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Robust direct data-driven control for probabilistic systems

SYSTEMS & control LETTERS 2025年 196卷

作者： von Rohr, Alexander Likhachev, Dmitrii Trimpe, Sebastian Rhein Westfal TH Aachen Inst Data Sci Mech Engn Aachen Germany

We propose a data-driven control method for systems with aleatoric uncertainty, such as robot fleets with variations between agents. Our method leverages shared trajectory data to increase the robustness of the designed controller and thus facilitate transfer to new variations without the need for prior parameter and uncertainty estimation. In contrast to existing work on experience transfer for performance, our approach focuses on robustness and uses data collected from multiple realizations to guarantee generalization to unseen ones. Our method is based on scenario optimization combined with recent formulations for direct data-driven control. We derive upper bounds on the minimal amount of data required to provably achieve quadratic stability for probabilistic systems with aleatoric uncertainty and demonstrate the benefits of our data-driven method through a numerical example. We find that the learned controllers generalize well to high variations in the dynamics even when based on only a few short open-loop trajectories. Robust experience transfer enables the design of safe and robust controllers that work "out of the box"without additional learning during deployment.

关键词： direct data-driven control Probabilistic uncertainty Scenario optimization

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direct data-driven control with embedded anti-windup compensation 2

Direct data-driven control with embedded anti-windup compens...

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2nd Annual Conference on Learning for Dynamics and control (L4DC)

作者： Breschi, Valentina Formentin, Simone Politecn Milan Dipartimento Elettron & Informaz Milan Italy

Input saturation is an ubiquitous nonlinearity in control systems and arises from the fact that all actuators are subject to a maximum power, thereby resulting in a hard limitation on the allowable magnitude of the input effort. In the scientific literature, anti-windup augmentation has been proposed to recover the desired linear closed-loop dynamics during transients, but the effectiveness of such a compensation is strongly linked to the accuracy of the mathematical model of the plant. In this work, it is shown that a feedback controller with embedded anti-windup compensator can be directly identified from data, by suitably extending the existing data-driven design theory. The effectiveness of the resulting method is illustrated on a benchmark simulation example.

关键词： direct data-driven control anti-windup input saturation

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Auto-tuning of reference models in direct data-driven control

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AUTOMATICA 2023年 155卷

作者： Masti, Daniele Breschi, Valentina Formentin, Simone Bemporad, Alberto IMT Sch Adv Studies Lucca Piazza San Francesco 19 Lucca Italy Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands Politecn Milan Dipartimento Elettron Bioingn & Informaz Via Ponzio 34-5 Milan Italy

Designing controllers directly from data often requires choosing a reference closed-loop model, whose behavior should be reproduced as tightly as possible by the actual closed-loop system via the selected controller structure (e.g., PID). Within a linear setting, we present a derivative-based approach to jointly select the reference model and controller parameters directly from data. The proposed strategy allows one to maximize closed-loop performance while enforcing user-defined constraints, and it is designed to handle non-minimum phase dynamics. The effectiveness of the proposed approach is shown through three numerical case studies.& COPY;2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： data-based control Model -reference control Autotuning direct data-driven control

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direct data-driven Robust Predictive control for Lur'e Systems based on Tailored data Sampling 8

Direct Data-Driven Robust Predictive Control for Lur'e Syste...

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8th IFAC Conference on Nonlinear Model Predictive control (NMPC)

作者： Hoang Hai Nguyen Findeisen, Rolf Tech Univ Darmstadt Control & Cyber Phys Syst Lab Darmstadt Germany

Predictive control requires a model of the system to compute the input. If the nominal model is not known, data-driven model predictive control approaches can be employed, which enables to obtain the input directly from past measured trajectories. We consider the problem of data-driven predictive control for Lur'e systems. Existing data-driven control approaches for Lur'e type systems assume that the output data of the nonlinearity is available, enabling the use of Willems' Fundamental Lemma. We propose to utilize prior knowledge of the systems into the data collection process for Lur'e systems. The data is purposely collected in the region where the system behaves nearly linear, while the nonlinearity effects are considered as noise in the controller design. Using the tailored data, we can formulate the control problem as a semi-definite optimization problem exploiting robust control ideas. The resulting controller stabilizes the closed-loop system asymptotically and guarantees constraint satisfaction. A numerical example is conducted to illustrate the method. Copyright (C) 2024 The Authors.

关键词： direct data-driven control Linear Matrix Inequalities Predictive control Nonlinear control

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direct data-driven Robust Predictive control for Lur'e Systems based on Tailored data Sampling

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IFAC-PapersOnLine 2024年第18期58卷 220-225页

作者： Hoang Hai Nguyen Rolf Findeisen Control and Cyber-Physical Systems Laboratory Technical University of Darmstadt Germany

Predictive control requires a model of the system to compute the input. If the nominal model is not known, data-driven model predictive control approaches can be employed, which enables to obtain the input directly from past measured trajectories. We consider the problem of data-driven predictive control for Lur'e systems. Existing data-driven control approaches for Lur'e type systems assume that the output data of the nonlinearity is available, enabling the use of Willems’ Fundamental Lemma. We propose to utilize prior knowledge of the systems into the data collection process for Lur'e systems. The data is purposely collected in the region where the system behaves nearly linear, while the nonlinearity effects are considered as noise in the controller design. Using the tailored data, we can formulate the control problem as a semi-definite optimization problem exploiting robust control ideas. The resulting controller stabilizes the closed-loop system asymptotically and guarantees constraint satisfaction. A numerical example is conducted to illustrate the method.

关键词： direct data-driven control Linear Matrix Inequalities Predictive control Nonlinear control

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direct data-driven discounted infinite horizon linear quadratic regulator with robustness guarantees☆

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AUTOMATICA 2025年 175卷

作者： Esmzad, Ramin Modares, Hamidreza Michigan State Univ Dept Mech Engn E Lansing MI 48824 USA

This paper presents a one-shot learning approach with performance and robustness guarantees for the linear quadratic regulator (LQR) control of stochastic linear systems. Even though data-based LQR control has been widely considered, existing results suffer either from data hungriness due to the inherently iterative nature of the optimization formulation (e.g., value learning or policy gradient reinforcement learning algorithms) or from a lack of robustness guarantees in one-shot non-iterative algorithms. To avoid data hungriness while ensuing robustness guarantees, an adaptive dynamic programming formalization of the LQR is presented that relies on solving a Bellman inequality. The control gain and the value function are directly learned by using a control-oriented approach that characterizes the closed-loop system using data and a decision variable from which the control is obtained. This closed-loop characterization is noise-dependent. The effect of the closed-loop system noise on the Bellman inequality is considered to ensure both robust stability and suboptimal performance despite ignoring the measurement noise. To ensure robust stability, it is shown that this system characterization leads to a closed-loop system with multiplicative and additive noise, enabling the application of distributional robust control techniques. The analysis of the suboptimality gap reveals that robustness can be achieved by construction without the need for regularization or parameter tuning. The simulation results on the active car suspension problem demonstrate the superiority of the proposed method in terms of robustness and performance gap compared to existing methods. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

关键词： direct data-driven control Linear quadratic regulator Robustness Suboptimality gap

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Adaptive and Robust direct data-driven controller for Surface Vessel Navigation 15

Adaptive and Robust Direct Data-Driven Controller for Surfac...

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15th IFAC Conference on control Applications in Marine Systems, Robotics and Vehicles (CAMS)

作者： Mandic, Luka Nad, Dula Miskovic, Nikola Univ Zagreb Fac Elect Engn & Comp Dept LABUST Lab Underwater Syst & Technol Unska 3 Zagreb Croatia

The challenging and unpredictable nature of the marine environment poses challenges for both human operators and non adaptive automatic controllers. This paper introduces and applies an extension of the DeePC algorithm as the vessel controller, aligning with the increasing trend of data-driven reference tracking in control system theory. The algorithm records input-output pairs during vessel operation, constructs a linear model that mimics the recorded system's trajectory and uses filtered and corrected trajectory required for the future behavior prediction in the direct data-driven scheme. The resulting controller is closed-loop stable, outperforms conventional control methods and is able to adapt to changing model parameters over time. The method is evaluated on a simulated Unmanned Surface Vehicle (USV), where it is initially used as a velocity controller and then extended to a dynamic positioning controller. Copyright (c) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： adaptive control direct data-driven control Unmanned Surface Vessel (USV) dynamic positioning

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On input design for direct data-driven controller tuning *

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IFAC Proceedings Volumes 2012年第16期45卷 1466-1471页

作者： Simone Formentin Alireza Karimi Sergio M. Savaresi Dipartimento di Elettronica e Informazione Politecnico di Milano Piazza L. Da Vinci 32 20133 Milano (Italy) Laboratoire d'Automatique École Polytechnique Fédérale de Lausanne (EPFL) CH-1015 Lausanne (Switzerland)

In recent years, noniterative Correlation-based Tuning (CbT) and Virtual Reference Feedback Tuning (VRFT) have been proposed as an alternative to the standard model-based approach for model-reference control design. In this work, the problem of input design for direct data-driven controller tuning methods is investigated. For bounded input energy, the excitation signal is designed such that the bias on the expectation of the control criterion is reduced. The above strategy is numerically tested on a benchmark example.

关键词： VRFT correlation-based tuning input design direct data-driven control

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direct data-driven strategy for closed-loop aircraft flutter test

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AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY 2023年第5期95卷 749-756页

作者： Wang, Jianhong Ramirez-Mendoza, Ricardo A. Jiangxi Univ Sci & Technol Coll Elect Engn & Automat Ganzhou Peoples R China

PurposeThis new paper aims to extend the authors' previous contributions about open-loop aircraft flutter test to closed-loop aircraft flutter test by virtue of the proposed direct data-driven strategy. After feeding back the output signal to the input and introducing one feedback controller in the adding feedback loop, two parts, i.e. unknown aircraft flutter model and unknown feedback controller, exist in this closed-loop aircraft flutter system, simultaneously, whose input and output are all corrupted with external noise. Because of the relations between aircraft flutter model parameters and the unknown aircraft model, direct data-driven identification is proposed to identify that aircraft flutter model, then some identification algorithms and their statistical analysis are given through the authors' own derivations. As the feedback controller can suppress the aircraft flutter or guarantee the flutter response converge to one desired constant value, the direct data-driven control is applied to design that feedback controller only through the observed data sequence directly. Numerical simulation results have demonstrated the efficiency of the proposed direct data-driven strategy. Generally, during our new information age, direct data-driven strategy is widely applied around our living life. Design/methodology/approachFirst, consider one more complex closed loop stochastic aircraft flutter model, whose input-output are all corrupted with external noise. Second, for the identification problem of closed-loop aircraft flutter model parameters, new identification algorithm and some considerations are given to the corresponding direct data-driven identification. Third, to design that feedback controller, existing in that closed-loop aircraft flutter model, direct data-driven control is proposed to design the feedback controller, which suppresses the flutter response actively. FindingsA novel direct data-driven strategy is proposed to achieve the dual missions, i.e. iden

关键词： Aircraft flutter Closed loop direct-data driven identification direct data-driven control

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Kalman Filter-Based data-driven Robust Model-Free Adaptive Predictive control of a Complicated Industrial Process

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第2期19卷 788-803页

作者： Zhou, Ping Zhang, Shuai Wen, Liang Fu, Jun Chai, Tianyou Wang, Hong Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Univ Manchester Control Syst Ctr Manchester M60 1QD Lancs England

The automatic control of blast furnace (BF) ironmaking process has always been an important yet arduous task in metallurgic engineering and automation. In this article, a novel Kalman filter-based robust model-free adaptive predictive control (MFAPC) method is proposed for the direct data-driven control of molten iron quality in BF ironmaking. First, a compact-form dynamic linearization-based extended MFAPC method for multivariable molten iron quality control is proposed by generalizing the existing single-variable MFAPC method to multivariable systems. Based on it, a Kalman filter-based robust MFAPC is further proposed considering the problems of data loss and measurement noise in quality detection. Specifically, the robust mechanism in the robust MFAPC combines a novel dynamic linearization method with a concept termed Pseudo-Jacobian matrix to predict the missing data during data loss. After that, a Kalman filter is constructed based on a prediction model to filter the measurement noise. The stability of the proposed control method is analyzed, and various data experiments using actual industrial data are performed to verify the effectiveness of the proposed methods.

关键词： Process control Iron Kalman filters Loss measurement Predictive models Noise measurement Adaptation models Blast furnace (BF) ironmaking process direct data-driven control multiparameter sensitivity analysis robust mechanism robust model-free adaptive predictive control (MFAPC)

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