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Distributed Online Convex Optimization with Time-Varying Constraints: Tighter Cumulative Constraint Violation Bounds under Slater's Condition

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IEEE Transactions on Automatic control 2025年

作者： Yi, Xinlei Li, Xiuxian Yang, Tao Xie, Lihua Hong, Yiguang Chai, Tianyou Johansson, Karl H. National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology China Ministry of Education Frontiers Science Center for Intelligent Autonomous Systems China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Nanyang Technological University School of Electrical and Electronic Engineering 50 Nanyang Avenue 639798 Singapore Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online convex optimization with time-varying constraints. In this setting, a network of agents makes decisions at each round, and then only a portion of the loss function and a coordinate block of the constraint function are privately revealed to each agent. The loss and constraint functions are convex and can vary arbitrarily across rounds. The agents collaborate to minimize static network regret and network cumulative constraint violation. A novel distributed online algorithm with a vanishing stepsize is proposed and it achieves an O(Tmax {c,1-c}) static network regret bound and an O(T1-c/2) network cumulative constraint violation bound, where T is the number of rounds and c∈ (0,1) is a user-defined trade-off parameter. When Slater's condition holds (i.e, there is a point that strictly satisfies the inequality constraints), the network cumulative constraint violation bound is reduced to O(T1-c). Moreover, if the loss functions are strongly convex, then static network regret bound is reduced to O(log (T)), and the network cumulative constraint violation bound is reduced to O(√log (T)T) and O(log (T)) without and with Slater's condition, respectively. To the best of our knowledge, this paper is the first to achieve tighter (network) cumulative constraint violation bounds for (distributed) online convex optimization with time-varying constraints under Slater's condition. Finally, the theoretical results are verified through numerical simulations. © 1963-2012 IEEE.

关键词： Convex optimization

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Towards Open-Source and Modular Space systems with ATMOS

arXiv

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arXiv 2025年

作者： Roque, Pedro Phodapol, Sujet Krantz, Elias Lim, Jaeyoung Verhagen, Joris Jiang, Frank J. Dörner, David Mao, Huina Tibert, Gunnar Siegwart, Roland Stenius, Ivan Tumova, Jana Fuglesang, Christer Dimarogonas, Dimos V. Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden School of Engineering Sciences KTH Royal Institute of Technology Stockholm Sweden Autonomous Systems Laboratory ETH Zürich Zürich Switzerland Division of Robotics Perception and Learning KTH Royal Institute of Technology Stockholm Sweden

In the near future, autonomous space systems will compose many of the deployed spacecraft. Their tasks will involve autonomous rendezvous and proximity operations with large structures, such as inspections, assembly, and maintenance of orbiting space stations, as well as human-assistance tasks over shared workspaces. To promote replicable and reliable scientific results for autonomous control of spacecraft, we present the design of a space robotics laboratory based on open-source and modular software and hardware. The simulation software provides a software-in-the-loop architecture that seamlessly transfers simulated results to the hardware. Our results provide an insight into such a system, including comparisons of hardware and software results, as well as control and planning methodologies for controlling free-flying platforms. Copyright © 2025, The Authors. All rights reserved.

关键词： Space stations

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Distributed Event-Triggered Bandit Convex Optimization With Time-Varying Constraints

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IEEE Transactions on control of Network systems 2025年

作者： Zhang, Kunpeng Yi, Xinlei Wen, Guanghui Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China Southeast University School of Automation Nanjing210096 China University of Groningen Engineering and Technology Institute Groningen Faculty of Science and Engineering GroningenAG 9747 Netherlands KTH Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm10044 Sweden KTH Royal Institute of Technology Digital Futures Stockholm10044 Sweden

This paper considers the distributed bandit convex optimization problem with time-varying inequality constraints over a network of agents, where the goal is to minimize network regret and cumulative constraint violation. Existing distributed online algorithms solving this problem require that each agent broadcasts its decision to its neighbors at each iteration. However, communication resources are often limited. To better utilize communication resources, we propose a distributed event-triggered online primal-dual algorithm with two-point bandit feedback. Under several classes of appropriately chosen decreasing parameter sequences and non-increasing event-triggered threshold sequences, we establish dynamic network regret and network cumulative constraint violation bounds. These bounds are comparable to the results achieved by distributed event-triggered online algorithms with full-information feedback. Finally, a numerical example is provided to verify the theoretical results. © 2014 IEEE.

关键词： Optimization algorithms

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Distributed Constrained Online Nonconvex Optimization with Compressed Communication

arXiv

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arXiv 2025年

作者： Zhang, Kunpeng Xu, Lei Yi, Xinlei Cao, Ming Johansson, Karl H. Chai, Tianyou Yang, Tao State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai201210 China Engineering and Technology Institute Groningen Faculty of Science and Engineering University of Groningen GroningenAG 9747 Netherlands Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Digital Futures Stockholm10044 Sweden

This paper considers distributed online nonconvex optimization with time-varying inequality constraints over a network of agents. For a time-varying graph, we propose a distributed online primal–dual algorithm with compressed communication to efficiently utilize communication resources. We show that the proposed algorithm establishes an O(T max{1−θ1,θ1}) network regret bound and an O(T 1−θ1/2) network cumulative constraint violation bound, where T is the number of iterations and θ1 ∈ (0, 1) is a user-defined trade-off parameter. When Slater’s condition holds (i.e, there is a point that strictly satisfies the inequality constraints at all iterations), the network cumulative constraint violation bound is reduced to O(T 1−θ1). These bounds are comparable to the state-of-the-art results established by existing distributed online algorithms with perfect communication for distributed online convex optimization with (time-varying) inequality constraints. Finally, a simulation example is presented to validate the theoretical results. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Ensemble bayesian decision making with redundant deep perceptual control policies 18

Ensemble bayesian decision making with redundant deep percep...

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18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

作者： Lee, Keuntaek Wang, Ziyi Vlahov, Bogdan Brar, Harleen Theodorou, Evangelos A. Autonomous Control and Decision Systems Laboratory Georgia Institute of Technology AtlantaGA30332 United States

ISBN: (纸本)9781728145495

This work presents a novel ensemble of Bayesian Neural Networks for control of safety-critical systems. decision making for safety-critical systems is challenging due to performance requirements with significant consequences in the event of failure. In practice, failure of such systems can be avoided by introducing redundancies of control. Neural Networks are generally not used for safety-critical systems as they can behave in unexpected ways in response to novel inputs. In addition, there may not be any indication as to when they will fail. BNNs have been recognized for their ability to produce not only viable outputs but also provide a measure of uncertainty in these outputs. This work combines the knowledge of prediction uncertainty obtained from BNNs and ensemble control for a redundant control methodology. Our technique is applied to an agile autonomous driving task. Multiple BNNs are trained to control a vehicle in an end-to-end fashion on different sensor inputs provided by the system. We show that an individual network is successful in maneuvering around the track but crashes in the presence of unforeseen input noise. Our proposed ensemble of BNNs shows successful task performance even in the event of multiple sensor failures. © 2019 IEEE.

关键词： decision making

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control augmentation system (CAS) synthesis via adaptation & learning

Control augmentation system (CAS) synthesis via adaptation &...

引用

Guidance, Navigation and control Conference, 1993

作者： Millington, Peter J. Baker, Walter L. Koenig, Mark A. Autonomous Systems Group Control & Decision Systems Division The Charles Stark Draper Laboratory Inc. CambridgeMA02139 United States

A method of synthesizing a control augmentation system via adaptation and learning is described, and simulation results based on the application of this hybrid controller to a six-degree-of-freedom nonlinear aircraft are presented. Two related compensators (one linear, one adaptive) are used for purposes of comparison. Bach controller is designed to improve the basic handling qualities of the aircraft. The hybrid controller was trained by exposing it to a number of transient responses, initiated from trim by applying different pilot stick inputs, at randomly selected flight conditions. Desired responses corresponding to matching inputs and conditions were generated by a simple linearized reference model, scheduled on altitude and Mach number. Performance statistics were obtained by analyzing pitch rate tracking errors. At each evaluation point, a pitch doublet stick input was applied to both the aircraft and reference models. In terms of the mean RMS pitch rate tracking error (averaged over 286 points covering the subsonic flight envelope), the learning augmented system outperformed both the adaptive and linear compensators by 195% and 700%, respectively. © 1993 American Institute of Aeronautics and Astronautics, Inc. All rights reserved.

关键词： Flight envelopes

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Distributed Optimization and Scaling Design for Solving Sylvester Equations

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Journal of systems Science & Complexity 2024年第6期37卷 2487-2510页

作者： CHENG Songsong YU Xin ZENG Xianlin LIANG Shu HONG Yiguang Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and AutomationAnhui UniversityHefei 230601China Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of TechnologyBeijing 100081China Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 201210China

This paper develops distributed algorithms for solving Sylvester *** authors transform solving Sylvester equations into a distributed optimization problem,unifying all eight standard distributed matrix *** the authors propose a distributed algorithm to find the least squares solution and achieve an explicit linear convergence *** results are obtained by carefully choosing the step-size of the algorithm,which requires particular information of data and Laplacian *** avoid these centralized quantities,the authors further develop a distributed scaling technique by using local information *** a result,the proposed distributed algorithm along with the distributed scaling design yields a universal method for solving Sylvester equations over a multi-agent network with the constant step-size freely chosen from configurable ***,the authors provide three examples to illustrate the effectiveness of the proposed algorithms.

关键词： Distributed optimization least squares solution linear convergence rate step-size interval Sylvester equation

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Image segmentation-based unmanned aerial vehicle safe navigation

Image segmentation-based unmanned aerial vehicle safe naviga...

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作者： Agrawa, Pooja Ratnoo, Ashwini Ghose, Debasish Indian Institute of Science Bangalore560 012 India Autonomous Vehicles Laboratory Department of Aerospace Engineering India Control and Decision Systems Laboratory Department of Aerospace Engineering India

This work proposes a vision-based guidance scheme for an unmanned aerial vehicle navigating through urban environments while seeking a predefined goal point. Optical flow of image corner feature points is considered to segment obstacles from the image. An obstacle-avoidance guidance law is proposed to avoid the segmented obstacles. Additionally, detecting open space between segmented obstacles, a passage-following guidance law also presented for intelligent decision making. Analytic comparison with an existing methodology is carried out to highlight superior obstacle-avoidance properties of the proposed strategy. Simulations are carried out in a three-dimensional environment for single and multiple obstacles. Results comply with the analytic findings and present a much improved avoidance performance as compared to existing optical flow-based methods. Copyright © 2017 by The Authors.

关键词： Air navigation

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Lane Geometry, Compliance Levels, and Adaptive Geo-fencing in CORRIDRONE Architecture for Urban Mobility

Lane Geometry, Compliance Levels, and Adaptive Geo-fencing i...

引用

2021 International Conference on Unmanned Aircraft systems, ICUAS 2021

作者： Tony, Lima Agnel Ratnoo, Ashwini Ghose, Debasish Guidance Control and Decision Systems Laboratory Indian Institute of Science Department of Aerospace Engineering Bangalore India Autonomous Vehicles Laboratory Indian Institute of Science Department of Aerospace Engineering Bangalore India

ISBN: (纸本)9780738131153

Integrating Unmanned Aerial Vehicles (UAVs) into airspace requires a reliable framework which is robust and scalable. CORRIDRONE is one such architecture that generates corridors for point-to-point traversal of drones. This work presents details about its central features like adaptive geo-fencing, drone compliance levels, and corridor geometry. Adaptive geo-fencing guarantees vehicle safety when multiple vehicles of different hardware and software capabilities are sharing the same airspace. Compliance levels, defined based on these capabilities of the UAVs, are an essential measure for determining geo-fence bounds. The lane geometry of CORRIDRONE is designed considering the aerodynamic aspects like downwash, which ensures in-flight stability of the UAVs. These features confirm safe transit of the vehicles and also ensures efficient operation of the system. The flexibility to accommodate multiple UAVs of varying compliance levels highlight the robustness of the proposed framework. © 2021 IEEE.

关键词： Geometry

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Deep Distributed Optimization for Large-Scale Quadratic Programming

arXiv

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arXiv 2024年

作者： Saravanos, Augustinos D. Kuperman, Hunter Oshin, Alex Abdul, Arshiya Taj Pacelli, Vincent Theodorou, Evangelos A. Autonomous Control and Decision Systems Laboratory Georgia Institute of Technology United States

Quadratic programming (QP) forms a crucial foundation in optimization, encompassing a broad spectrum of domains and serving as the basis for more advanced algorithms. Consequently, as the scale and complexity of modern applications continue to grow, the development of efficient and reliable QP algorithms is becoming increasingly vital. In this context, this paper introduces a novel deep learning-aided distributed optimization architecture designed for tackling large-scale QP problems. First, we combine the state-of-the-art Operator Splitting QP (OSQP) method with a consensus approach to derive DistributedQP, a new method tailored for network-structured problems, with convergence guarantees to optimality. Subsequently, we unfold this optimizer into a deep learning framework, leading to DeepDistributedQP, which leverages learned policies to accelerate reaching to desired accuracy within a restricted amount of iterations. Our approach is also theoretically grounded through Probably Approximately Correct (PAC)-Bayes theory, providing generalization bounds on the expected optimality gap for unseen problems. The proposed framework, as well as its centralized version DeepQP, significantly outperform their standard optimization counterparts on a variety of tasks such as randomly generated problems, optimal control, linear regression, transportation networks and others. Notably, DeepDistributedQP demonstrates strong generalization by training on small problems and scaling to solve much larger ones (up to 50K variables and 150K constraints) using the same policy. Moreover, it achieves orders-of-magnitude improvements in wall-clock time compared to OSQP. The certifiable performance guarantees of our approach are also demonstrated, ensuring higher-quality solutions over traditional optimizers. © 2024, CC BY-NC-SA.

关键词： Quadratic programming

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