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On the reduction of Linear Parameter-Varying State-Space models

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arXiv 2024年

作者： Olucha, E. Javier Terzin, Bogoljub Das, Amritam Tóth, Roland Control Systems Group Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

This paper presents an overview and comparative study of the state of the art in state-order reduction (SOR) and scheduling dimension reduction (SDR) for linear parameter-varying (LPV) state-space (SS) models, comparing and benchmarking their capabilities, limitations and performance. The use case chosen for these studies is an interconnected network of nonlinear coupled mass spring damper systems with three different configurations, where some spring coefficients are described by arbitrary user-defined static nonlinear functions. For SOR, the following methods are compared: linear time-invariant (LTI), LPV and LFR-based balanced reductions, moment matching (MM) and parameter-varying oblique projection. For SDR, the following methods are compared: principal component analysis (PCA), trajectory PCA, Kernel PCA and LTI balanced truncation, autoencoders and deep neural network. The comparison reveals the most suitable reduction methods for the different benchmark configurations, from which we provide use case SOR and SDR guidelines that can be used to choose the best reduction method for a given LPV-SS model. © 2024, CC BY.

关键词： Principal component analysis

Learning-based model augmentation with LFRs

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arXiv 2024年

作者： Hoekstra, Jan H. Verhoek, Chris Tóth, Roland Schoukens, Maarten Control Systems Group Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Nonlinear system identification (NL-SI) has proven to be effective in obtaining accurate models for highly complex systems. Especially, recent encoder-based methods for artificial neural networks state-space (ANN-SS) models have achieved state-of-the-art performance on various benchmarks, while offering consistency and computational efficiency. The inclusion of prior knowledge of the system can be exploited to increase (i) estimation speed, (ii) accuracy, and (iii) interpretability of the resulting models. This paper proposes an encoder based model augmentation method incorporating prior knowledge from first-principles (FP) models. We introduce a novel linear-fractional-representation (LFR) model structure that allows for the unified representation of various augmentation structures including the ones that are commonly used in the literature, and an identification algorithm for estimating the proposed structure together with appropriate initialization methods. The performance and generalization capabilities of the proposed method are demonstrated on a hardening mass-spring-damper simulation. © 2024, CC BY-NC-SA.

关键词： Nonlinear systems

Management of Scientific Agricultural Digital Ecosystems Based on Ecosystem Classification 16

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Management of Scientific Agricultural Digital Ecosystems Bas...

16th International Conference Management of Large-Scale System Development, MLSD 2023

作者： Kulba, Vladimir Medennikov, Viktor V.A. Trapeznikov Institute of Control Sciences of the Russian Academy of Sciences Laboratory of Modular Data Processing and Control Systems Moscow Russia Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Information and Computing Systems Department Moscow Russia

ISBN: (纸本)9798350337907

The paper considers a mathematical model for managing a scientific digital ecosystem exemplified by the agriculture case;the model enables assessing the impact science exerts on the regions' socioeconomic position in order to build an adequate response to the advanced economies' regime of severe sanctions against Russia. For these purposes, a systemic analysis of the subject identification and classification of ecosystems was conducted to address the critical issue of determining the basic concepts and terms of the country's digitalization. © 2023 IEEE.

关键词： Agriculture

Calibration of Front Wheel Odometry Model 27th

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Calibration of Front Wheel Odometry Model

27th Symposium of the International Association of Vehicle System Dynamics, IAVSD 2021

作者： Fazekas, Máté Gáspár, Péter Németh, Balázs Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 Budapest1111 Hungary

ISBN: (纸本)9783031073045

Accurate and cost-effective state estimation is needed to reach self-driving. The well-known GNSS and IMU fusion can be improved by the integration of wheel odometry. The robustness of this type of odometry is increased if both the rear and front wheels are utilized. Furthermore, the method is cost-effective, but for accurate motion estimation, the vehicle model parameters have to be calibrated. This paper presents the whole calibration task, such as input estimation, filtering of reference outputs, and parameter identification. The proposed estimation method is a unique version of the Gauss-Newton method, to mitigate the distortion effect of pose initialization. The effectiveness of the proposed calibration process is illustrated through vehicle test experiments. The validation demonstrates that the calibration results in below than 1% relative estimation error, thus the front-odometry can be integrated into the state estimation layer of a self-driving vehicle. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Motion estimation

On the relationship between the activity at point of interests and road traffic

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Communications in Transportation Research 2023年第1期3卷 150-160页

作者： MátéKolat Tamás Tettamanti TamásBécsi Domokos Esztergár-Kiss Department of Control for Transportation and Vehicle Systems Faculty of Transportation Eng.and Vehicle Eng.Budapest University of Technology and EconomicsBudapest1111PestHungary Systems and Control Laboratory Institute for Computer Science and Control of the Eötvös Lorand Research NetworkBudapest1053PestHungary Department of Transport Technology and Economics Faculty of Transportation Eng.and Vehicle Eng.Budapest University of Technology and EconomicsBudapest1111PestHungary

The estimation and analysis of road traffic represent the preliminary steps towards satisfying the current needs for smooth,safe,and green ***,effective traffic monitoring is an essential topic alongside the planning of sustainable transportation systems and the development of new traffic management *** contrast to classical traffic detection solutions,this study investigates the correlation between travelers'social activities and road *** s's primary goal is to investigate the presence of the relationship between social activity and road traffic,which might allow an infrastructure-independent traffic monitoring technique as ***'s general activities at Point of Interest(POI)locations(measured as occupancy parameter)are correlated with traffic data so that,finally,proper proxys can be defined for link-level average traffic speed *** method is tested and evaluated using real-world traffic and POI occupancy data from Budapest(District XI.).The results of the correlation investigation justify an indirect relationship between activity at POIs and road traffic,which holds promise for future practical applicability.

关键词： Road traffic estimation Google popular times(GPT) Point of interest(POI) POI occupancy Genetic algorithm(GA)

A Data-Driven Adaptive control Approach for Enhancing the Dynamic Response of VSGs in Varying Grid Conditions

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IEEE Transactions on Power Delivery 2025年第3期40卷 1421-1433页

作者： Fahad, Shah She, Buxin Yin, Junjie Li, Fangxing Cui, Hantao Bo, Rui Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Senior Power Systems Engineer ETAP Irvine CA USA Optimization & Control Group Parcific Northwest National Laboratory Richland USA Department of Electrical Engineering and Computer Science University of Tennessee at Knoxville Knoxville TN USA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Conventionally, a virtual synchronous generator (VSG) is designed for islanded mode (IM) operation to meet specific operational requirements such as the rate of change of frequency (RoCoF). However, the operation of VSG designed for IM may not meet the operational and control criteria in grid connected mode (GCM) when the grid conditions vary. In addition, conventional VSG control technology does not consider the influence of the presynchronization scheme when connected to a weak grid, which degrades the RoCoF in IM. To overcome the aforementioned challenges, the proposed study presents a twin-delayed deep deterministic policy gradient (TD3) algorithm to improve the seamless transition performance of VSG from IM to GCM and vice versa. In the first step, the VSG-based power system model is used as a foundation for problem formulation of the proposed TD3 algorithm. Secondly, a reward function is designed according to the performance requirements, i.e., frequency and RoCoF requirements, of the VSG in order to guide the training of the agent in varying load, power reference, and grid conditions. Finally, the superiority of the proposed algorithm over existing methods is validated in MATLAB/SIMULINK and RTDS based real-time simulation environment. © 1986-2012 IEEE.

关键词： Deep reinforcement learning

Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks

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arXiv 2024年

作者： Liu, Yuhan Tóth, Roland Schoukens, Maarten Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Physics-guided neural networks (PGNN) is an effective tool that combines the benefits of data-driven modeling with the interpretability and generalization of underlying physical information. However, for a classical PGNN, the penalization of the physics-guided part is at the output level, which leads to a conservative result as systems with highly similar state-transition functions, i.e. only slight differences in parameters, can have significantly different time-series outputs. Furthermore, the classical PGNN cost function regularizes the model estimate over the entire state space with a constant trade-off hyperparameter. In this paper, we introduce a novel model augmentation strategy for nonlinear state-space model identification based on PGNN, using a weighted function regularization (W-PGNN). The proposed approach can efficiently augment the prior physics-based state-space models based on measurement data. A new weighted regularization term is added to the cost function to penalize the difference between the state and output function of the baseline physics-based and final identified model. This ensures the estimated model follows the baseline physics model functions in regions where the data has low information content, while placing greater trust in the data when a high informativity is present. The effectiveness of the proposed strategy over the current PGNN method is demonstrated on a benchmark example. © 2024, CC BY-NC-SA.

关键词： Cost functions

Automated Linear Parameter-Varying Modeling of Nonlinear systems: A Global Embedding Approach

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arXiv 2025年

作者： Olucha, E. Javier Koelewijn, Patrick J.W. Das, Amritam Tóth, Roland Control Systems Group Eindhoven University of Technology Eindhoven Netherlands Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

In this paper, an automated linear parameter-varying (LPV) model conversion approach is proposed for nonlinear dynamical systems. The proposed method achieves global embedding of the original nonlinear behavior of the system by leveraging the second fundamental theorem of calculus to factorize matrix function expressions without any approximation. The implementation of the proposed method in the LPVcore toolbox for Matlab is discussed, and its performance is showcased on a comprehensive example of automated LPV model conversion of an unbalanced disk system, which is then used to design an LPV controller that is deployed on the original nonlinear system. In addition, the conversion capabilities are further demonstrated by obtaining an LPV embedding of a three-degree-of-freedom control moment gyroscope. All software implementations are available at ***. © 2025, CC BY.

关键词： MATLAB

Binary Stochastic Flip Optimization for Training Binary Neural Networks

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Binary Stochastic Flip Optimization for Training Binary Neur...

2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Shibuya, Tatsukichi Inoue, Nakamasa Kawakami, Rei Sato, Ikuro Department of Computer Science Institute of Science Tokyo Tokyo Japan Department of Systems and Control Engineering Institute of Science Tokyo Tokyo Japan Denso IT Laboratory Tokyo Japan

ISBN: (纸本)9798350368741

For deploying deep neural networks on edge devices with limited resources, binary neural networks (BNNs) have attracted significant attention, due to their computational and memory efficiency. However, once a neural network is binarized, finetuning it on edge devices becomes challenging because most conventional training algorithms for BNNs are designed for use on centralized servers and require storing real-valued parameters during training. To address this limitation, this paper introduces binary stochastic flip optimization (BinSFO), a novel training algorithm for BNNs. BinSFO employs a parameter update rule based on Boolean operations, eliminating the need to store real-valued parameters and thereby reducing memory requirements and computational overhead. In experiments, we demonstrated the effectiveness and memory efficiency of BinSFO in finetuning scenarios on six image classification datasets. BinSFO performed comparably to conventional training algorithms with a 70.7% smaller memory requirement. Code is released at https://***/TatsukichiShibuya/ICASSP2025_BinSFO. © 2025 IEEE.

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