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IEEE Conference on Decision and control

作者： William P. Heath Joaquin Carrasco School of Computer Science and Engineering Bangor University Dean St LL57 1UT UK Department of Electrical and Electronic Engineering Control Systems and Robotics Group Nancy Rothwell Building University of Manchester M13 9PL UK

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no guarantee the closedloop system has finite incremental gain. It has been suggested in the literature that without this guarantee such a system may be critically sensitive to time-varying exogenous signals including noise. We show that multipliers guarantee the power gain of the system to be bounded and quantifiable. Furthermore power may be measured about an appropriate steady state bias term, provided the multiplier does not require the nonlinearity to be odd. Hence dynamic multipliers can be used to guarantee Lurye systems have low sensitivity to noise, provided other exogenous systems have constant steady state. We illustrate the analysis with an example where the exogenous signal is a power signal with non-zero mean.

关键词： Sensitivity Power measurement Noise Stability analysis Steady-state Time-varying systems Lyapunov methods

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Learning For Predictive control: A Dual Gaussian Process Approach

arXiv

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arXiv 2022年

作者： Liu, Yuhan Wang, Pengyu Tóth, Roland Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Flight Dynamics and Control Laboratory Department of Aerospace Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation. Effectiveness of the proposed strategy is demonstrated via numerical simulations. © 2022, CC BY-NC-SA.

关键词： Model predictive control

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ERROR ANALYSIS OF KERNEL EDMD FOR PREDICTION AND control IN THE KOOPMAN FRAMEWORK

arXiv

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arXiv 2023年

作者： Philipp, Friedrich Schaller, Manuel Worthmann, Karl Peitz, Sebastian Nüske, Feliks Technische Universität Ilmenau Optimization-Based Control Group Institute for Mathematics Ilmenau Germany Optimization-Based Control Group Institute of Mathematics Technische Universität Ilmenau Ilmenau Germany Paderborn University Department of Computer Science Data Science for Engineering Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Department of Mathematics and Computer Science Freie Universität Berlin Germany

Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to approximate the Koopman operator for deterministic and stochastic (control) systems. This operator is linear and encompasses full information on the (expected stochastic) dynamics. In this paper, we analyze a kernel-based EDMD algorithm, known as kEDMD, where the dictionary consists of the canonical kernel features at the data points. The latter are acquired by i.i.d. samples from a user-defined and application-driven distribution on a compact set. We prove bounds on the prediction error of the kEDMD estimator when sampling from this (not necessarily ergodic) distribution. The error analysis is further extended to control-affine systems, where the considered invariance of the Reproducing Kernel Hilbert Space is significantly less restrictive in comparison to invariance assumptions on an a-priori chosen dictionary. © 2023, CC BY.

关键词： Dynamic mode decomposition

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MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Navigation and Inspection

MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Na...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Olaya Álvarez-Tuñón Hemanth Kanner Luiza Ribeiro Marnet Huy Xuan Pham Jonas le Fevre Sejersen Yury Brodskiy Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiRLab) Aarhus University Aarhus C Denmark EIVA a/s Skanderborg Denmark Automatic Control Group (RAT) Paderborn University Paderborn Germany

This paper presents MIMIR-UW, a multipurpose underwater synthetic dataset for SLAM, depth estimation, and object segmentation to bridge the gap between theory and application in underwater environments. MIMIR-UW integrates three camera sensors, inertial measurements, and ground truth for robot pose, image depth, and object segmentation. The underwater robot is deployed within a pipe exploration scenario, carrying artificial lights that create uneven lighting, in addition to natural artefacts such as reflections from natural light and backscattering effects. Four environments totalling eleven tracks are provided, with various difficulties regarding light conditions or dynamic elements. Two metrics for dataset evaluation are proposed, allowing MIMIR-UW to be compared with other datasets. State-of-art methods on SLAM, segmentation and depth estimation are deployed and benchmarked on MIMIR-UW. Moreover, the dataset's potential for sim-to-real transfer is demonstrated by leveraging the segmentation and depth estimation models trained on MIMIR-UW in a real pipeline inspection scenario. To the best of the authors' knowledge, this is the first underwater dataset targeted for such a variety of methods. The dataset is publicly available online. https://***/remaro-network/MIMIR-UW/

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State-feedback memory control for uncertain cyber-physical systems under Denial-of-Service attacks*

引用

IFAC-PapersOnLine 2023年第2期56卷 544-549页

作者： Pedro M. Oliveira Ricardo C.L.F. Oliveira Jonathan M. Palma Márcio J. Lacerda Control and Modelling Group (GCOM) Department of Electrical Engineering Federal University of São João del-Rei - UFSJ São João del-Rei MG Brazil School of Electrical and Computer Engineering University of Campinas - UNICAMP Campinas São Paulo Brazil Faculty of Engineering Universidad de Talca Curicó Chile

This paper tackles the problem of state-feedback control for uncertain cyber-physical systems with the presence of Denial-of-Service attacks. The closed-loop stability is guaranteed through a new state-feedback packet-based strategy that utilizes past states (memory) to artificially enrich the control dynamics. The presence of an energy-bounded attacker is assumed and the closed-loop system under attack is modeled as an uncertain switched system. A less conservative stability analysis condition based on the Lyapunov theory is used as starting point for the synthesis conditions, which are formulated in terms of Linear Matrix Inequalities. Numerical experiments are conducted to illustrate the technique's effectiveness and perform comparisons with packet-based methods from the literature.

关键词： Robust control Cyber-physical systems DoS attacks Linear Matrix Inequalities control with memory

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End-to-end imaging system optimization for computer vision in driving automation

End-to-end imaging system optimization for computer vision i...

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2021 3D Imaging and Applications, 3DIA 2021

作者： Weikl, Korbinian Schroeder, Damien Blau, Daniel Liu, Zhenyi Stechele, Walter Department of Electrical and Computer Engineering Technical University of Munich Munich Germany Automated Driving BMW Group Munich Germany State Key Laboratory of Automotive Simulation and Control Jilin University Changchun China

Full driving automation imposes to date unmet performance requirements on camera and computer vision systems, in order to replace the visual system of a human driver in any conditions. So far, the individual components of an automotive camera have mostly been optimized independently, or without taking into account the effect on the computer vision applications. We propose an end-to-end optimization of the imaging system in software, from generation of radiometric input data over physically based camera component models to the output of a computer vision system. Specifically, we present an optimization framework which extends the ISETCam and ISET3d toolboxes to create synthetic spectral data of high dynamic range, and which models a state-of-the-art automotive camera in more detail. It includes a state-of-the-art object detection system as benchmark application. We highlight in which way the framework approximates the physical image formation process. As a result, we provide guidelines for optimization experiments involving modification of the model parameters, and show how these apply to a first experiment on high dynamic range imaging. © This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://***/licenses/by/4.0/.

关键词： Cameras

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Optimal Energy Storage System Placement for Robust Stabilization of Power Systems Against Dynamic Load Altering Attacks

Optimal Energy Storage System Placement for Robust Stabiliza...

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Mediterranean Conference on control and Automation (MED)

作者： Roberto Germanà Alessandro Giuseppi Antonio Pietrabissa Alessandro Di Giorgio Department of Computer Control and Management Engineering Antonio Ruberti at Sapienza University of Rome Rome Italy Cyber Security Research Group of CRAT Rome Italy

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

This paper presents a study on the "Dynamic Load Altering Attacks" (D-LAAs), their effects on the dynamics of a transmission network, and provides a robust control protection scheme, based on polytopic uncertainties, invariance theory, Lyapunov arguments and graph theory. The proposed algorithm returns an optimal Energy Storage Systems (ESSs) placement, that minimizes the number of ESSs placed in the network, together with the associated control law that can robustly stabilize against D-LAAs. The paper provides a contextualization of the problem and a modelling approach for power networks subject to D-LAAs, suitable for the designed robust control protection scheme. The paper also proposes a reference scenario for the study of the dynamics of the control actions and their effects in different cases. The approach is evaluated by numerical simulations on large networks.

关键词： Robust control Uncertainty Heuristic algorithms Power system dynamics Power system stability Numerical simulation Numerical models

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Performance comparison of discrete-time robust adaptive controllers for grid-tied power converters under unbalanced grids

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2023年 4卷

作者： de Oliveira Evald, Paulo Jefferson Dias Hollweg, Guilherme Vieira da Silveira, Wagner Barreto Milbradt, Deise Maria Cirollini Tambara, Rodrigo Varella Gründling, Hilton Abílio Intelligent Systems and Control Group Federal University of Pelotas Pelotas - RS Brazil Department of Electrical and Computer Engineering University of Michigan Deaborn MI United States Power Electronics and Control Group Federal University of Santa Maria Brazil - RS Santa Maria - RS Brazil

Renewable energy generation systems largely use grid-tied power converters with LCL filters. A well-known issue in this kind of system is the resonance of the LCL filter, whose frequency is straightforwardly affected by grid condition. Variations on grid impedance tends to make the controller performance poor and until turn the closed-loop unstable. Therefore, some controllers are designed for specific operation ranges to ensure performance and global stability. However, a deep study of Model Reference Adaptive-based controllers for this kind of issue was not conducted yet. The aim of this paper is to fit this research gap. Thus, this paper presents a performance comparison of feasible discrete-time robust adaptive controllers, based on reduced order reference model, for grid-tied power converters with LCL filter under unbalanced grid conditions, providing an assessment of the benefits and drawbacks of each considered control strategy. The evaluated control laws are: Robust Model Reference Adaptive controller, Robust Model Reference-based Adaptive Super-Twisting Sliding Mode controller, Robust Adaptive One Sample Ahead Preview controller, and Robust Adaptive Proportional-Integral controller. Through performance comparison, it can be defined what controller is more adequate in face of grid condition, taking into consideration grid-injected currents quality and design complexity of controller. Experimental results of grid-injected current control of a grid-tied 5.8 kW Voltage-Source Inverter with LCL filter considering relevant grid voltage unbalance are presented to corroborate the controllers’ performance and discuss their reference tracking response, robustness to the parametric variation, exogenous disturbance rejection, parameters adaptability, and global stability. In evaluated scenarios, Robust Adaptive Proportional-Integral controller presented slower regulation dynamics between compared adaptive structures, but reduced tracking error, as well as lower total h

关键词： Adaptive PI controller Grid-tied converters LCL Filter One sample ahead preview controller Robust model reference adaptive control Super-Twisting sliding mode

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Risk-Constrained Reinforcement Learning for Inverter-Dominated Power System controls

arXiv

引用

arXiv 2023年

作者： Kwon, Kyung-Bin Mukherjee, Sayak Vu, Thanh Long Zhu, Hao The Chandra Department of Electrical and Computer Engineering The University of Texas at Austin 2501 Speedway AustinTX78712 United States The Optimization and Control Group Pacific Northwest National Laboratory RichlandWA99352 United States

This paper develops a risk-aware controller for grid-forming inverters (GFMs) to minimize large frequency oscillations in GFM inverter-dominated power systems. To tackle the high variability from loads/renewables, we incorporate a mean-variance risk constraint into the classical linear quadratic regulator (LQR) formulation for this problem. The risk constraint aims to bound the time-averaged cost of state variability and thus can improve the worst-case performance for large disturbances. The resulting risk-constrained LQR problem is solved through the dual reformulation to a minimax problem, by using a reinforcement learning (RL) method termed as stochastic gradient-descent with max-oracle (SGDmax). In particular, the zero-order policy gradient (ZOPG) approach is used to simplify the gradient estimation using simulated system trajectories. Numerical tests conducted on the IEEE 68-bus system have validated the convergence of our proposed SGDmax for GFM model and corroborate the effectiveness of the risk constraint in improving the worst-case performance while reducing the variability of the overall control cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Stochastic systems

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Deep Learning Algorithms for Detecting Freezing of Gait in Parkinson's Disease: A Cross-Dataset Study

SSRN

引用

SSRN 2024年

作者： Sigcha, Luis Borzì, Luigi Olmo, Gabriella University of Limerick LimerickV94 T9PX Ireland Group Department of Electronic and Computer Engineering University of Limerick LimerickV94 T9PX Ireland ANTHEA Lab Data Analytics and Technologies for Health Lab Department of Control and Computer Engineering Politecnico di Torino Turin10129 Italy PolitoBIOMed Lab Biomedical Engineering Lab Politecnico di Torino Turin10129 Italy

Freezing of gait (FoG) is a complex and disabling symptom of Parkinson’s disease (PD), which has a significant impact on the quality of life of people with PD (PwPD) and increases the risk of falls and related injuries. This study aims to evaluate the generalization capability of deep learning (DL) algorithms in FoG detection. To address this task, various machine learning (ML) and deep learning (DL) algorithms were implemented, fine-tuned, and evaluated using diverse data splitting and validation strategies. The experiments performed yielded mixed results. Although the implementations demonstrated competitive performance in single-dataset settings, all approaches showed limited robustness in cross-dataset tests and suboptimal generalization across different datasets. These results highlight the importance of standardized data collection procedures to ensure uniformity. The specification of sensor settings and predefined sensor locations can foster homogeneity in datasets, even when dealing with diverse subjects and environments. Such standardization efforts are crucial for advancing generalized methodologies in the detection of FoG applicable to both research and clinical applications. © 2024, The Authors. All rights reserved.

关键词： Wearable sensors

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