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11th International Conference on Learning Representations, ICLR 2023

作者： Beintema, Gerben I. Schoukens, Maarten Tóth, Roland Department of Electrical Engineering Eindhoven University of Technology Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models. However, even with numerous recent developments, the CT nonlinear state-space (NL-SS) model identification problem remains to be solved in full, considering common experimental aspects such as the presence of external inputs, measurement noise, latent states, and general robustness. This paper presents a novel estimation method that addresses all these aspects and that can obtain state-of-the-art results on multiple benchmarks with compact fully connected neural networks capturing the CT dynamics. The proposed estimation method called the subspace encoder approach (SUBNET) ascertains these results by efficiently approximating the complete simulation loss by evaluating short simulations on subsections of the data, by using an encoder function to estimate the initial state for each subsection and a novel state-derivative normalization to ensure stability and good numerical conditioning of the training process. We prove that the use of subsections increases cost function smoothness together with the necessary requirements for the existence of the encoder function and we show that the proposed state-derivative normalization is essential for reliable estimation of CT NL-SS models. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Cost functions

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Multiagent Consensus over Time-Invariant and Time-Varying Signed Digraphs via Eventual Positivity

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IEEE Transactions on automatic control 2023年第9期68卷 5429-5444页

作者： Fontan, Angela Wang, Lingfei Hong, Yiguang Shi, Guodong Altafini, Claudio Linköping University Division of Automatic Control Department of Electrical Engineering LinköpingSE-58183 Sweden KTH Royal Institute of Technology Division of Decision and Control Systems StockholmSE-10044 Sweden Chinese Academy of Sciences Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing100190 China University of Chinese Academy of Sciences Beijing100190 China Tongji University Department of Control Science and Engineering Shanghai201804 China University of Sydney Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering SydneyNSW2008 Australia

Laplacian dynamics on signed digraphs have a richer behavior than those on nonnegative digraphs. In particular, for the so-called 'repelling' signed Laplacians, the marginal stability property (needed to achieve consensus) is not guaranteed a priori and, even when it holds, it does not automatically lead to consensus, as these signed Laplacians may lose rank even in strongly connected digraphs. Furthermore, in the time-varying case, instability can occur even when switching in a family of systems each of which corresponds to a marginally stable signed Laplacian with the correct corank. In this article, we present novel conditions for achieving consensus on signed digraphs based on the property of eventual positivity, a Perron-Frobenius (PF) type of property for signed matrices. The conditions we develop cover both time-invariant and time-varying cases. A particularly simple sufficient condition, valid in both cases, is that the Laplacians are normal matrices. Such condition can be relaxed in several ways. For instance, in the time-invariant case it is enough that the Laplacian has this PF property on the right side, but not on the left side (i.e., on the transpose). For the time-varying case, convergence to consensus can be guaranteed by the existence of a common Lyapunov function for all the signed Laplacians. All conditions can be easily extended to bipartite consensus. © 1963-2012 IEEE.

关键词： Lyapunov functions

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Uncertainty-Aware Decision-Making and Planning for Autonomous Forced Merging 63

Uncertainty-Aware Decision-Making and Planning for Autonomou...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Zhou, Jian Gao, Yulong Olofsson, Bjorn Frisk, Erik Imperial College London Department of Electrical and Electronic Engineering London United Kingdom Linköping University Division of Vehicular Systems Department of Electrical Engineering Linköping Sweden Lund University Department of Automatic Control Lund Sweden

ISBN: (纸本)9798350316339

In this paper, we develop an uncertainty-aware decision-making and motion-planning method for an autonomous ego vehicle in forced merging scenarios, considering the motion uncertainty of surrounding vehicles. The method dynamically captures the uncertainty of surrounding vehicles by online estimation of their acceleration bounds, enabling a reactive but rapid understanding of the uncertainty characteristics of the surrounding vehicles. By leveraging these estimated bounds, a non-conservative forward occupancy of surrounding vehicles is predicted over a horizon, which is incorporated in both the decision-making process and the motion-planning strategy, to enhance the resilience and safety of the planned reference trajectory. The method successfully fulfills the tasks in challenging forced merging scenarios, and the properties are illustrated by comparison with several alternative approaches. © 2024 IEEE.

关键词： Autonomous vehicles

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Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

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IEEE/CAA Journal of automatica Sinica 2024年第1期11卷 231-239页

作者： Yahui Liu Bin Tian Yisheng Lv Lingxi Li Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 IEEE the Transportation and Autonomous Systems Institute(TASI)and the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis(IUPUI)Indianapolis 46202 USA

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://***/yahuiliu99/PointC onT.

关键词： Content-based Transformer deep learning feature aggregator local attention point cloud classification

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Game theory system optimization in a supplier-customer transport network 10

Game theory system optimization in a supplier-customer trans...

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10th International Conference on Information Technology and Quantitative Management, ITQM 2023

作者： Bilbiie, Radu Popescu, Dumitru Dimon, Catalin Teme, Marius Borne, Pierre Department of Automatic Control and Systems Engineering University Politehnica of Bucharest Bucharest Romania Ecole Centrale de Lille Villeneuve d'Ascq France

The development of modern IT technologies and resources, offer in many economic-industrial applications, efficient solutions for planning and managing of specific activities. New research directions in the field of management and optimization of supply-customer (SC) systems are defined and evaluated. The paper proposes a management model based on a causal graph structure, which expresses the connection and interdependence between the two entities involved in the specific activities of the field, the suppliers of goods and services and the consumers of these goods and products. Information related to production and storage capacity, up-to-date requirements for customer needs, delivery planning, production and consumption dynamics are considered. The transport between the actors involved in the system is an important component with weight in the costs of supply-sale activities. The optimal solution for the supply-customer transport system is proposed based on different strategies of game theory. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Tenth International Conference on Information Technology and Quantitative Management.

关键词： Game theory

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Automated and controlled patch generation for enhanced fixing of communication software vulnerabilities

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Intelligent and Converged Networks 2024年第3期5卷 222-236页

作者： Shuo Feng Shuai Yuan Zhitao Guan Xiaojiang Du School of Control and Computer Engineering North China Electric Power UniversityBeijing 100029China Department of Finance Operationsand Information SystemsBrock UniversitySt.Catharines L2S3A1Canada Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA

Software is a crucial component in the communication systems,and its security is of paramount ***,it is susceptible to different types of attacks due to potential ***,significant time and effort is required to fix such *** propose an automated program repair method based on controlled text generation ***,we utilize a fine-tuned language model for patch generation and introduce a discriminator to evaluate the generation process,selecting results that contribute most to vulnerability ***,we perform static syntax analysis to expedite the patch verification *** effectiveness of the proposed approach is validated using QuixBugs and Defects4J datasets,demonstrating significant improvements in generating correct patches compared to other existing methods.

关键词： automatic program repair controlled text generation communication software security program language model

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Guidance of Agricultural Ground Robots Team with an Aerial Vehicle: A Cost-Effective Solution 56

Guidance of Agricultural Ground Robots Team with an Aerial V...

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56th International Symposium on Robotics, ISR Europe 2023

作者： Ugurlu, Halil Ibrahim Bardakci, Deniz Pham, Huy Xuan Kayacan, Erdal The Department of Electrical and Computer Engineering Aarhus University Aarhus C8000 Denmark Automatic Control Group Department of Electrical Engineering and Information Technology Paderborn University Paderborn Germany

ISBN: (纸本)9783800761418

Increasing the operational efficiency of agricultural machines is essential by the use of artificial intelligence (AI)-based navigation, planning, and control algorithms to handle the increasing demand for food production without compromising sustainability. In this study, an end-to-end path planning algorithm (AgroRL) is proposed for aerial-ground robots team collaboration. In the proposed solution, while main operations in the field are handled by the ground vehicle, the aerial robot is responsible for re-planning a collision-free trajectory for the ground robot when the robot faces an obstacle. Deep reinforcement learning is used for training the end-to-end policy for local re-planning of the aerial robot. The agent, informed by the global trajectory, generates local plans based on depth images. Variational autoencoders are also investigated for dimension reduction of the depth images in obstacle avoidance context to speed up deep reinforcement learning and alleviate the computational complexity of the policy network. The agriculture environment is developed in the Webots open-source robot simulator for training and testing purposes. The efficiency and efficacy of the end-to-end planner are evaluated over a number of cluttered field scenarios. The simulation experiments demonstrate a single aerial vehicle guiding multiple ground robots in agricultural operations. © VDE VERLAG GMBH Berlin Offenbach.

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Structured Observer Synthesis via Static Output Feedback

Structured Observer Synthesis via Static Output Feedback

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UKACC International Conference on control (control)

作者： Hadriano Morales Escamilla Paul Trodden Department of Automatic Control and Systems Engineering (ACSE) The University of Sheffield UK

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

This paper considers the problem of structured observer synthesis for linear-time-invariant (LTI) systems. The importance of the problem is demonstrated through two motivational examples: utilizing high-fidelity black-box models as part of observers, and anti-windup gain design. The problem is connected with its control counterpart: static output feedback, and the process is illustrated through the design of an anti-windup gain for a multi-variable PID controller. A numerical example shows how the resulting closed-loop system converges faster than a recent method in the literature, with the added benefit of solving the problem with a non-iterative method, which decreases the computational effort.

关键词： Linear systems control design Computational modeling Closed box Process control Observers Numerical models

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Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI)

Sparse Identification of Nonlinear Dynamics with Side Inform...

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American control Conference (ACC)

作者： Gabriel F. Machado Morgan Jones Department of Automatic Control and Systems Engineering The University of Sheffield Sheffield UK

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Modern societies have an abundance of data yet good system models are rare. Unfortunately, many of the current system identification and machine learning techniques fail to generalize outside of the training set, producing models that violate basic physical laws. This work proposes a novel method for the Sparse Identification of Nonlinear Dynamics with Side Information (SINDy-SI). SINDy-SI is an iterative method that uses Sum-of-Squares (SOS) programming to learn optimally fitted models while guaranteeing that the learned model satisfies side information, such as symmetries and physical laws. Guided by the principle of Occam's razor, that the simplest or most regularized best fitted model is typically the superior choice, during each iteration SINDy-SI prunes the basis functions associated with small coefficients, yielding a sparse dynamical model upon termination. Through several numerical experiments we will show how the combination of side information constraints and sparse polynomial representation cultivates dynamical models that obey known physical laws while displaying impressive generalized performance beyond the training set.

关键词： Training Programming Numerical simulation Data models Polynomials Numerical models System identification

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Decentralized Game-based Auction Algorithm for Scheduling Robotic Chargers to Service EVs with Uncertain Demands

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年 1-14页

作者： Fang, Qiuyang Zhang, Jianlei Wang, Chen Xie, Guangming Zhang, Chunyan Department of Automation College of Computer and Control Engineering Nankai University Tianjin China National Engineering Research Center of Software Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China

In this study, we introduce a novel auction-based algorithm modeled as a decentralized coalition formation game, designed for the complex requirements of large-scale multi-robot task allocation under uncertain demand. This context is particularly illustrative in scenarios where robots are tasked to charge electric vehicles. The algorithm begins by partitioning a composite task sequence into distinct subsets based on spatial similarity principles. Subsequently, we employ a coalition formation game paradigm to coordinate the assembly of robots into cooperative coalitions focused on these distinct subsets. To mitigate the impact of unpredictable task demands on allocations, our approach utilizes the conditional value-at-risk to assess the risk associated with task execution, along with computing the potential revenue of the coalition with an emphasis on risk-related outcomes. Additionally, integrating consensus auctions into the coalition formation framework allows our approach to accommodate assignments for individual robot-task pairings, thus preserving the stability of individual robotic decision autonomy within the coalition structure and assignment distribution. Simulative analyses on a prototypical parking facility layout confirm that our algorithm achieves Nash equilibrium within the coalition structure in polynomial time and demonstrates significant scalability. Compared to competing algorithms, our proposal exhibits superior performance in resilience, task execution efficiency, and reduced overall task completion times. The results demonstrate that our approach is an effective strategy for solving the scheduling challenges encountered by multi-robot systems operating in complex environments. IEEE

关键词： Polynomial approximation

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