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Safe Distributed control of Multi-Robot systems With Communication Delays

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IEEE Transactions on Vehicular technology 2025年

作者： Ballotta, Luca Talak, Rajat Delft University of Technology Delft Center for Systems and Control Delft2628 CD Netherlands Massachusetts Institute of Technology Laboratory for Information and Decision Systems CambridgeMA02139 United States

Safe operation of multi-robot systems is critical, especially in communication-degraded environments such as underwater for seabed mapping, underground caves for navigation, and in extraterrestrial missions for assembly and construction. We address safety of networked autonomous systems where the information exchanged between robots incurs communication delays. We formalize a notion of distributed control barrier function for multi-robot systems, a safety certificate amenable to a distributed implementation, which provides formal ground to using graph neural networks to learn safe distributed controllers. Further, we observe that learning a distributed controller ignoring delays can severely degrade safety. We finally propose a predictor-based framework to train a safe distributed controller under communication delays, where the current state of nearby robots is predicted from received data and age-of-information. Numerical experiments on multi-robot collision avoidance show that our predictor-based approach can significantly improve the safety of a learned distributed controller under communication delays. A video abstract is available at https://***/Hcu1Ri32Spk. © 1967-2012 IEEE.

关键词： Multipurpose robots

Reinforcement Learning for Adaptive Planner Parameter Tuning: A Perspective on Hierarchical Architecture

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arXiv 2025年

作者： Lu, Wangtao Wei, Yufei Xu, Jiadong Jia, Wenhao Li, Liang Xiong, Rong Wang, Yue State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou China College of Information and Engineering Zhejiang University of Technology Hangzhou310023 China

automatic parameter tuning methods for planning algorithms, which integrate pipeline approaches with learning-based techniques, are regarded as promising due to their stability and capability to handle highly constrained environments. While existing parameter tuning methods have demonstrated considerable success, further performance improvements require a more structured approach. In this paper, we propose a hierarchical architecture for reinforcement learning-based parameter tuning. The architecture introduces a hierarchical structure with low-frequency parameter tuning, mid-frequency planning, and high-frequency control, enabling concurrent enhancement of both upper-layer parameter tuning and lower-layer control through iterative training. Experimental evaluations in both simulated and real-world environments show that our method surpasses existing parameter tuning approaches. Furthermore, our approach achieves first place in the Benchmark for Autonomous Robot Navigation (BARN) Challenge. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

Accelerated Distributed Aggregative Optimization

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IEEE Transactions on automatic control 2025年

作者： Liu, Jiaxu Chen, Song Cai, Shengze Xu, Chao Chu, Jian Zhejiang University School of Mathematical Sciences Zhejiang Hangzhou310027 China Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Zhejiang Hangzhou310027 China

This paper delves into the investigation of a distributed aggregative optimization problem within a network. In this scenario, each agent possesses its own local cost function, which relies not only on the local state variable but also on an aggregated function of state variables from all agents. To expedite the optimization process, we amalgamate the heavy ball and Nesterov's accelerated method with distributed aggregative gradient tracking, resulting in the proposal of two innovative algorithms, aimed at resolving the distributed aggregative optimization problem. Our analysis demonstrates that the proposed algorithms can converge to an optimal solution at a global linear convergence rate when the objective function is strongly convex with the Lipschitz-continuous gradient, and when the parameters (e.g., step size and momentum coefficients) are chosen within specific ranges. Additionally, we present several numerical experiments to verify the effectiveness, robustness and superiority of our proposed algorithms. © 2025 IEEE.

关键词： Optimization algorithms

Discrete-time Two-Layered Forgetting RLS Identification under Finite Excitation

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arXiv 2025年

作者： Tsuruhara, Satoshi Ito, Kazuhisa Graduate School of Engineering and Science Functional Control Systems Shibaura Institute of Technology JSPS Research 307 Fukasaku Minuma Saitama3378570 Japan Department of Machinery and Control Systems Shibaura Institute of Technology 307 Fukasaku Minuma Saitama3378570 Japan

In recent years, adaptive identification methods that can achieve the true value convergence of parameters without requiring persistent excitation (PE) have been widely studied, and concurrent learning has been intensively studied. However, the parameter convergence rate is limited for the gradient-based method owing to small parameter update gain, and even the introduction of forgetting factors does not work sufficiently. To address this problem, this study proposes a novel discrete-time recursive least squares method under finite excitation (FE) conditions using two forgetting factors (inner and outer) and an augmented regressor matrix comprising a sum of regressor vectors. The proposed method ensures the PE condition of the augmented regressor matrix under FE conditions of the regressor vector and allows the properly design of the forgetting factor without estimator windup and/or destabilization of the system. Numerical simulations demonstrate its effectiveness by comparing it with several conventional methods. © 2025, CC BY-NC-ND.

关键词： Channel estimation

Distributed Design of Ultra Large-Scale control systems: Progress, Challenges, and Prospects

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arXiv 2025年

作者： Pedroso, Leonardo Batista, Pedro Heemels, W.P.M.H. Control Systems Technology Section Eindhoven University of Technology Netherlands Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Portugal

The transition from large centralized complex control systems to distributed configurations that rely on a network of a very large number of interconnected simpler subsystems is ongoing and inevitable in many applications. It is attributed to the quest for resilience, flexibility, and scalability in a multitude of engineering fields with far-reaching societal impact. Although many design methods for distributed and decentralized control systems are available, most of them rely on a centralized design procedure requiring some form of global information of the whole system. Clearly, beyond a certain scale of the network, these centralized design procedures for distributed controllers are no longer feasible and we refer to the corresponding systems as ultra large-scale systems (ULSS). For these ULSS, design algorithms are needed that are distributed themselves among the subsystems and are subject to stringent requirements regarding communication, computation, and memory usage of each subsystem. In this paper, a set of requirements is provided that assures a feasible real-time implementation of all phases of a control solution on an ultra large scale. State-of-the-art approaches are reviewed in the light of these requirements and the challenges hampering the development of befitting control algorithms are pinpointed. Comparing the challenges with the current progress leads to the identification and motivation of promising research directions. © 2025, CC BY-NC-ND.

关键词： Decentralized control

REGULARIZED HIGHER-ORDER TAYLOR APPROXIMATION METHODS FOR COMPOSITE NONLINEAR LEAST-SQUARES

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arXiv 2025年

作者： Nabou, Yassine Necoara, Ion Automatic Control and Systems Engineering Department National University of Science and Technology Politehnica Bucharest Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics The Romanian Academy Bucharest Romania

In this paper, we develop a regularized higher-order Taylor based method for solving composite (e.g., nonlinear least-squares) problems. At each iteration, we replace each smooth component of the objective function by a higher-order Taylor approximation with an appropriate regularization, leading to a regularized higher-order Taylor approximation (RHOTA) algorithm. We derive global convergence guarantees for RHOTA algorithm. In particular, we prove stationary point convergence guarantees for the iterates generated by RHOTA, and leveraging a Kurdyka-Lojasiewicz (KL) type property of the objective function, we derive improved rates depending on the KL parameter. When the Taylor approximation is of order 2, we present an efficient implementation of RHOTA algorithm, demonstrating that the resulting nonconvex subproblem can be effectively solved utilizing standard convex programming tools. Furthermore, we extend the scope of our investigation to include the behavior and efficacy of RHOTA algorithm in handling systems of nonlinear equations and optimization problems with nonlinear equality constraints deriving new rates under improved constraint qualifications conditions. Finally, we consider solving the phase retrieval problem with a higher-order proximal point algorithm, showcasing its rapid convergence rate for this particular application. Numerical simulations on phase retrieval and output feedback control problems also demonstrate the efficacy and performance of the proposed methods when compared to some state-of-the-art optimization methods and software. Copyright © 2025, The Authors. All rights reserved.

关键词： Feedback control

An Improved DeeplabV3+ Tile Image Positioning Method

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An Improved DeeplabV3+ Tile Image Positioning Method

2024 International Conference Optoelectronic Information and Optical Engineering, OIOE 2024

作者： Li, Xiaoyang Wang, Rui Tong, Xing Niu, Lianqiang Shenyang University of Technology Shenyang China Key Laboratory of Network Control Systems Chinese Academy of Sciences China Shenyang Institute of Automation Chinese Academy of Sciences China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (数字)9781510688209

ISBN: (纸本)9781510688193

In order to improve the detection accuracy and efficiency of surface defects on ceramic tiles, a detection model based on DeepLabV3+and combined with MobileNetv2 for feature extraction was constructed. Using ECA attention mechanism and multi branch receptive field cascade fusion structure to expand the sampling range of global and local element features for the empty space pyramid pooling module ASPP of the original model. Meanwhile, the model decoder uses depthwise separable convolutions instead of the original standard convolutions. Experimental results have shown that the improved model has an MPA value of 96.12%, a MIou value of 97.29%, an F1 value of 90.03%, and an Accuracy value of 99.89%. Compared with the previous model, the improved model's indicators have increased by 4.07%, 4.44%, 2.04%, and 1.02%, respectively. The segmentation accuracy and detection speed have been significantly improved. The experimental results fully demonstrate that the algorithm proposed in this article can effectively complete the task of detecting surface defects on ceramic tiles. © 2025 SPIE.

关键词： Image segmentation

The Fredholm Determinants Approach to the Computations of Quantum Entanglement 15th

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The Fredholm Determinants Approach to the Computations of...

15th International Conference on Parallel Processing and Applied Mathematics, PPAM 2024

作者： Gielerak, Roman Wiśniewska, Joanna Sawerwain, Marek Institute of Control and Computation Engineering University of Zielona Góra Licealna 9 Zielona Góra65-417 Poland Institute of Information Systems Faculty of Cybernetics Military University of Technology Gen. S. Kaliskiego 2 Warszaw00-908 Poland

ISBN: (纸本)9783031856990

The paper discusses several mathematical issues related to the numerical calculation of entanglement amount present in states describing bipartite quantum systems. The novel aspect of the presented material is the use of the Fredholm determinants theory methods for computing the amount of entanglement through the use of several, entropy-based entanglement measures. In particular, the continuous variable systems case [30, 31] is covered properly by the obtained here results, including presentation of a suitable renormalisation procedure of extracting the finite part of entanglement (often arises a situation in which the value of the corresponding entropy is infinite). Particular attention has been paid to the application of so-called unified entropy variants known as Rényi, resp. Tsallis entropies in the region of parameter α values in the interval (0, 1), where, in the case of infinitely dimensional systems, the infinite value of the corresponding unified entropy is very typical, even for frequently encountered quantum states. A library of functions supporting entropies and the corresponding entanglement calculations are also presented as an extension of the constructed by us EntDetector package for the Python language platform. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Quantum entanglement

Modeling and Analysis of Train control Center Simulation System Based on CPN

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Modeling and Analysis of Train Control Center Simulation Sys...

Electrical Automation and Artificial Intelligence (ICEAAI), International Conference on

作者： Sheng Luo Tao He Key Laboratory of Optoelectronics and Intelligent Control Ministry of Education Lanzhou Jiaotong University Lanzhou Gansu China Lanzhou Jiaotong University Institute of Automatic Control Gansu Provincial Rail Transit Signal and Control Evaluation Industry Technology Center Lanzhou Gansu China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Train control Center (TCC) is the hub of the information interaction of the column control system. It is the core system that ensures the safety of railway driving and ensure transportation efficiency. The traditional manual test method has many problems, such as high test cost, long time-consuming and low efficiency, and great influence of human subjective factors. This paper uses computer simulation technology to build a simulation test system. By analyzing the information interaction between the column control center and other devices, the computer simulation technology is used to build the simulation test system simulation control system operation. This system can intuitively reflect the performance of the column control system, which is convenient to optimize and adjust the design scheme, make theoretical guidance for the establishment of the real control system. According to the functional test requirements of TCC, this paper uses Colored Petri Net (CPN) to establish a hierarchical model for the simulation test system of the train control center. Taking the temporary speed limit scene test as an example, the formal modeling of the simulation test system through CPN can avoid the ambiguity caused by the specification description, improve the timeliness and safety of the system, help to find the problems that may occur in the design specification, manufacturing and operation of the system, and provide some reference for the design of the real train control system.

关键词： Computer simulation Petri nets Process control Transportation control systems Rail transportation Safety systems simulation Security Software development management