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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Grobbel, Max Varga, Balint Hohmann, Sören Fzi - Forschungszentrum Informatik Karlsruhe76131 Germany Institute of Control Systems Karlsruhe Institute of Technology Karlsruhe76131 Germany

ISBN: (纸本)9798350337020

Telemanipulation has become a promising technology that combines human intelligence with robotic capabilities to perform tasks remotely. However, it faces several challenges such as insufficient transparency, low immersion, and limited feedback to the human operator. Moreover, the high cost of haptic interfaces is a major limitation for the application of telemanipulation in various fields, including elder care, where our research is focused. To address these challenges, this paper proposes the usage of nonlinear model predictive control for telemanipulation using low-cost virtual reality controllers, including multiple control goals in the objective function. The framework utilizes models for human input prediction and task-related models of the robot and the environment. The proposed framework is validated on an UR5e robot arm in the scenario of handling liquid without spilling. Further extensions of the framework such as pouring assistance and collision avoidance can easily be included. © 2023 IEEE.

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Response probability density of a single-degree-of-freedom linear system under non-Gaussian random excitation with dominant frequency 14

Response probability density of a single-degree-of-freedom l...

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14th International Conference on Recent Advances in Structural Dynamics, RASD 2024

作者： Tsuchida, Takahiro Department of Systems and Control Engineering Tokyo Institute of Technology Ookayama 2-12-1 Meguro-ku Tokyo152-8552 Japan

The response probability density function (PDF) of a single-degree-of-freedom linear system under non-Gaussian random excitation with a non-zero dominant frequency is investigated. The non-Gaussian excitation is a zero-mean stationary stochastic process prescribed by a first-order PDF and a power spectral density (PSD) with bandwidth and dominant frequency parameters. Uniform and Laplace distributions are considered for the excitation PDF. Monte Carlo simulations are performed to obtain the stationary displacement and velocity PDFs of the system. The simulation results indicate that the effects of excitation non-Gaussianity on the response PDFs vary greatly, depending on the bandwidth and dominant frequency of the excitation PSD. Particularly, the excitation non-Gaussianity appears strongly in the response PDFs when the excitation has a narrow bandwidth and the excitation dominant frequency matches the natural frequency of the system. Next, it is shown that when the excitation and response waveforms are similar, the response PDFs take a shape similar to the excitation PDF;on the other hand, when the waveforms are different, the response PDFs are nearly Gaussian, irrespective of the shape of the excitation PDF. Furthermore, as a quantitative index of waveform similarity, the maximum absolute value of the normalized cross-correlation function of excitation and response is introduced. The correspondence between the index value and the non-Gaussianity of the response PDF is revealed. © 2024 institute of Physics Publishing. All rights reserved.

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Denoising and detection for binary black hole gravitational waves in the context of the Einstein Telescope

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Science China(Physics,Mechanics & Astronomy) 2025年第7期Mechanics & Astronomy) .卷 203-219页

作者： CunLiang Ma ZeHua Liu ZhiFu Gao Zhoujian Cao MingZhen Jia Kai Wei TanMing Dai JunQin Wu Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control School of Information Engineering Jiangxi University of Science and Technology Xinjiang Astronomical Observatory Chinese Academy of Sciences Institute of Applied Mathematics Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced StudyUCAS

With the advent of third-generation(3G) ground-based gravitational wave(GW) detectors,such as the Einstein Telescope(ET),we anticipate a substantial enhancement in sensitivity across a wide frequency *** machine learning approach for GW search necessitates an update to address the challenges posed by data features that deviate from those of 2G *** this paper,we introduce a novel GW search pipeline specifically designed for 3G ground-based detectors like *** pipeline leverages three types of deep learning models:an envelope extraction model,a denoising model,and an astrophysical origin discrimination ***,we propose a signal consistency test across multiple *** that denoising results vary among different detectors,we present a new method for selecting the optimal *** selected waveform serves as a template for estimating the signal-to-noise ratio(SNR) of strain data from all ***,if 3G detectors operate alongside 2G detectors,the templates derived from 3G detector data can be utilized to predict the SNR for 2G detectors,significantly reducing the computational burden of GW searches for the *** also assess the robustness of our method when applied to data containing binary neutron star(BNS) foreground *** believe that the proposed method holds promise for detecting BBH events in future 3G detectors.

关键词： gravitational wave search machine learning Einstein Telescope gravitational wave denoising

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Model-based dynamic periodic event-triggered control for nonlinear networked control systems with transmission delays 63

Model-based dynamic periodic event-triggered control for non...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Li, Wangjiang Yu, Hao Dhullipalla, Mani H. Shi, Dawei Beijing Institute of Technology School of Automation Beijing100081 China Division of Decision and Control Systems School of Eecs Stockholm10044 Sweden

ISBN: (纸本)9798350316339

This paper considers dynamic periodic eventtriggered control for nonlinear networked control systems. A model-based periodic event-triggering mechanism is proposed to potentially reduce the consumption of transmission resources for the networked control systems that are subject to time-varying inter-sampling intervals, transmission delays, and scheduling protocols. Furthermore, to compensate for the adverse effects of delays, the controller node is equipped with two specialized units: a propagation unit and a model unit. The role of propagation units is to work with the delayed data, using the data to estimate its current value and subsequently updating the state within the model unit. A predictor unit is employed at the sensor node to configure event-triggering conditions. A hybrid system framework is used to model the networked control systems. Moreover, sufficient conditions on the transmission intervals, delays and dynamic event-triggered control are given to ensure closed-loop asymptotic stability. Additionally, an allocation framework of the event-triggering mechanism is proposed, taking into account the information available at the sensor node. Finally, an example of a singlelink robot arm is simulated to illustrate the effectiveness and feasibility of the theoretical results. © 2024 IEEE.

关键词： Networked control systems

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Validation of a Limit Ellipsis controller for Rescue Drones 16

Validation of a Limit Ellipsis Controller for Rescue Drones

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16th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2022

作者： Varga, Balint Doer, Christopher Trommer, Gert F. Hohmann, Soren Institute of Control Systems Karlsruhe Institute for Technology Karlsruhe76131 Germany

ISBN: (数字)9781665481250

ISBN: (纸本)9781665481250

In recent years, more and more robotic systems have been supporting rescue forces in their missions. This paper presents the control algorithm and its application of an unmanned areal vehicle (UAV), which can support emergency personnel in their work. An adaption of the so-called Limit Ellipsis controller (LEC) for indoor UAV is proposed. This adaptation enables the LEC to be used in semi-structured environments with static and dynamic obstacles. The main benefit of the LEC is that it can prevent deadlocks caused by other methods in complex environments. Furthermore, the LEC function is implemented on an experimental UAV system and tested in various environments. The proposed technical system enables exploration of a building by a semi-autonomous UAV, saving valuable rescue time. The UAV can fly into the building and explore the interior without collision. The results show that the proposed controller can adequately avoid local minima, guide the UAV to the desired target, and provide essential information for the rescue team in real demonstration scenarios. © 2022 IEEE.

关键词： controllers

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The DAO to Metacontrol for Metasystems in Metaverses:The System of Parallel control systems for Knowledge Automation and control Intelligence in CPSS

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IEEE/CAA Journal of automatica Sinica 2022年第11期9卷 1899-1908页

作者： Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing100190Chi the Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao999078China

An investigation and outline of Metacontrol and Decontrol in Metaverses for control intelligence and knowledge automation are *** control with prescriptive knowledge and parallel philosophy is proposed as the starting point for the new control philosophy and technology,especially for computational control of metasystems in cyberphysical-social *** argue that circular causality,the generalized feedback mechanism for complex and purposive systems,should be adapted as the fundamental principle for control and management of metasystems with metacomplexity in ***,an interdisciplinary approach is suggested for Metacontrol and Decontrol as a new form of intelligent control based on five control metaverses:MetaVerses,MultiVerses,InterVerses,TransVerse,and DeepVerses.

关键词： Metacontrol Metasystems Metaverses CPSS DAO Decontrol Prescriptive control Parallel control Computational control Knowledge Automation control Intelligence Parallel Intelligence

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Differentially Private Distributed Parameter Estimation

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Journal of systems Science & Complexity 2023年第1期36卷 187-204页

作者： WANG Jimin TAN Jianwei ZHANG Ji-Feng School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China

Data privacy is an important issue in control systems,especially when datasets contain sensitive information about *** this paper,the authors are concerned with the differentially private distributed parameter estimation problem,that is,we estimate an unknown parameter while protecting the sensitive information of each ***,the authors propose a distributed stochastic approximation estimation algorithm in the form of the differentially private consensus+innovations(DP-CI),and establish the privacy and convergence property of the proposed ***,it is shown that the proposed algorithm asymptotically unbiased converges in mean-square to the unknown parameter while differential privacy-preserving holds for finite number of ***,the exponentially damping step-size and privacy noise for DP-CI algorithm is *** estimate approximately converges to the unknown parameter with an error proportional to the step-size parameter while differential privacy-preserving holds for all *** tradeoff between accuracy and privacy of the algorithm is effectively ***,a simulation example is provided to verify the effectiveness of the proposed algorithm.

关键词： Differential privacy distributed parameter estimation stochastic approximation

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Combine deep learning and Bayesian analysis to separate overlapping gravitational wave signals

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Science China(Physics,Mechanics & Astronomy) 2025年第5期68卷 205-219页

作者： Cunliang Ma Weiguang Zhou Zhoujian Cao Mingzhen Jia Jiangxi Provincial Key Laboratory of Multidimensional Intelligent Perception and Control Jiangxi University of Science and TechnologyGanzhou 341000China Institute of Applied Mathematics Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced StudyUCASHangzhou 310024China

Future gravitational wave(GW)observatories,such as the Einstein Telescope,are anticipated to encounter overlapping GW signals,presenting considerable obstacles to GW data processing techniques,including signal identification and parameter *** this letter,we propose a scheme of combining deep learning and Bayesian analysis to disentangle overlapping GW *** deep learning part takes a data-driven approach that employs an encoder-separation-decoder framework which is powerful enough to extract the shape of the signal even when the GW signals completely *** Bayesian analysis part takes the matched filtering technique to extract the amplitude of the GW *** scheme can facilitate the utilization of existing GW detection and parameter estimation methods for future instances of overlapping *** methodology effectively reduces biases in parameter estimation when handling multiple intertwined ***,this marks the first known instance where deep learning has been successfully utilized to disentangle overlapping GW signals.

关键词： gravitational wave separation machine learning Einstein Telescope parameter estimation

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On final opinions of the Friedkin-Johnsen model over random graphs with partially stubborn community 63

On final opinions of the Friedkin-Johnsen model over random ...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Wang, Lingfei Xing, Yu Johansson, Karl H. Kth Royal Institute of Technology and with Digital Futures Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm Sweden

ISBN: (纸本)9798350316339

This paper studies the formation of final opinions for the Friedkin-Johnsen (FJ) model with a community of partially stubborn agents. The underlying network of the FJ model is symmetric and generated from a random graph model, in which each link is added independently from a Bernoulli distribution. It is shown that the final opinions of the FJ model will concentrate around those of an FJ model over the expected graph as the network size grows, on the condition that the stubborn agents are well connected to other agents. Probability bounds are proposed for the distance between these two final opinion vectors, respectively for the cases where there exist non-stubborn agents or not. Numerical experiments are provided to illustrate the theoretical findings. The simulation shows that, in presence of non-stubborn agents, the link probability between the stubborn and the non-stubborn communities affect the distance between the two final opinion vectors significantly. Additionally, if all agents are stubborn, the opinion distance decreases with the agent stubbornness. © 2024 IEEE.

关键词： Graph theory

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Mean policy-based proximal policy optimization for maneuvering decision in multi-UAV air combat

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Neural Computing and Applications 2024年第31期36卷 19667-19690页

作者： Zheng, Yifan Xin, Bin He, Bin Ding, Yulong Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China School of Automation Beijing Institute of Technology Beijing China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

Autonomous maneuvering decision-making is a crucial technology for Unmanned Aerial Vehicles (UAVs) to take the air domination in modern unmanned warfare. With the advantage of balancing exploration and exploitation, as well as the immediacy of end-to-end output by combining with deep neural network, multi-agent reinforcement learning (MARL) has made remarkable achievements in multi-UAV autonomous air combat maneuvering decision-making (MUAAMD). However, the implementation of effective cooperative policy learning remains a challenging issue for MARL methods with centralized training decentralized execution (CTDE) paradigm. This paper proposes a MARL-based method to improve the performance of cooperation in MUAAMD. Firstly, considering the constraints of dynamic and limited perception for UAVs in the realistic air combat scenario, the MUAAMD problem is formulated based on partially observable Markov game (POMG) model. Secondly, a novel efficient MARL algorithm named the mean policy-based proximal policy optimization (MP3O) is introduced. Specifically, a joint policy optimization mechanism is constructed by estimating the policies of neighboring agents in group as a mean-field approximation while training, which enables both centralized evaluation and improvement of cooperative policy under the CTDE paradigm. Thirdly, by combining with three improvement techniques, a cooperative decision-making framework for MUAAMD based on MP3O is proposed. Empirically, results of simulations and comparative experiments validate the effectiveness of proposed method in promoting cooperative policy learning in resolving MUAAMD problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Decision making

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