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Collision-free motion control with learning features for automated vehicles in roundabouts

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Transportation Research Procedia 2023年 72卷 3545-3552页

作者： Balázs Németh Zsófia Farkas Zoltán Antal Dániel Fényes Péter Gáspár Institute for Computer Science and Control (SZTAKI) Eötvös Loránd Research Network (ELKH) Kende utca 13-15. 1111 Budapest Hungary Budapest University of Technology and Economics Department of Control for Transportation and Vehicle Systems Stoczek utca 2. 1111 Budapest Hungary

control design for safe and time-efficient motion of automated vehicles in roundabout scenarios poses various challenges, especially adaptation to the actual traffic scenario and coordination of the vehicles. This paper proposes the design of a hierarchical motion control with learning feature for roundabout scenarios. The control is designed on two levels, such as on cloud level and on vehicle level. The control on the cloud level is designed by using reinforcement learning ( RL ), with which the energy efficient motion of the vehicle is achieved. The vehicle level contains a robust controller and a supervisor, with which the collision avoidance of the vehicles is guaranteed. The proposed control on a Hardware-in-the-Loop environment with small-scaled indoor vehicles in augmented reality is implemented. The effectiveness of the control and the safe motion of the automated vehicles under multi -vehicle scenario are demonstrated. The provided scenario illustrates that safe, i.e., collision-free motion of all automated vehicles can be guaranteed.

关键词： automated vehicles learning features roundabouts collision-free motion control design

Consistent and Asymptotically Efficient Localization from Range-Difference Measurements

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arXiv 2023年

作者： Zeng, Guangyang Mu, Biqiang Shi, Ling Chen, Jiming Wu, Junfeng The School of Data Science Chinese University of Hong Kong Shenzhen Shenzhen China The Department of Automation University of Science and Technology of China Hefei China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong The College of Control Science and Engineering The State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou310027 China

We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee the asymptotic identifiability of the model and show the consistency and asymptotic normality of the maximum likelihood (ML) estimator. Then, we devise an estimator that owns the same asymptotic property as the ML one. Specifically, we prove that the negative log-likelihood function converges to a function, which has a unique minimum and positive definite Hessian at the true source's position. Hence, it is promising to execute local iterations, e.g., the Gauss-Newton (GN) algorithm, following a consistent estimate. The main issue involved is obtaining a preliminary consistent estimate. To this aim, we construct a linear least-squares problem via algebraic operation and constraint relaxation and obtain a closed-form solution. We then focus on deriving and eliminating the bias of the linear least-squares estimator, which yields an asymptotically unbiased (thus consistent) estimate. Noting that the bias is a function of the noise variance, we further devise a consistent noise variance estimator that involves 3-order polynomial rooting. Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator. Simulation results demonstrate the superiority of our proposed algorithm in the large sample case. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

Leakage Localization in Water Distribution Networks: A Model-Based Approach

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arXiv 2022年

作者： Lindström, Ludvig Gracy, Sebin Magnússon, Sindri Sandberg, Henrik The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology and Digital Futures Stockholm Sweden The Department of Computer and Systems Sciences Stockholm University Sweden

The paper studies the problem of leakage localization in water distribution networks. For the case of a single pipe that suffers from a single leak, by taking recourse to pressure and flow measurements, and assuming those are noiseless, we provide a closed-form expression for leak localization, leak exponent and leak constant. For the aforementioned setting, but with noisy pressure and flow measurements, an expression for estimating the location of the leak is provided. Finally, assuming the existence of a single leak, for a network comprising of more than one pipe and assuming that the network has a tree structure, we provide a systematic procedure for determining the leak location, the leak exponent, and the leak constant. © 2022, CC BY.

关键词： Water distribution systems

Pinning control of chimera states in systems with higher-order interactions

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arXiv 2024年

作者： Muolo, Riccardo Gambuzza, Lucia Valentina Nakao, Hiroya Frasca, Mattia Department of Systems and Control Engineering Institute of Science Tokyo former Tokyo Institute of Technology Tokyo152-8552 Japan Department of Electrical Electronics and Computer Science Engineering University of Catania Catania95125 Italy Research Center for Autonomous Systems Materialogy Institute of Innovative Research Institute of Science Tokyo former Tokyo Institute of Technology Kanagawa226-8501 Japan

Understanding and controlling the mechanisms behind synchronization phenomena is of paramount importance in nonlinear science. In particular, the emergence of chimera states, patterns in which order and disorder coexist simultaneously, continues to puzzle scholars, due to its elusive nature. Recently, it has been shown that higher-order (many-body) interactions greatly enhance the presence of chimera states, which are easier to be found and more persistent. In this work, we show that the higher-order framework is fertile not only for the emergence of chimera states, but also for its control. Via pinning control, a technique consisting in applying a forcing to a subset of the nodes, we are able to trigger the emergence of chimera states with only a small fraction of controlled nodes, at striking contrast with the case without higher-order interactions. We show that our setting is robust for different higher-order topologies and types of pinning control and, finally, we give a heuristic interpretation of the results via phase reduction theory. Our numerical and theoretical results provide further understanding on how higher-order interactions shape collective behaviors in nonlinear dynamics. © 2024, CC BY.

关键词： Synchronization

Inverse Kalman Filtering for systems with Correlated Noises

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Inverse Kalman Filtering for Systems with Correlated Noises

IEEE Conference on Decision and control

作者： Yibei Li Xiaoming Hu Bo Wahlberg Lihua Xie School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Mathematics Division of Optimization and Systems Theory KTH Royal Institute of Technology Sweden Division of Decision and Control Systems School of Electrical Engineering and Computer Sciencem KTH Royal Institute of Technology Sweden

This paper focuses on two inverse problems of the Kalman filter in which the process and measurement noises are correlated. The unknown covariance matrix in a stochastic system is reconstructed from observations of its posterior beliefs. For the standard inverse Kalman filtering problem, a novel duality-based formulation is proposed, where a well-defined inverse optimal control (IOC) problem is solved instead. Identifiability of the underlying model is proved, and a least squares estimator is designed that is statistically consistent. The time-invariant case using the steady-state Kalman gain is further studied. Since this inverse problem is ill-posed, a canonical class of covariance matrices is constructed, which can be uniquely identified from the dataset with asymptotic convergence. Finally, the performances of the proposed methods are illustrated by numerical examples.

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A Consumer Electronics-Enhanced UAV System for Agricultural Farm Tracking With Fuzzy SMO and Actuator Fault Detection control Algorithms

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IEEE Transactions on Consumer Electronics 2025年

作者： Bilal, Hazrat Aslam, Muhammad Shamrooz Tian, Yibin Ullah, Inam Ayouni, Sarra Vasilakos, Athanasios V. College of Mechatronics and Control Engineering College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou2211106 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Gachon University Department of Computer Engineering Seongnam13120 Korea Republic of Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan Princess Nourah bint Abdulrahman University Department of Information Systems College of Computer and Information Sciences Saudi Arabia Grimstad Norway

The adoption of agricultural robots, or agrobots, has revolutionized modern farming operations, ranging from crop monitoring to automated harvesting, significantly boosting productivity. Motivated by the rapid advancements in agrobots and their integration into smart agricultural practices, this study proposes an autonomous trajectory tracking system for wheat farms using quadcopter UAVs. To address actuator fault detection, including stuck faults and partial loss of efficiency, a TSF-H∞-SMO (Takagi-Sugeno Fuzzy-based H∞ Sliding Mode Observer) fault detection framework is introduced. The approach initiates with the derivation of a TSF (Takagi-Sugeno Fuzzy) attitude control model that integrates an uncertainty term, constructed from the original nonlinear dynamics of the UAV and approximated through local linear models at four equilibrium positions. An actuator fault model is subsequently integrated to develop a comprehensive TSF-UAV model, accounting for actuator faults. The TSF-H∞-SMO is then designed using matrix coordinate transformation to enable precise fault detection. The fault detection capabilities of the TSF-H∞-SMO are evaluated through simulations on the TSF-UAV model under SISO (single-input single-output) actuator fault scenarios. The experimental results validate the proposed system, demonstrating its ability to detect a range of actuator faults accurately and promptly. The analysis reveals a proportional relationship between the amplitude of the state change and the severity of the fault, attributed to the interaction between system states and actuator flaps. This approach underscores the potential for deploying autonomous UAV-based fault detection and trajectory tracking systems in agricultural applications. Furthermore, integrating such advanced fault-tolerant control algorithms holds promise for consumer technology applications, where precision, reliability, and robustness are critical to enhancing system performance and operational efficiency. © 1

关键词： Agricultural robots

Data-Driven Adequacy and Security Assessment: Machine Learning Trends for Reliable Smart Energy Networks

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Data-Driven Adequacy and Security Assessment: Machine Learni...

Smart Grid Conference (SGC)

作者： Mohammad Mohsen Hayati Hassan Majidi Hossein Shahinzadeh Mehdi Abapour Mahmud Fotuhi-Firuzabad Gevork B. Gharehpetian Faculty of Electrical and Computer Engineering Energy Systems Research Institute (ESRI) University of Tabriz Tabriz Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Iran Center of Excellence in Power System Management and Control (CEPSMC) Sharif University of Technology Tehran Iran

ISBN: (数字)9798331511333

ISBN: (纸本)9798331511340

Smart energy networks (SENs) face numerous challenges related to security, reliability, operational uncertainties, and increasing penetration of renewable energies (REs), necessitating advanced methodologies for adequacy and security assessment. This study delves into the utilization of machine learning-driven approaches (MLDAs) to quantify reliability and gauge the dependability of future sustainable SENs. By autonomously acquiring knowledge from datasets, MLDAs offer swift and reliable evaluations, surpassing traditional methodologies. The integration of artificial intelligence (AI) in enhancing power system reliability is highlighted, emphasizing the importance of extensive datasets and evolving AI algorithms. Data-driven approaches empower energy network operators to proactively identify and mitigate security risks, enhancing the reliability of critical infrastructure systems. The research underscores the significance of constructing reliable databases, addressing challenges in real-time operations, and ensuring the performance and trustworthiness of machine learning (ML) algorithms. Overall, the study showcases the potential of ML techniques in revolutionizing reliability management and risk assessment in smart energy networks, paving the way for sustainable and reliable power systems.

关键词： Uncertainty Machine learning algorithms Databases Machine learning Power system reliability Smart grids Management Reliability Security Risk management

Self-Triggered Ternary control for Resilient Consensus against Mobile Adversarial Agents

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Self-Triggered Ternary Control for Resilient Consensus again...

IEEE Conference on Decision and control

作者： Hiroki Matsume Yuan Wang Hideaki Ishii Xavier Défago Department of Computer Science Tokyo Institute of Technology Tokyo/Yokohama Japan Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

In this paper, we consider the problem of multi-agent consensus in the presence of mobile adversaries. Faulty agents try to prevent the coordination among the regular agents and moreover are mobile in the sense that they can change their identities over time. Our approach towards resilient consensus is to extend the so-called mean subsequence reduced (MSR) algorithms to reduce the necessary communication based on two measures: The information exchanged by the agents takes the form of ternary data in each message and furthermore self-triggered method is used to keep the transmission frequency limited. Certain features are introduced to address issues specific to the mobile nature of the adversarial agents. We verify the effectiveness of the proposed algorithm by means of a numerical example.

关键词： Frequency measurement Consensus protocol Behavioral sciences Resilience

Benchmarking Semantic Communications for Image Transmission Over MIMO Interference Channels

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arXiv 2024年

作者： Wang, Yanhu Guo, Shuaishuai Dong, Anming Zhao, Hui School of Control Science and Engineering Shandong Key Laboratory of Wireless Communication Technologies Shandong University Jinan250061 China The School of Computer Science and Technology Qilu University of Technology Jinan250353 China The Communication Systems Department EURECOM Sophia Antipolis 06410 France

Semantic communications offer promising prospects for enhancing data transmission efficiency. However, existing schemes have predominantly concentrated on point-to-point transmissions. In this paper, we aim to investigate the validity of this claim in interference scenarios compared to baseline approaches. Specifically, our focus is on general multiple-input multiple-output (MIMO) interference channels, where we propose an interference-robust semantic communication (IRSC) scheme. This scheme involves the development of transceivers based on neural networks (NNs), which integrate channel state information (CSI) either solely at the receiver or at both transmitter and receiver ends. Moreover, we establish a composite loss function for training IRSC transceivers, along with a dynamic mechanism for updating the weights of various components in the loss function to enhance system fairness among users. Experimental results demonstrate that the proposed IRSC scheme effectively learns to mitigate interference and outperforms baseline approaches, particularly in low signal-to-noise (SNR) regimes. © 2024, CC BY.

关键词： Channel state information