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An Observer-Based Composite Identifier for Online Estimation of the Thévenin Equivalent Parameters of a Power System

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arXiv 2022年

作者： Zonetti, Daniele Ortega, Romeo Cisneros, Rafael Bobtsov, Alexey Mancilla-David, Fernando Gomis-Bellmunt, Oriol The Centre d'Innovació Tecnològica En Convertidors Estàtics I Accionaments Departament d'Enginyeria Elèctrica Universitat Politècnica De Catalunya Barcelona08028 Spain The Departamento Académico De Sistemas Digitales ITAM Rio Hondo 1 Col. Progreso Tizapan Ciudad de México01080 Mexico The Department Of Control Systems And Robotics ITMO University Kronverkskiy av. 49 Saint-Petersburg197101 Russia The Department Of Electrical Engineering University Of Colorado Denver DenverCO80204 United States

We consider a Thévenin equivalent circuit capturing the dynamics of a power grid as seen from the point of common coupling with a power electronic converter, and provide a solution to the problem of online identification of the corresponding circuit parameters. For this purpose, we first derive a linear regression model in the conventional abc coordinates and next design a bounded observer-based composite identifier that requires local measurements and knowledge of the grid frequency only. An extension that guarantees exponential convergence of the estimates, under the additional assumption of knowledge of the grid X/R ratio, is further provided. The performance of the proposed identifier, which subsumes a conventional gradient descent algorithm, is illustrated via detailed computer simulations. © 2022, CC BY.

关键词： Equivalent circuits

P-WAE: Generalized patch-wasserstein autoencoder for anomaly screening

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arXiv 2021年

作者： Zhang, Hui Chen, Yurong Wang, Yaonan Cao, Yihong Wu, Q.M. Jonathan Yang, Yimin National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Changsha Hunan410082 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B3P4 Canada Department of Computer Science Lakehead University OntarioP7B 5E1 Canada Vector Institute TorontoM5G 1M1 Canada

To mitigate the inspector’s workload and improve the quality of the product, computer vision-based anomaly detection (AD) techniques are gradually deployed in real-world industrial scenarios. Recent anomaly analysis benchmarks progress to generative models. The aim is to model the defect-free distribution so that anomalies can be classified as out-of-distribution samples. Nevertheless, there are two disturbing factors that need researchers and deployers to prioritize: (i) the simplistic prior latent distribution inducing limited expressive capability;(ii) the collapsed mutual-dependent features resulting in poor generalization. In this paper, we propose a novel Patch-wise Wasserstein AutoEncoder (P-WAE) architecture to alleviate those challenges. In particular, a patch-wise variational inference model coupled with solving the jigsaw puzzle is designed, which is a simple yet effective way to increase the expressiveness and complexity of the latent manifold. This alleviates the blurry reconstruction problem. In addition, the Hilbert-Schmidt Independence Criterion (HSIC) bottleneck is introduced to constrain the over-regularization representation. Comprehensive experiments, conducted on the MVTec AD dataset, demonstrate the superior performance of our proposed method. © 2021, CC BY.

关键词： Anomaly detection

Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection

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arXiv 2021年

作者： Chen, Yurong Zhang, Hui Wang, Yaonan Wu, Q. M. Jonathan Yang, Yimin National Engineering Laboratory of Robot Visual Perception and Control Technology School of Robotics Hunan University Hunan Changsha410082 China Department of Electrical and Computer Engineering University of Windsor WindsorONN9B3P4 Canada Department of Computer Science Lakehead University ONP7B 5E1 Canada Vector Institute TorontoONM5G 1M1 Canada

Anomaly detection refers to identifying the observation that deviates from the normal pattern, which has been an active research area in various domains. Recently, the increasing data scale, complexity, and dimension turns the traditional representation and statistical-based outlier detection method into challenging. In this paper, we leverage the generative model in hyperspectral images anomaly detection. The gist is to model the distribution of the normal data, while the out-of-distribution sample can be viewed as the outlier. At first, the variational inference-based anomaly detection methods are investigated. We theoretically and empirically find that they are unstable due to the strong notion of distance (f-divergence) served as the regularization. Secondly, this paper introduces sliced Wasserstein distance, which is a weaker distribution measure compared with f-divergence. However, the number of randomly slicing poses a difficulty to estimate the true distance. In the end, we propose a projected sliced Wasserstein (PSW) autoencoder-based anomaly screening method. In particular, we leverage a computation-friendly eigen-decomposition method to find the principal component as slicing the high-dimensional data. Furthermore, our proposed distance can be calculated with the closed-form, even the prior distribution is not Gaussian. Comprehensive experiments conducted on various real-world hyperspectral anomaly detection benchmarks demonstrate the superior performance of our proposed method. © 2021, CC BY.

关键词： Spectroscopy

Sim4EndoR: A Reinforcement Learning Centered Simulation Platform for Task Automation of Endovascular robotics

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arXiv 2025年

作者： Yao, Tianliang Ban, Madaoji Lu, Bo Pei, Zhiqiang Qi, Peng Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Department of Electrical and Electronic Engineering Faculty of Engineering The University of Hong Kong 999077 Hong Kong Robotics and Microsystems Center School of Mechanical and Electric Engineering Soochow University Jiangsu Suzhou China School of Oriental Pan-Vascular Devices Innovation College University of Shanghai for Science and Technology 516 Jungong Road Shanghai200093 China State Key Laboratory of Cardiovascular Diseases Medical Innovation Center Shanghai East Hospital School of Medicine Tongji University Shanghai200092 China

Robotic-assisted percutaneous coronary intervention (PCI) holds considerable promise for elevating precision and safety in cardiovascular procedures. Nevertheless, current systems heavily depend on human operators, resulting in variability and the potential for human error. To tackle these challenges, Sim4EndoR, an innovative reinforcement learning (RL) based simulation environment, is first introduced to bolster task-level autonomy in PCI. This platform offers a comprehensive and risk-free environment for the development, evaluation, and refinement of potential autonomous systems, enhancing data collection efficiency and minimizing the need for costly hardware trials. A notable aspect of the groundbreaking Sim4EndoR is its reward function, which takes into account the anatomical constraints of the vascular environment, utilizing the geometric characteristics of vessels to steer the learning process. By seamlessly integrating advanced physical simulations with neural network-driven policy learning, Sim4EndoR fosters efficient sim-to-real translation, paving the way for safer, more consistent robotic interventions in clinical practice, ultimately improving patient outcomes. © 2025, CC BY.

关键词： Reinforcement learning

State observation of affine-in-the-states systems with unknown time-varying parameters and output delay

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arXiv 2021年

作者： Bobtsov, Alexey Nikolaev, Nikolay Ortega, Romeo Efimov, Denis Kozachek, Olga Department of Control Systems and Robotics ITMO University Kronverkskiy av. 49 Saint-Petersburg197101 Russia Departamento Académico de Sistemas Digitales ITAM Ciudad de México Mexico INRIA Univ. Lille CNRS UMR 9189 CRIStAL LilleF-59000 France Laboratory "Control of Complex Systems" Institute of Problems of Mechanical Engineering V.O. Bolshoj pr. 61 St. Petersburg199178 Russia

In this paper we address the problem of adaptive state observation of affine-in-the-states time-varying systems with delayed measurements and unknown parameters. The development of the results proposed in the [Bobtsov et al. 2021a] and in the [Bobtsov et al. 2021c] is considered. The case with known parameters has been studied by many researchers-see [Sanz et al. 2019, Bobtsov et al. 2021b] and references therein-where, similarly to the approach adopted here, the system is treated as a linear time-varying system. We show that the parameter estimation-based observer (PEBO) design proposed in [Ortega et al. 2015, 2021] provides a very simple solution for the unknown parameter case. Moreover, when PEBO is combined with the dynamic regressor extension and mixing (DREM) estimation technique [Aranovskiy et al. 2016, Ortega et al. 2019], the estimated state converges in fixed-time with extremely weak excitation assumptions. © 2021, CC BY-NC-ND.

关键词： Parameter estimation

Methods for Measuring the Just Noticeable Difference for Variable Stimuli: Implications for Perception of Metabolic Rate with Exoskeleton Assistance

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Methods for Measuring the Just Noticeable Difference for Var...

IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Roberto L. Medrano Gray C. Thomas Elliott Rouse Department of Mechanical Engineering Robotics Institute University of Michigan Ann Arbor MI USA Neurobionics Lab University of Michigan Ann Arbor MI USA Neurobionics and Locomotor Control Systems Labs University of Michigan

ISBN: (数字)9781728159072

ISBN: (纸本)9781728159089

The reduction of metabolic rate has become a pillar for quantifying the success of exoskeletons for performance augmentation, but how well can humans perceive these metabolic benefits? Measuring human perceptual ability in this context presents a unique challenge, since the stimulus of metabolic rate is not directly controllable. In this paper we introduce and compare two methods for addressing indirect stimuli: conventional method of constant stimuli (MOCS) with binning, and single presentation (SP). Both methods are based on a maximum likelihood estimation for a sigmoidal psychometric curve, under the assumption of a constant Weber fraction. Using Monte Carlo simulations, we conclude that while conventional MOCS with binning performs better for low actuation noise levels, single presentation strategies are better otherwise. We additionally present a pilot result for one subject using the single presentation strategy, which quantified the Just Noticeable Difference (JND) of changes to metabolic rate of 25.2 ± 23.2%.

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Adaptive finite-time synchronized control of multi-robotic fiber placement system with model uncertainties and disturbances

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ISA transactions 2025年

作者： Ronghua Zhang Yaonan Wang Wenfang Xie Pengcheng Li Haoran Tan Yiming Jiang College of Electrical and Information Engineering Hunan University Changsha 410082 Hunan China National Engineering Laboratory of Robot Visual Perception and Control Technology Hunan University Changsha 410082 Hunan China. Department of Mechanical Industrial and Aerospace Concordia University Montreal H3G2W1 Quebec Canada. College of Mechanical and Electrical Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 Jiangsu China. National Engineering Laboratory of Robot Visual Perception and Control Technology Hunan University Changsha 410082 Hunan China. Electronic address: tanhaoran@***. School of Robotics Hunan University Changsha 410082 Hunan China

The use of multiple robots to manufacture composite components represents a critical development direction for fiber placement systems (FPSs). In multi-robotic fiber placement systems (MRFPSs) with heterogeneous mechanical structures, robots collaborate to perform fiber placement tasks. Consequently, robot synchronization emerges as a primary factor in determining the performance of the fiber placement process. However, the difficulty in establishing accurate system models and the presence of disturbances are two significant challenges to achieving precise robot synchronization. Additionally, the system is expected to exhibit desirable dynamic characteristics, such as finite-time error convergence. To address these issues and requirements, we propose a novel adaptive finite-time synchronization control (AFSC) algorithm for the system. Specifically, a finite-time sliding mode observer is developed to handle kinematic uncertainty. A novel fast non-singular terminal sliding mode (FNTSM) manifold is constructed in the AFSC algorithm. Moreover, the control algorithm integrates an adaptive law to handle dynamic uncertainty and an adaptive term to counteract disturbances. Performance analysis demonstrates that the AFSC ensures that the coupled, synchronization, and tracking errors converge to zero within finite time. Furthermore, simulations and experiments are conducted to validate the effectiveness of the AFSC algorithm.

关键词： Disturbances Fiber placement Finite-time control Model uncertainties Multi-robots Synchronization control

Visual navigation in real-world Indoor environments using end-to-end deep reinforcement learning

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arXiv 2020年

作者： Kulhánek, Jonáš Derner, Erik Babuška, Robert Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague16636 Czech Republic Department of Control Engineering Faculty of Electrical Engineering Czech Technical University in Prague Prague16627 Czech Republic Cognitive Robotics Faculty of 3mE Delft University of Technology CD Delft2628 Netherlands Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague16636 Czech Republic

Visual navigation is essential for many applications in robotics, from manipulation, through mobile robotics to automated driving. Deep reinforcement learning (DRL) provides an elegant map-free approach integrating image processing, localization, and planning in one module, which can be trained and therefore optimized for a given environment. However, to date, DRL-based visual navigation was validated exclusively in simulation, where the simulator provides information that is not available in the real world, e.g., the robot’s position or image segmentation masks. This precludes the use of the learned policy on a real robot. Therefore, we propose a novel approach that enables a direct deployment of the trained policy on real robots. We have designed visual auxiliary tasks, a tailored reward scheme, and a new powerful simulator to facilitate domain randomization. The policy is fine-tuned on images collected from real-world environments. We have evaluated the method on a mobile robot in a real office environment. Training took ~30 hours on a single GPU. In 30 navigation experiments, the robot reached a 0.3-meter neighborhood of the goal in more than 86.7 % of cases. This result makes the proposed method directly applicable to tasks like mobile manipulation. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

Multi-model least squares-based recomputation framework for large data analysis

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arXiv 2021年

作者： Zhang, Wandong Wu, Q.M. Jonathan Yang, Yimin Zhao, W.G. Will Wang, TianLei Zhang, Hui Department of Electrical and Computer Engineering University of Windsor Windsor Canada Department of Computer Science Lakehead University Thunder Bay Canada Vector Institute for Artificial Intelligence Toronto Canada Faculty of Business Administration Lakehead University Canada Artificial Intelligence Group CEGEP de Ste Foy Canada Institute of Information and Control Hangzhou Dianzi University Hangzhou China School of Robotics Hunan University Changsha China

Most multilayer least squares (LS)-based neural networks, such as deep random vector functional link (RVFL), are structured with two separate stages: unsupervised feature encoding and supervised pattern classification. Once the unsupervised learning is finished, the latent encoding would be fixed without supervised fine-tuning. However, in complex tasks such as handling the ImageNet dataset, there are often many more clues that can be directly encoded, while the unsupervised learning, by definition, cannot know exactly what is useful for a certain task. There is a need to retrain the latent space representations in the supervised classification stage to learn some clues that unsupervised learning has not yet learned. In particular, the error matrix from the output layer is pulled back to each hidden layer, and the parameters of the hidden layer are recalculated with Moore-Penrose (MP) inverse for more generalized representations. In this paper, a recomputation-based multilayer network using MP inverse (RML-MP) is developed. A sparse RML-MP (SRML-MP) model to boost the performance of RML-MP is then proposed. The experimental results with varying training samples (from 3 K to 1.8 M) show that the proposed models provide better generalization performance than most representation learning algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding