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IEEE Conference on Decision and control

作者： Rogier Dinkla Tom Oomen Sebastiaan P. Mulders Jan-Willem van Wingerden Delft Center for Systems and Control Mekelweg 2 Delft University of Technology Delft The Netherlands Control Systems Technology Group Eindhoven University of Technology Eindhoven The Netherlands

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Many systems are subject to periodic disturbances and exhibit repetitive behaviour. Model-based repetitive control employs knowledge of such periodicity to attenuate periodic disturbances and has seen a wide range of successful industrial implementations. The aim of this paper is to develop a data-driven repetitive control method. In the developed framework, linear periodically time-varying (LPTV) behaviour is lifted to linear time-invariant (LTI) behaviour. Periodic disturbance mitigation is enabled by developing an extension of Willems’ fundamental lemma for systems with exogenous disturbances. The resulting Data-enabled Predictive Repetitive control (DeePRC) technique accounts for periodic system behaviour to perform attenuation of a periodic disturbance. Simulations demonstrate the ability of DeePRC to effectively mitigate periodic disturbances in the presence of noise.

关键词： Linear systems Prevention and mitigation Noise Attenuation

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Space-Filling Input Design for Nonlinear State-Space Identification

arXiv

引用

arXiv 2024年

作者： Kiss, Máté Tóth, Roland Schoukens, Maarten Control System group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

The quality of a model resulting from (black-box) system identification is highly dependent on the quality of the data that is used during the identification procedure. Designing experiments for linear time-invariant systems is well understood and mainly focuses on the power spectrum of the input signal. Performing experiment design for nonlinear system identification on the other hand remains an open challenge as informativity of the data depends both on the frequency-domain content and on the time-domain evolution of the input signal. Furthermore, as nonlinear system identification is much more sensitive to modeling and extrapolation errors, having experiments that explore the considered operation range of interest is of high importance. Hence, this paper focuses on designing space-filling experiments i.e., experiments that cover the full operation range of interest, for nonlinear dynamical systems that can be represented in a state-space form using a broad set of input signals. The presented experiment design approach can straightforwardly be extended to a wider range of system classes (e.g., NARMAX). The effectiveness of the proposed approach is illustrated on the experiment design for a nonlinear mass-spring-damper system, using a multisine input signal. © 2024, CC BY-NC-SA.

关键词： Frequency domain analysis

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A MATLAB toolbox for training and implementing physics–guided neural network–based feedforward controllers

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IFAC-PapersOnLine 2023年第2期56卷 4068-4073页

作者： M. Bolderman M. Lazar H. Butler Control Systems Group Eindhoven University of Technology The Netherlands ASML Veldhoven The Netherlands

Physics-guided neural networks (PGNNs) enable accurate identification of inverse system dynamics by effectively embedding a known physical model within a neural network (NN), and thereby achieve high performance when implemented as feedforward controllers. However, training PGNNs using existing NN toolboxes is complicated. Therefore, this paper presents a MATLAB toolbox that systematically implements, trains, and validates PGNNs. Dedicated functions implement recent results that have been proposed in literature, i.e., we ensure that the PGNN converges to a value of the cost function that is strictly upperbounded by the value obtained when using only the physical model, while also imposing a form of graceful degradation when the trained PGNN is used on data that was not present in the training data. The toolbox is available at: https://***/mbolderman/PGNN-Toolbox/ .

关键词： Mechatronics motion control systems data-based control software for system identification identification for control

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MMH-FE:AMulti-Precision and Multi-Sourced Heterogeneous Privacy-Preserving Neural network Training Based on Functional Encryption

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Computers, Materials & Continua 2025年第3期82卷 5387-5405页

作者： Hao Li Kuan Shao Xin Wang Mufeng Wang Zhenyong Zhang The State Key Laboratory of Public Big Data College of Computer Science and TechnologyGuizhou UniversityGuiyang550025China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science CenterQilu University of Technology(Shandong Academy of Sciences)Jinan250014China China Industrial Control Systems Cyber Emergency Response Team Beijing100040China

Due to the development of cloud computing and machine learning,users can upload their data to the cloud for machine learning model ***,dishonest clouds may infer user data,resulting in user data *** schemes have achieved secure outsourced computing,but they suffer from low computational accuracy,difficult-to-handle heterogeneous distribution of data from multiple sources,and high computational cost,which result in extremely poor user experience and expensive cloud computing *** address the above problems,we propose amulti-precision,multi-sourced,andmulti-key outsourcing neural network training ***,we design a multi-precision functional encryption computation based on Euclidean ***,we design the outsourcing model training algorithm based on a multi-precision functional encryption with multi-sourced ***,we conduct experiments on three *** results indicate that our framework achieves an accuracy improvement of 6%to 30%.Additionally,it offers a memory space optimization of 1.0×2^(24) times compared to the previous best approach.

关键词： Functional encryption multi-sourced heterogeneous data privacy preservation neural networks

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Integrating data-informativity conditions in predictor models for single module identification in dynamic networks

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IFAC-PapersOnLine 2023年第2期56卷 2377-2382页

作者： Paul M.J. Van den Hof Karthik R. Ramaswamy Stefanie J.M. Fonken Control Systems Group Department of Electrical Engineering Eindhoven University of Technology The Netherlands

For consistent identification of a target module in a dynamic network with the local direct method, basically two prime conditions need to be satisfied: (a) a set of structural conditions on the choice of the predictor model, i.e. a set of input and output node variables, and (b) conditions on data-informativity. While for conditions (a) constructive algorithms for node selection have been presented that appropriately guarantee that the identified object can indeed reveal the target module, the requirements for satisfying (b) have not yet been integrated fully. In this paper, we will present simplified path-based results for generic data-informativity, and show how they can be integrated in constructive algorithms for predictor model selection that provide consistent target module estimates. It is shown that data-informativity not only requires a sufficient number of external excitation signals to be present in the network, but also puts restrictions on the structure of the predictor model, i.e. the selection of input and output node variables. Some examples are presented to illustrate the new results.

关键词： System identification identifiability dynamic networks interconnected systems

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On-the-fly Surrogation for Complex Nonlinear Dynamics

arXiv

引用

arXiv 2025年

作者： Olucha, E. Javier Singh, Rajiv Das, Amritam Tóth, Roland Control Systems Group Eindhoven University of Technology Netherlands The MathWorks Inc. Natick United States Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Hungary

High-fidelity models are essential for accurately capturing nonlinear system dynamics. However, simulation of these models is often computationally too expensive and, due to their complexity, they are not directly suitable for analysis, control design or real-time applications. Surrogate modelling techniques seek to construct simplified representations of these systems with minimal complexity, but adequate information on the dynamics given a simulation, analysis or synthesis objective at hand. Despite the widespread availability of system linearizations and the growing computational potential of autograd methods, there is no established approach that systematically exploits them to capture the underlying global nonlinear dynamics. This work proposes a novel surrogate modelling approach that can efficiently build a global representation of the dynamics on-the-fly from local system linearizations without ever explicitly computing a model. Using radial basis function interpolation and the second fundamental theorem of calculus, the surrogate model is only computed at its evaluation, enabling rapid computation for simulation and analysis and seamless incorporation of new linearization data. The efficiency and modelling capabilities of the method are demonstrated on simulation examples. © 2025, CC BY.

关键词： Differentiation (calculus)

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Scaled Relative Graph Analysis of Lur’e systems and the Generalized Circle Criterion

arXiv

引用

arXiv 2024年

作者： Krebbekx, Julius P.J. Tóth, Roland Das, Amritam Control Systems group Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Lab HUN-REN Institute for Computer Science and Control Budapest Hungary

Scaled Relative Graphs (SRGs) provide a novel graphical frequency-domain method for the analysis of nonlinear systems. However, we show that the current SRG analysis suffers from a pitfall that limit its applicability in analyzing practical nonlinear systems. We overcome this pitfall by modifying the SRG of a linear time invariant operator, combining the SRG with the Nyquist criterion, and apply our result to Lur’e systems. We thereby obtain a generalization of the celebrated circle criterion, which deals with a broader class of nonlinearities, and provides (incremental) L2-gain performance bounds. © 2024, CC BY.

关键词： Frequency domain analysis

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Flexible Process control for Scalable Electrolysis systems

Flexible Process Control for Scalable Electrolysis Systems

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2023 IEEE International Conference on Electrical, Computer and Energy Technologies, ICECET 2023

作者： Lorenz, Julius Klose, Anselm Lange, Hannes Kock, Tobias Urbas, Leon Process Control Systems Tud Dresden University of Technology Dresden Germany Process to Order Lab Tud Dresden University of Technology Dresden Germany Process Systems Engineering Group Tud Dresden University of Technology Dresden Germany

ISBN: (纸本)9798350327816

Large-scale water electrolysis systems are built from a multitude of stack units. These stack units have to be controlled during operation. The question arises how process control of the system can be designed in that way, that scalable, extendable but also optimal operation of the stack units becomes possible. To achieve this goal, existing process control strategies from literature are analyzed. The requirements resulting from the inherent numbering up of stack units are described and an approach for an adaptable optimized process control strategy considering the system properties is proposed. The proposed process control strategy is implemented in a modeled electrolysis system and evaluated regarding the stated criteria. The results could show, that the inherent modularization of electrolysis systems can be used in advantage for a scalable operation. © 2023 IEEE.

关键词： Modular Automation Process control Water Electrolysis

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Process control Principles for Scalable Electrolysis systems 28

Process Control Principles for Scalable Electrolysis Systems

引用

28th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2023

作者： Lorenz, Julius Klose, Anselm Lange, Hannes Kock, Tobias Urbas, Leon TUD Dresden University of Technology Process Control Systems Dresden Germany TUD Dresden University of Technology Process to Order Lab Dresden Germany TUD Dresden University of Technology Process Systems Engineering Group Dresden Germany

ISBN: (纸本)9798350339918

The scale up of hydrogen production to large scale production systems demands a structured approach to control these systems. With suitable control principles, optimization for operation can be achieved whilst maintaining flexibility. In this paper, existing control strategies are analyzed and compared for large scale electrolysis systems. It is argued, how modularization can enable the optimal distribution of set-points and enables the systems for capacity extension. An initial implementation in MATLAB is presented, showing the feasibility of the proposed power distribution concept. © 2023 IEEE.

关键词： Process control

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Predictable Interval MDPs through Entropy Regularization

Predictable Interval MDPs through Entropy Regularization

引用

IEEE Conference on Decision and control

作者： M.J.T.C. van Zutphen G. Delimpaltadakis W.P.M.H. Heemels D.J. Antunes Department of Mechanical Engineering Control Systems Technology Group Eindhoven University of Technology The Netherlands

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Regularization of control policies using entropy can be instrumental in adjusting predictability levels of real-world systems. Applications benefiting from such approaches range from cybersecurity, which aims at maximal unpredictability, to human-robot interaction, where predictable behavior is highly desirable. In this paper, we consider entropy regularization for interval Markov decision processes (IMDPs), which are uncertain MDPs where transition probabilities are only known to belong to specified intervals. Lately, IMDPs have gained significant popularity in the context of abstracting stochastic systems for control design. In this work, we address robust minimization of the linear combination of entropy and a standard cumulative cost in IMDPs, thereby establishing a trade-off between optimality and predictability. We show that optimal deterministic policies exist, and devise a value-iteration algorithm to compute them. The algorithm solves a number of convex programs at each step. Finally, through an illustrative example we show the benefits of penalizing entropy in IMDPs.

关键词： Costs control design Stochastic systems Markov decision processes Instruments Human-robot interaction Prediction algorithms Minimization Entropy Standards

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