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Two-Player Incomplete Games of Resilient Multiagent systems

IFAC-PapersOnLine 2023年第2期56卷 258-263页

作者： Yurid E. Nugraha Tomohisa Hayakawa Hideaki Ishii Ahmet Cetinkaya Quanyan Zhu Department of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo 152-8552 Japan Department of Computer Science Tokyo Insitute of Technology Yokohama 226-8502 Japan Department of Functional Control Systems Shibaura Institute of Technology Tokyo 135-8548 Japan Department of Electrical and Computer Engineering New York University Brooklyn NY 11201 USA

Evolution of agents’ dynamics of multiagent systems under consensus protocol in the face of jamming attacks is discussed, where centralized parties are able to influence the control signals of the agents. In this paper we focus on a game-theoretical approach of multiagent systems where the players have incomplete information on their opponents’ strength. We consider repeated games with both simultaneous and sequential player actions where players update their beliefs of each other over time. The effect of the players’ optimal strategies according to Bayesian Nash Equilibrium and Perfect Bayesian Equilibrium on agents’ consensus is examined. It is shown that an attacker with incomplete knowledge may fail to prevent consensus despite having sufficient resources to do so.

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PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware Object Masking

PredictiveSLAM - Robust Visual SLAM Through Trajectory-Aware...

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International Conference on Unmanned Aircraft systems (ICUAS)

作者： Micha Heiß Jakob Grimm Hansen Dengyun Li Michał Kozłowski Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

This paper proposes PredictiveSLAM, a novel extension to ORB-SLAM2, which extracts features from specific regions of interest (ROI). The proposed method was designed with the risk posed both to humans and robotic systems in large-scale industrial sites in mind. The ROI are determined through an object detection network trained to detect moving human beings. The method detects and removes humans from feature extraction, predicting their potential future trajectory. This is done by omitting a specific ROI from extraction, deemed to be occluded in consecutive time steps. Two masking methods -static object and moving object trajectories - are proposed. This approach improves tracking accuracy and the performance of SLAM by removing the dynamic features from the reference for tracking and loop closures. The method is tested on data collected in a laboratory environment and compared against a state-of-the-art ground truth system. The validation data was collected from real-time experiments which aimed at simulating the typical human worker behaviours in industrial environments using an unmanned aerial vehicle (UAV). This study illustrates the advantages of the proposed method over earlier approaches, even with a highly dynamic camera setup on a UAV working in challenging environments.

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Designing a Robust control System Based on Sliding Mode control for Two-axis Gimbal systems

Designing a Robust Control System Based on Sliding Mode Cont...

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IEEE NW Russia Young Researchers in electrical and Electronic engineering Conference (EIConRusNW)

作者： Nguyen Viet Phuong Nguyen Trong Tuyen Nguyen Duy Khanh Nguyen Ngoc Hung Department of Aerospace Control Systems Academy of Military Science and Technology Hanoi Vietnam Department of Biomedical Engineering Le Quy Don Technical University Hanoi Vietnam Department of Automatic Control Systems Saint Petersburg State Electrotechnical University Hanoi Vietnam Department of Aerospace Control Systems Le Quy Don Technical University Hanoi Vietnam

Two-axis gimbals are used in stabilizing and controlling the aiming of optical systems. The main task is to isolate the angular motion of the payload and the disturbances affecting the optical axis of the camera while maintaining the camera’s orientation locked onto the target. Currently, two commonly used gimbal structures are the Yaw-Pitch type and the Roll-Swing type. In this paper, we delve into an in-depth study to develop the full mathematical models for both types, thereby analyzing and evaluating the influence of the cross-channel noise moment effects caused by gimbal imbalance on the stability and aiming control quality under complex angular motion conditions of the payload. Simulation results conducted using MATLAB/SIMULINK with the two-axis gimbal model demonstrate that employing a robust control system significantly reduces the impact of disturbances and enhances the quality of control and stability of the transmission system.

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FROST: Fusion and Multimodal 3D Reconstruction of Icy Surfaces for Robotic Exploration

FROST: Fusion and Multimodal 3D Reconstruction of Icy Surfac...

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Computational Intelligence on engineering/Cyber Physical systems Companion (CIES Companion), IEEE Symposium on

作者： Huy Xuan Pham Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (Air Lab) Aarhus University Aarhus C Denmark Automatic Control Group (RAT) Paderborn University Paderborn Germany

ISBN: (数字)9798331508494

ISBN: (纸本)9798331508500

Three-dimensional (3D) reconstruction serves as a cornerstone in various robotic applications, playing critical roles in scene understanding and navigation. Traditionally, LiDAR has been instrumental in generating precise point clouds of the environment, providing essential data for these applications. However, the efficacy of LiDAR sensors is significantly hindered in challenging conditions, such as the presence of water or icy surfaces. The complex interplay between laser beams and icy or non-ideal surfaces can result in signal degradation, distortion, or even complete signal loss, adversely affecting the accuracy and reliability of the 3D reconstruction process. The reflective and refractive properties of ice, along with its variable surface conditions, present challenges that traditional LiDAR sensors struggle to address. This paper proposes a diverse dataset to facilitate a multimodal approach for reconstructing icy surfaces using various sensors. A preliminary study based on this dataset demonstrates the feasibility of combining the geometric surface obtained from consecutive LiDAR scans with reconstructed meshes from visual cameras. The integration of distinct data sources has the potential to improve the robustness of reconstruction algorithms in diverse scenarios. The dataset can be downloaded at https://***/records/13862009.

关键词： Point cloud compression Surface reconstruction Laser radar Three-dimensional displays Navigation Ice Robustness Laser beams Image reconstruction Surface treatment

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Risk-Averse Learning with Delayed feedback

arXiv

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arXiv 2024年

作者： Wang, Siyi Wang, Zifan Johansson, Karl H. Hirche, Sandra Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

In real-world scenarios, the impacts of decisions may not manifest immediately. Taking these delays into account facilitates accurate assessment and management of risk in real-world environments, thereby ensuring the efficacy of strategies. In this paper, we investigate risk-averse learning using Conditional Value at Risk (CVaR) as risk measure, while incorporating delayed feedback with unknown but bounded delays. We develop two risk-averse learning algorithms that rely on one-point and two-point zeroth-order optimization approaches, respectively. The regret achieved by the algorithms is analyzed in terms of the cumulative delay and the number of total samplings. The results suggest that the two-point risk-averse learning achieves a smaller regret bound than the one-point algorithm. Furthermore, the one-point risk-averse learning algorithm attains sublinear regret under certain delay conditions, and the two-point risk-averse learning algorithm achieves sublinear regret with minimal restrictions on the delay. We provide numerical experiments on a dynamic pricing problem to demonstrate the performance of the proposed algorithms. © 2024, CC0.

关键词： Adversarial machine learning

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Efficient Home Energy Management using Load Approximation Models

Efficient Home Energy Management using Load Approximation Mo...

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control Conference (ICC), 2019 Sixth Indian

作者： Siddhant Vibhute Anupama Kowli Department of Systems and Control Engineering Indian Institute of Technology Bombay Mumbai India The Department of Electrical Engineering Indian Institute of Technology Bombay Mumbai India

Recent years have witnessed evolution of home energy management systems (HEMS) that allow end-use energy consumers to adjust their consumption in response to the needs of the wider energy infrastructure. In the context of electricity networks, this has lead to algorithm development for electricity cost reduction and grid support provision by load shifting or load curtailment or other load control. Such algorithms require models of appliance power profiles; a common approach is to approximate the power profile of an appliance by its rated or average power. However, the actual consumption patterns of any appliance may vary continuously during its operation. This paper presents a model that improves upon the existing state-of-art by introducing the notion of cycle of operation with power consumption varying across cycles. The paper also describes how such models can be incorporated into HEMS algorithms for cost-and-comfort optimization. Results demonstrating the reduction in cost and peak load as well as improvement in the total load profile are presented to demonstrate the usefulness of such representations.

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Nonlinear Analysis of Bifurcatory Properties of Mathematical Model of Subpopulation Dynamics in the Case of a Single Niche for Subpopulation 3

Nonlinear Analysis of Bifurcatory Properties of Mathematical...

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3rd IEEE International Conference on System Analysis and Intelligent Computing, SAIC 2022

作者： Kuzenkov, Olexandr Busher, Victor Chornyi, Oleksii Nikolenko, Anatoliy Kuznetsov, Vitaliy Savvin, Oleksandr Oles Honchar Dnipro National University The Department of Calculating Mathematics and Mathematical Cybernetics Dnipro Ukraine National University 'Odessa Maritime Academy' Department of Electrical Engineering and Electronics Odessa Ukraine Institute of Electromechanics Energy Saving and Automatic Control Systems Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Ukrainian State University of Science and Technologies Department of the Electrical Engineering Dnipro Ukraine Ukrainian State University of Science and Technologies Department of Ecology Heat Engineering Occupational Safety and Health Dnipro Ukraine

ISBN: (数字)9798350396744

ISBN: (纸本)9798350396744

The article is devoted to the use of mathematical models of the dynamics of heterogeneous populations, and computer simulation based on the above models allows to identify general trends in subpopulations, predict the state of the system and obtain results on possible consequences of artificial intervention. Also, the use of mathematical models can predict the spread of genetic anomalies. The authors propose a model of subpopulation dynamics with a logistic function as a basic one. It is concluded that the system-wide dynamics of subpopulation processes depends not only on the reproductive potential of subpopulations, but also on the intrasystemic dynamics that objectively occur in such systems. The adequacy of the proposed mathematical model is proved. © 2022 IEEE.

关键词： Dynamics

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CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival Prediction for Mobile Robots

CAMETA: Conflict-Aware Multi-Agent Estimated Time of Arrival...

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

作者： Jonas le Fevre Sejersen Erdal Kayacan Department of Electrical and Computer Engineering Artificial Intelligence in Robotics Laboratory (AiR Lab) Aarhus University Aarhus C Denmark Department of Electrical Engineering and Information Technology Automatic Control Group Paderborn University Paderborn Germany

This study presents the conflict-aware multi-agent estimated time of arrival (CAMETA) framework, a novel approach for predicting the arrival times of multiple agents in unstructured environments without predefined road infrastructure. The CAMETA framework consists of three components: a path planning layer generating potential path suggestions, a multi-agent ETA prediction layer predicting the arrival times for all agents based on the paths, and lastly, a path selection layer that calculates the accumulated cost and selects the best path. The novelty of the CAMETA framework lies in the heterogeneous map representation and the heterogeneous graph neural network architecture. As a result of the proposed novel structure, CAMETA improves the generalization capability compared to the state-of-the-art methods that rely on structured road infrastructure and historical data. The simulation results demonstrate the efficiency and efficacy of the multi-agent ETA prediction layer, with a mean average percentage error improvement of 29.5% and 44% when compared to a traditional path planning method (A *) which does not consider conflicts. The performance of the CAMETA framework shows significant improvements in terms of robustness to noise and conflicts as well as determining proficient routes compared to state-of-the-art multi-agent path planners.

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Asymmetry of the Relative Entropy in the Regularization of Empirical Risk Minimization

arXiv

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arXiv 2024年

作者： Daunas, Francisco Esnaola, Iñaki Perlaza, Samir M. Vincent Poor, H. The Department of Automatic Control & Systems Engineering The University of Sheffield SheffieldS1 3JD United Kingdom INRIA Centre Inria d'Université Côte d'Azur Sophia Antipolis06902 France The Department of Automatic Control and Systems Engineering The University of Sheffield SheffieldS1 3JD United Kingdom The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States The GAATI Mathematics Laboratory University of French Polynesia Fa'a'ā98702 French Polynesia

The effect of relative entropy asymmetry is analyzed in the context of empirical risk minimization (ERM) with relative entropy regularization (ERM-RER). Two regularizations are considered: (a) the relative entropy of the measure to be optimized with respect to a reference measure (Type-I ERM-RER);or (b) the relative entropy of the reference measure with respect to the measure to be optimized (Type-II ERM-RER). The main result is the characterization of the solution to the Type-II ERM-RER problem and its key properties. By comparing the well-understood Type-I ERM-RER with Type-II ERM-RER, the effects of entropy asymmetry are highlighted. The analysis shows that in both cases, regularization by relative entropy forces the solution's support to collapse into the support of the reference measure, introducing a strong inductive bias that can overshadow the evidence provided by the training data. Finally, it is shown that Type-II regularization is equivalent to Type-I regularization with an appropriate transformation of the empirical risk function. © 2024, CC BY.

关键词： Risk assessment

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A Resilient Consensus-Based Energy 5.0 Framework for Micro-Grids Under Ramp-Rate Constraints and Stochastic FDI Attacks

A Resilient Consensus-Based Energy 5.0 Framework for Micro-G...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Ijaz Ahmed Abdul Basit Muhammad Rehan Amjad Ali Muhammad Maaruf Muhammad Khalid Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences (PIEAS) Islamabad Pakistan IRC for Sustainable Energy Systems King Fahd University of Petroleum & Minerals (KFUPM) Saudi Arabia Control & Instrumentation Engineering Department KFUPM Dhahran Saudi Arabia Electrical Engineering Department KFUPM Dhahran Saudi Arabia

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Distributed power allocation (DPA) in micro-grids (MGs) has a significant potential for attaining a cost effective and reliable solution of power dispatch. Considering the significant risks posed by cyber-attacks and system limitations to the MGs, it is important to develop resilient methods for an efficient DPA, which is valuable both in terms of theoretical understanding and practical application for energy 5.0 framework. This work addresses the DPA problem in MGs, specifically focusing on the economic dispatch (ED) for the generation side while taking into account ramp-rate limits (RRLs) and stochastic false-data injection (FDI) attacks. A consensus-based protocol has been developed by considering the information of the incremental costs of neighbouring generators and local power mismatch for a generation facility. A stochastic FDI attack at the shared information over the network has been accounted. The proposed technique guarantees robust (and resilient) convergence by providing a sufficient criterion under RRLs and stochastic FDI attacks. The proposed approach is based of the distributed ED framework, and convergence analysis has been performed via two Lyapunov functions. The convergence of total power mismatch and error in synchronization of incremental costs to bounded sets are ensured by the proposed approach. The presented framework achieves an efficient energy production cost. The network's robustness and resilience for the shared information of incremental cost of energy production units as the consensus variable are accounted. The simulation study for a set of five generators under stochastic FDI attacks and RRLs are provided which confirm the theoretical findings of the proposed scheme.

关键词： Costs Protocols Uncertainty Stochastic processes Production Robustness Generators

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