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

作者： Zhanping Zhong Zhuoning Dong Xiaoming Duan Jianping He The School of Automation Science and Electrical Engineering Beihang University Beijing China The Department of Automation Ministry of Education of China Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Shanghai China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

We investigate a pursuit-evasion game taking place in an unbounded three-dimensional space, where a flexible pursuer with hybrid dynamics collaborates with a fast pursuer and aims to capture a flexible evader within a finite time. The key feature of this problem lies in the hybrid dynamics of the flexible pursuer, which can change its dynamics once during the game and switch to a fast pursuer with increased speed but lower maneuverability. To address this challenge, we devise a hybrid strategy based on the soft actor-critic framework, tailored specifically for the flexible pursuer, which encompasses both maneuvering and switch tactics. We introduce a switch factor to the input of the actor network and incorporate switch actions to further expand the action space. These additions enable the flexible pursuer to execute maneuvering actions and determine a moment to switch to a fast pursuer. The reward function is designed to account for related angle, altitude, speed, and sparse reward. Through extensive ablation experiments conducted in a simulated environment, we demonstrate the efficacy of our algorithm in facilitating the learning of hybrid strategies for the flexible pursuer, resulting in significantly improved capture rates compared to alternative methods.

关键词： Solid modeling Three-dimensional displays Heuristic algorithms Collaboration Switches Games Deep reinforcement learning Vectors Intelligent robots

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A GM-TCN based Hybrid Prediction Method for Ash Fouling Prediction in Coal-fired Power Plant Boiler

A GM-TCN based Hybrid Prediction Method for Ash Fouling Pred...

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2022 Chinese Automation Congress, CAC 2022

作者： Shi, Yuanhao Han, Tianxiang Cui, Fangshu Wen, Jie Qiao, Gangzhu North University of China School of Electrical and Control Engineering Taiyuan China Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China North University of China School of Data Science and Technology Taiyuan China

ISBN: (纸本)9781665465335

In response to the problem that ash deposition in boilers has a great impact on production efficiency as well as production safety, a grey level theory prediction model based on time convolution networks (GM-TCN) is proposed to predict the degree of ash in boiler heated areas by using a cleaning factor to assess the ash deposition condition on the heated surfaces. The accuracy of the prediction model is improved by utilizing the features of grey level theory that can predict the developmental changes in system behavior, combined with the high-precision approximation capability of the time-convolutional network. The modelling prediction of the ash level in the heated area of boilers during the production of coal-fired power stations is investigated. Experiments demonstrate that the model can predict the cleanliness factor with high accuracy, and the predicted values are closest to the true values compared to the other two models, verifying the accuracy and reliability of the model. © 2022 IEEE.

关键词： Convolution

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Sensor Fault Detection and Isolation in Autonomous Nonlinear systems Using Neural Network-Based Observers

Sensor Fault Detection and Isolation in Autonomous Nonlinear...

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European control Conference (ECC)

作者： John Cao M. Umar B. Niazi Matthieu Barreau Karl Henrik Johansson Division of Decision and Control Systems Digital Futures KTH Royal Institute of Technology Stockholm Sweden Department of Electrical Engineering and Computer Science Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

This paper presents a novel observer-based approach to detect and isolate faulty sensors in nonlinear systems. The proposed sensor fault detection and isolation (s-FDI) method applies to a general class of nonlinear systems. Our focus is on s-FDI for two types of faults: complete failure and sensor degradation. The key aspect of this approach lies in the utilization of a neural network-based Kazantzis-Kravaris/Luenberger (KKL) observer. The neural network is trained to learn the dynamics of the observer, enabling accurate output predictions of the system. Sensor faults are detected by comparing the actual output measurements with the predicted values. If the difference surpasses a theoretical threshold, a sensor fault is detected. To identify and isolate which sensor is faulty, we compare the numerical difference of each sensor measurement with an empirically derived threshold. We derive both theoretical and empirical thresholds for detection and isolation, respectively. Notably, the proposed approach is robust to measurement noise and system uncertainties. Its effectiveness is demonstrated through numerical simulations of sensor faults in a network of Kuramoto oscillators.

关键词： Uncertainty Fault detection Noise Observers Numerical simulation Sensor systems Sensors

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Highly Adaptive Single- and Wide-Band Cooperative Rectifiers for Electromagnetic-Luminous Hybrid Ambient Energy Harvesting

SSRN

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SSRN 2024年

作者： Zeng, Baihua Zheng, Shaoyong Chan, Wing Shing School of Electronics and Information Technology Guangdong Provincial Key Laboratory of Optoelectronic Information Processing Chips and Systems Sun Yat-sen University Guangzhou510006 China Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong

With the rapid development of emerging wireless communication technologies, the scattered ambient electromagnetic (EM) power at different frequencies becomes an attractive energy source for low-power IoT devices. As the essential component of an ambient EM energy harvesting system, a rectifier should exhibit high conversion efficiency at low input power levels. Theoretical analysis of Schottky diodes indicates that conversion efficiencies can be significantly enhanced under hybrid input power excitation. Based on this operating principle, a single-band cooperative rectifying topology with two harmonic-terminated networks is first proposed and extended to a wide-band configuration to harvest EM- luminous hybrid ambient energy adaptively. Both single- and wide-band cooperative rectifiers were fabricated and measured for validation. Under different indoor light sources irradiation, the single-band cooperative rectifier maintained conversion efficiencies higher than 46% at 2.4 GHz within an input EM power range of 40 dB, and the wide-band cooperative rectifier realized an ultra-wide bandwidth from 0.8 to 2.4 GHz with conversion efficiencies over 30%. Compared to the existing state-of-the-art works, the implemented cooperative rectifiers have realized an extended input power range and ultra-wide bandwidth for hybrid ambient energy harvesting. © 2024, The Authors. All rights reserved.

关键词： Conversion efficiency

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System Level Synthesis for Affine control Policies: Model Based and Data-Driven Settings

arXiv

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arXiv 2025年

作者： Schüepp, Lukas De Pasquale, Giulia Dörfler, Florian Alonso, Carmen Amo Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Physikstrasse 3 Zurich8092 Switzerland Computer Science Department of Stanford University Palo AltoCA United States Control Systems Group Department of Electrical Engineering TU Eindhoven Flux Groene Loper 19 Eindhoven5612AP Netherlands

There is an increasing need for effective control of systems with complex dynamics, particularly through data-driven approaches. System Level Synthesis (SLS) has emerged as a powerful framework that facilitates the control of large-scale systems while accounting for model uncertainties. SLS approaches are currently limited to linear systems and time-varying linear control policies, thus limiting the class of achievable control strategies. We introduce a novel closed-loop parameterization for time-varying affine control policies, extending the SLS framework to a broader class of systems and policies. We show that the closed-loop behavior under affine policies can be equivalently characterized using past system trajectories, enabling a fully data-driven formulation. This parameterization seamlessly integrates affine policies into optimal control problems, allowing for a closed-loop formulation of general Model Predictive control (MPC) problems. To the best of our knowledge, this is the first work to extend SLS to affine policies in both model-based and data-driven settings, enabling an equivalent formulation of MPC problems using closed-loop maps. We validate our approach through numerical experiments, demonstrating that our model-based and data-driven affine SLS formulations achieve performance on par with traditional model-based MPC. © 2025, CC BY.

关键词： Distributed parameter control systems

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IRS/UAV-Based Edge-Computing and Traffic-Offioading Over 6G THz Mobile Wireless Networks

IRS/UAV-Based Edge-Computing and Traffic-Offioading Over 6G ...

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IEEE International Conference on Communications (ICC)

作者： Fei Wang Xi Zhang Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&M University College Station TX USA

Intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and Terahertz (THz) communications, which are recognized as 6G promising techniques, have attracted significant attentions. We propose UAV energy minimization schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over broadband THz mobile networks. In the networks, multiple UAVs serving as MEC-servers collect data from multiple ground users (GUs) with the assistance of a set of passive IRSs. First, we formulate a UAV energy minimization problem, which jointly optimizes the IRSs' phase shifts, UAVs' trajectories, and system computation and communication resources. For THz communications, since the composite channel power gains of GUs are complicated functions of UAVs' trajectories and IRSs' phase shifts, the formulated optimization problem is non-convex. Then, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which then can be iteratively solved. Finally, we validate and evaluate the proposed schemes by numerical analyses, which show that the energy consumption of UAVs can be reduced by around 40% by using IRSs.

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Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-Based 6G Mobile Wireless Networks

Active-IRS-Enabled Energy-Efficiency Optimizations for UAV-B...

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Annual Conference on information Sciences and systems (CISS)

作者： Fei Wang Xi Zhang Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing China Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&M University College Station TX USA

We propose the novel scheme to solve a multi-objective optimization problem over an unmanned aerial vehicle (UAV) communications system to jointly minimize the energy consumption of the UAV and ground users (GUs). In particular, the UAV communicates with multiple GUs using active intelligent reflecting surfaces (IRSs), which can actively amplify and thus significantly enhance the strengths of reflected signals. We develop an energy minimization scheme based on the multi-objective hierarchical deep reinforcement learning (DRL), by decomposing the formulated optimization problem into two-layered subproblems. By solving the upper-level subproblem, we derive the optimal UAV trajectory and GUs scheduling strategies to minimize the UAV's energy consumption. By solving the lower-level subproblem, we obtain the IRS's phase shifts and amplification factors and GUs' transmit/receive beamforming to minimize the GUs' energy consumption. Finally, we validate and evaluate the proposed schemes through simulations, which show that the UAV's and GUs' energy consumption can be significantly reduced by using the active IRS, when the thermal noise powers at the IRS are much smaller than those at the UAV and GUs.

关键词： Energy consumption Communication systems Wireless networks Reinforcement learning Autonomous aerial vehicles Minimization Thermal noise

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Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

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IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

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Finite-Time Sideslip Differentiator-Based LOS Guidance for Robust Path Following of Snake Robots

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 239-253页

作者： Yang Xiu Dongfang Li Miaomiao Zhang Hongbin Deng Rob Law Yun Huang Edmond Q.Wu Xin Xu the School of Mechatronical Engineering Beijing Institute of TechnologyBeijing 100081China the School of Electrical Engineering and Automation Fuzhou UniversityFuzhou 350108 the Antai College of Economics&Management Shanghai Jiao Tong UniversityShanghai 200240China the Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China the Asia-Pacific Academy of Economics and Management University of MacaoMacao 999078China the Department of Decision Science Macao University of Science and TechnologyMacao 999078China the Department of Automation Shanghai Jiao Tong Universityand also with the Key Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai 200240China the College of Mechatronics and Automation National University of Defense TechnologyChangsha 410073China IEEE

This paper presents a finite-time sideslip differentiator-based line-of-sight(LOS)guidance method for robust path following of snake ***,finite-time stable sideslip differentiator and adaptive LOS guidance method are proposed to counteract sideslip drift caused by cross-track *** proposed differentiator can accurately observe the cross-track error and sideslip angle for snake robots to avoid errors caused by calculating sideslip angle *** our method,the designed piecewise auxiliary function guarantees the finite-time stability of position ***,for the case of external disturbances and state constraints,a Barrier Lyapunov functionbased backstepping adaptive path following controller is presented to improve the robot’s *** uniform ultimate boundedness of the closed-loop system is proved by analyzing ***,a gait frequency adjustment-based virtual velocity control input is derived to achieve the exponential convergence of the tangential *** last,the availability and superiority of this work are shown through simulation and experiment results.

关键词： Line-of-sight(LOS) path following sideslip snake robot

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A Machine-Learning Architecture for Sensor Fault Detection, Isolation, and Accommodation in Digital Twins

A Machine-Learning Architecture for Sensor Fault Detection, ...

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作者： Darvishi, Hossein Ciuonzo, Domenico Rossi, Pierluigi Salvo Norwegian University of Science and Technology Department of Electronic Systems Trondheim 7491 Norway École Polytechnique Fédérale de Lausanne (EPFL) Signal Processing Laboratory (LTS4) Lausanne 1015 Switzerland University of Naples Federico Ii Department of Electrical Engineering and Information Technologies (DIETI) Naples 80125 Italy Sintef Energy Research Department of Gas Technology Trondheim 7491 Norway

Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time raw data into digital twins (DTs). However, sensors might be unreliable due to inherent issues and/or environmental conditions. This article aims at detecting anomalies instantaneously in measurements from sensors, identifying the faulty ones and accommodating them with appropriate estimated data, thus paving the way to reliable DTs. More specifically, a real-time general machine-learning-based architecture for sensor validation is proposed, built upon a series of neural-network estimators and a classifier. Estimators correspond to virtual sensors of all unreliable sensors (to reconstruct normal behavior and replace the isolated faulty sensor within the system), whereas the classifier is used for detection and isolation tasks. A comprehensive statistical analysis on three different real-world datasets is conducted and the performance of the proposed architecture is validated under hard and soft synthetically generated faults. © 2001-2012 IEEE.

关键词： Fault detection

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