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SOPA-GA-CNN: Synchronous optimisation of parameters and architectures by genetic algorithms with convolutional neural network blocks for securing Industrial Internet-of-Things

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IET Cyber-systems and Robotics 2023年第1期5卷 51-66页

作者： Jia-Cheng Huang Guo-Qiang Zeng Guang-Gang Geng Jian Weng Kang-Di Lu College of Cyber Security and the National Joint Engineering Research Center of Network Security Detection and Protection Technology Jinan UniversityGuangzhouChina National-Local Joint Engineering Research Center of Digitalize Electrical Design Technology Wenzhou UniversityWenzhouChina National Laboratory of Industrial Control Technology Institute of Cyber-Systems and ControlZhejiang UniversityHangzhouChina

In recent years,deep learning has been applied to a variety of scenarios in Industrial Internet of Things(IIoT),including enhancing the security of ***,the existing deep learning methods utilised in IIoT security are manually designed by heavily relying on the experience of the *** authors have made the first contribution concerning the joint optimisation of neural architecture search and hyper-parameters optimisation for securing IIoT.A novel automated deep learning method called synchronous optimisation of parameters and architectures by GA with CNN blocks(SOPA-GA-CNN)is proposed to synchronously optimise the hyperparameters and block-based architectures in convolutional neural networks(CNNs)by genetic algorithms(GA)for the intrusion detection issue of *** efficient hybrid encoding strategy and the corresponding GA-based evolutionary operations are designed to characterise and evolve both the hyperparameters,including batch size,learning rate,weight optimiser and weight regularisation,and the architectures,such as the block-based network topology and the parameters of each CNN *** experimental results on five intrusion detection datasets in IIoT,including secure water treatment,water distribution,Gas Pipeline,Botnet in Internet of Things and Power System Attack Dataset,have demonstrated the superiority of the proposed SOPA-GA-CNN to the state-of-the-art manually designed models and neuron-evolutionary methods in terms of accuracy,precision,recall,F1-score,and the number of parameters of the deep learning models.

关键词： cyber-systems deep learning deep neural network Internet of things swarm intelligence

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Indirect adaptive higher order sliding mode control for third order systems in parametric strict-feedback form 63

Indirect adaptive higher order sliding mode control for thir...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Watermann, Lars Andritsch, Benedikt Koch, Stefan Reichhartinger, Markus Horn, Martin Reger, Johann Technische Universität Ilmenau P.O. Box 100565 Control Engineering Group P.O.Box 100565 Ilmenau98684 Germany Institute of Automation and Control Graz University of Technology Graz Austria Institute of Automation and Control Graz University of Technology Christian Doppler Laboratory for Model-Based Control of Complex Test Bed Systems Graz Austria

ISBN: (纸本)9798350316339

In this contribution the design of an indirect adaptive third order sliding mode controller based on a backstepping-like procedure is presented. A recursively defined homogeneous control Lyapunov function is combined with the adaptive backstepping method via tuning functions to compensate for unmatched parametric uncertainties. A matched bounded disturbance is eliminated by discontinuous control. It is shown that the states of the regarded system in parametric strict-feedback form converge asymptotically to the equilibrium while the parameter estimation error is bounded. Further, it is guaranteed that all control and adaptation signals are bounded. The proposed design is demonstrated in a simulation of a system with unmatched parametric uncertainty and matched disturbance. © 2024 IEEE.

关键词： Sliding mode control

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An Event-Triggered Tracking control Scheme Based on Predictive control Method and its Application 42

An Event-Triggered Tracking Control Scheme Based on Predicti...

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42nd Chinese control Conference, CCC 2023

作者： Cao, Lei Yumuk, Erhan Ionescu, Clara-Mihaela Liu, Guo-Ping Harbin Institute of Technology Department of Control Science and Engineering Harbin150001 China Ghent University Dynamical Systems and Control Research Group Department of Electromechanical Systems and Metal Engineering Ghent9052 Belgium Center for Control Science and Technology Southern University of Science and Technology Shenzhen518055 China Istanbul Technical University Department of Control and Automation Engineering Istanbul34467 Turkey

ISBN: (纸本)9789887581543

In this paper, the tracking control problem for networked control system (NCS) under communication delays is investigated. In order to realize the tracking of the NCS, an event-triggered predictive control strategy is provided to compensate for the delays and achieve stability. Both tracking performance and input increments are considered in the proposed cost function. Based on the incremental inputs, an event-triggered mechanism is established to reduce the communication costs. The efforts of different parameters are illustrated through the results of the simulation, and the effectiveness of the proposed control scheme is also validated from the application of the networked lighting setup. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： networked control systems

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

arXiv

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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

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Baseline Results for Selected Nonlinear System Identification Benchmarks

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IFAC-PapersOnLine 2024年第15期58卷 474-479页

作者： Max Champneys Gerben I. Beintema R. Tóth Maarten Schoukens Timothy J. Rogers Dynamics Research Group University of Sheffield Sheffield UK Control System group Eindhoven University of Technology Eindhoven the Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Nonlinear system identification remains an important open challenge across research and academia. Large numbers of novel approaches are seen published each year, each presenting improvements or extensions to existing methods. It is natural, therefore, to consider how one might choose between these competing models. Benchmark datasets provide one clear way to approach this question. However, to make meaningful inference based on benchmark performance it is important to understand how well a new method performs comparatively to results available with well-established methods. This paper presents a set of ten baseline techniques and their relative performances on five popular benchmarks. The aim of this contribution is to stimulate thought and discussion regarding objective comparison of identification methodologies.

关键词： Nonlinear System Identification Benchmarks NARX Nonlinear State-Space

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Hybrid control of a Variable-Speed Peristaltic PUMP

Hybrid Control of a Variable-Speed Peristaltic PUMP

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2024 American control Conference, ACC 2024

作者： Beijen, Michiel Op Smeijers, Thijs Tijman Boerrigter, Gijs Van Den Eijnden, Sebastiaan University of Twente Group of Precision Engineering Department of Engineering Technology Enschede7500 AE Netherlands Demcon High-Tech Systems Enschede7521 PH Netherlands Eindhoven University of Technology Control Systems Technology Group Department of Mechanical Engineering Eindhoven5600 MB Netherlands

ISBN: (纸本)9798350382655

Peristaltic pumps are used for transporting liquids within disposable tubes, and are commonly found in medical devices. The peristaltic pump principle, however, introduces disturbances, thereby distorting the desired rotational speed of the pump. Proportional plus double integral (PI2) control is commonly used to reject these disturbances, but at the cost of deteriorated transient response and robustness margins. To balance these trade-offs in a more desirable manner, in this paper we propose a hybrid PI2 control strategy that allows for reducing the steady-state error while maintaining robustness margins similar to linear PI2 control. Experiments on a representative setup demonstrate up to 45% improvement in disturbance rejection capabilities. © 2024 AACC.

关键词： Disturbance rejection

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Learning-based model augmentation with LFRs

arXiv

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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

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Local geoid modeling in the central part of Java, Indonesia, using terrestrial-based gravity observations

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Geodesy and Geodynamics 2023年第3期14卷 231-243页

作者： Rahayu Lestari Brian Bramanto Kosasih Prijatna Arisauna M.Pahlevi Widy Putra Raa Ina Sidrotul Muntaha Febriananda Ladivanov Departement of Geodesy and Geomatics Engineering Faculty of Earth Sciences and TechnologyInstitut Teknologi BandungIndonesia Geodesy Research Group Faculty of Earth Sciences and TechnologyInstitut Teknologi BandungIndonesia Geodetic and Geodynamic Control Network Center Geospatial Information Agency of Indonesia(BIG)Indonesia

The Global Navigation Satellite System(GNSS)positioning method has been significantly developed in geodetic ***,the height obtained through GNSS observations is given in a geodetic height system that needs to be converted to orthometric height for engineering *** on geoid height,which can be calculated using the global geopotential mode,is required to convert such GNSS observations into orthometric ***,its accuracy is still insufficient for most engineering ***,a reliable geoid model is essential,especially in areas growing fast,e.g.,the central part of Java,*** this study,we modeled the local geoid model in the central part of Java,Indonesia,using terrestrial-based gravity *** Stokes'formula with the second Helmert's condensation method under the Remove-Compute-Restore approach was implemented to model the *** comparison between our best-performing geoid model and GNSS/leveling observations showed that the standard deviation of the geoid height differences was estimated to be 4.4 *** geoid result outperformed the commonly adopted global model of EGM2008 with the estimated standard deviation of geoid height differences of 10.7 cm.

关键词： Gravimetric geoid Stokes integration Second Helmert's condensation method GNSS/Leveling

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

arXiv

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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

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

arXiv

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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

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