested method over the traditional form of model-free adaptive control design.

关键词： data-driven control nonlinear adaptive control nonlinear systems saturation

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Automatic Tracking of Surgical Instruments with a Continuum Laparoscope Using data-driven control in Robotic Surgery

ADVANCED INTELLIGENT SYSTEMS

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ADVANCED INTELLIGENT SYSTEMS 2023年第2期5卷

作者： Kong, Xiaowen Mo, Hangjie Dong, Erbao Liu, Yunhui Sun, Dong Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Hefei Peoples R China City Univ Hong Kong Dept Biomed Engn Hong Kong 999077 Peoples R China Chinese Univ Hong Kong Dept Mech & Automat Engn Hong Kong 999077 Peoples R China

In existing surgery process, surgeons need to manually adjust the laparoscopes to provide a better field of view (FOV) during operation, which may distract surgeons and slow down the surgery process. Herein, a data-driven control method that uses a continuum laparoscope to adjust the FOV by tracking the surgical instruments is presented. A Koopman-based system identification method is first applied to linearize the nonlinear system. Shifted Chebyshev polynomials are used to construct observation functions that transfer low-dimension observations to high-dimension ones. The Koopman operator is approximated using a finite-dimensional estimation method. An optimal controller is further developed according to the trained linear dynamic model. Furthermore, a learning-based pose estimation framework is designed to detect keypoints on surgical instruments and provides visual feedback for the control system. Compared with other detection methods, the proposed scheme achieves a higher detection precision and provides more optional keypoints for tracking. Simulation and experiments validate the feasibility of the proposed control method. Experimental results show that the proposed method can automatically adjust the field of continuum laparoscope by tracking surgical instruments in real time and satisfy the clinical requirements.

关键词： continuum laparoscope data-driven control keypoint detection robotic surgery visual feedback

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data-driven Safe control of Uncertain Linear Systems Under Aleatory Uncertainty

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IEEE TRANSACTIONS ON AUTOMATIC control 2024年第1期69卷 551-558页

作者： Modares, Hamidreza Michigan State Univ Dept Mech Engn E Lansing MI 48863 USA

Safe control of constrained uncertain linear systems under aleatory uncertainty is considered. Aleatory uncertainty characterizes random noises and is modeled by a probability distribution function (pdf). data-based probabilistic safe controllers are designed for the cases where the noise pdf is 1) zero-mean Gaussian with a known covariance, 2) zero-mean Gaussian with an uncertain covariance, and 3) zero-mean non-Gaussian with an unknown distribution. Easy-to-check-model-based conditions for guaranteeing probabilistic safety are provided for the first case by introducing probabilistic $\lambda$-contractive sets. These results are then extended to the second and third cases by leveraging distributionally-robust probabilistic safe control and conditional-value-at-risk-based probabilistic safe control, respectively. data-based implementations of these probabilistic safe controllers are then considered. Moreover, an upper bound on the minimal risk level, under which the existence of a safe controller is guaranteed, is learned using collected data. A simulation example is provided to show the effectiveness of the proposed approach.

关键词： Chance constraints data-driven control probabilistic safe control

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A continuous-time fundamental lemma and its application in data-driven optimal control☆

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SYSTEMS & control LETTERS 2024年 194卷

作者： Schmitz, Philipp Faulwasser, Timm Rapisarda, Paolo Worthmann, Karl Tech Univ Ilmenau Inst Math Optimizat Based Control Grp Ilmenau Germany Inst Control Syst Hamburg Univ Technol Hamburg Germany Univ Southampton Southampton England

data-driven control of discrete-time and continuous-time systems is of tremendous research interest. In this paper, we explore data-driven optimal control of continuous-time linear systems using input-output data. Based on a density result, we rigorously derive error bounds for finite-order polynomial approximations of elements of the system behavior. To this end, we leverage a link between latent variables and flat outputs of controllable systems. Combined with a continuous-time counterpart of Willems et al.'s fundamental lemma, we characterize the suboptimality resulting from polynomial approximations in data-driven linear-quadratic optimal control. Finally, we draw upon a numerical example to illustrate our results.

关键词： Continuous time data-driven control Differential flatness Identifiable Persistency of excitation Polynomial approximation

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