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Quantized Distributed Nonconvex Optimization with Linear Convergence

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Quantized Distributed Nonconvex Optimization with Linear Con...

IEEE Conference on Decision and control

作者： Lei Xu Xinlei Yi Jiayue Sun Yang Shi Karl Henrik Johansson Tao Yang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm Sweden Department of Mechanical Engineering University of Victoria Victoria Canada

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper considers distributed nonconvex op-timization for minimizing the average of local cost functions, by using local information exchange over undirected communication networks. Since the communication channels often have limited bandwidth or capacity, we first introduce a quantization rule and an encoder/decoder scheme to reduce the transmission bits. By integrating them with a distributed algorithm, we then propose a distributed quantized nonconvex optimization algorithm. Assuming the global cost function satisfies the Polyak– Łojasiewicz condition, which does not require the global cost function to be convex and the global minimizer is not necessarily unique, we show that the proposed algorithm linearly converges to a global optimal point. Moreover, a low data rate is shown to be sufficient to ensure linear convergence when the algorithm parameters are properly chosen. The theoretical results are illustrated by numerical simulation examples.

关键词： Quantization (signal) Communication channels Bandwidth Cost function Numerical simulation Communication networks Distributed algorithms

Distributed Model-Free Adaptive Learning control of Discrete-Time Nonlinear Multiagent systems

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IEEE Transactions on Neural Networks and Learning systems 2025年 PP卷 PP页

作者： Ma, Yong-Sheng Che, Wei-Wei Xu, Shi-Xu Deng, Chao Wu, Zheng-Guang Beijing Institute of Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems School of Automation Beijing100081 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries College of Information Science and Engineering Shenyang110819 China Qingdao University Shandong Key Laboratory of Industrial Control Technology Department of Automation Qingdao266071 China Nanjing University of Posts and Telecommunications Institute of Advanced Technology Nanjing210000 China Zhejiang University State Key Laboratory of Industrial Control Technology Institute of Cyber-Systems and Control Hangzhou310027 China

This article investigates the distributed control problem for nonlinear multiagent systems (MASs) with unknown system models. A novel distributed model-free adaptive learning algorithm is developed to learn a controller from the online system data. Notably, a significant advancement over conventional methods is that the proposed algorithm requires only local interaction data from neighboring agents, eliminating dependencies on both a priori system structural knowledge and global topology information. Comprehensive simulations validate the theoretical results and demonstrate the superior efficacy of the devised algorithm. © 2012 IEEE.

关键词： Distributed parameter control systems

Compressed Gradient Tracking Algorithms for Distributed Nonconvex Optimization

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arXiv 2023年

作者： Xu, Lei Yi, Xinlei Wen, Guanghui Shi, Yang Johansson, Karl H. Yang, Tao The State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China The Lab for Information & Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Department of Systems Science School of Mathematics Southeast University Nanjing210096 China The Department of Mechanical Engineering University of Victoria VictoriaBCV8W 2Y2 Canada The Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

In this paper, we study the distributed nonconvex optimization problem, aiming to minimize the average value of the local nonconvex cost functions using local information exchange. To reduce the communication overhead, we introduce three general classes of compressors, i.e., compressors with bounded relative compression error, compressors with globally bounded absolute compression error, and compressors with locally bounded absolute compression error. By integrating them, respectively, with the distributed gradient tracking algorithm, we then propose three corresponding compressed distributed nonconvex optimization algorithms. Motivated by the state-of-the-art BEER algorithm proposed in [1], which is an efficient compressed algorithm integrating gradient tracking with biased and contractive compressors, our first proposed algorithm extends this algorithm to accommodate both biased and non-contractive compressors. For each algorithm, we design a novel Lyapunov function to demonstrate its sublinear convergence to a stationary point if the local cost functions are smooth. Furthermore, when the global cost function satisfies the Polyak–Lojasiewicz (P–L) condition, we show that our proposed algorithms linearly converge to a global optimal point. It is worth noting that, for compressors with bounded relative compression error and globally bounded absolute compression error, our proposed algorithms’ parameters do not require prior knowledge of the P–L constant. The theoretical results are illustrated by numerical examples, demonstrating the effectiveness of the proposed algorithms in significantly reducing the communication burden while maintaining the convergence performance. Moreover, simulation results show that the proposed algorithms outperform the state-of-the-art compressed distributed nonconvex optimization algorithms. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

Analysis and control of autonomous mobility-on-demand systems

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arXiv 2021年

作者： Zardini, Gioele Lanzetti, Nicolas Pavone, Marco Frazzoli, Emilio Institute for Dynamic Systems and Control Department of Mechanical and Process Engineering ETH Zürich Zürich Switzerland Automatic Control Laboratory Department of Information Technology and ElectricalEngineering ETH Zürich Zürich Switzerland Autonomous Systems Lab Department of Aeronautics and Astronautics Stanford University StanfordCA United States

Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research. © 2021, CC BY-NC-ND.

关键词： Autonomous vehicles

Nonlinear Cooperative Output Regulation with Input Delay Compensation

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arXiv 2024年

作者： Zheng, Shiqi Ahn, Choon Ki Jiang, Xiaowei Yan, Huaicheng Shi, Peng The School of Automation China University of Geosciences Wuhan430079 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China The Shaoyang Institute of Advanced Manufacturing Technology Shaoyang422000 China The School of Electrical Engineering Korea University Seoul136-701 Korea Republic of The Key Laboratory of Smart Manufacturing in Energy Chemical Process of Ministry of Education East China University of Science and Technology Shanghai200237 China The School of Electrical and Mechanical Engineering The University of Adelaide AdelaideSA5005 Australia

This paper investigates the cooperative output regulation (COR) of nonlinear multi-agent systems (MASs) with long input delay based on periodic event-triggered mechanism. Compared with other mechanisms, periodic event-triggered control can automatically guarantee a Zeno-free behavior and avoid the continuous monitoring of triggered conditions. First, a new periodic event-triggered distributed observer, which is based on the fully asynchronous communication data, is proposed to estimate the leader information. Second, a new distributed predictor feedback control method is proposed for the considered nonlinear MASs with input delay. By coordinate transformation, the MASs are mapped into new coupled ODE-PDE target systems with some disturbance-like terms. Then, we show that the COR problem is solvable. At last, to further save the communication resource, a periodic event-triggered mechanism is considered in the sensor-to-controller transmission in every agent. A new periodic event-triggered filter is proposed to deal with the periodic event-triggered feedback data. The MASs with input delay are mapped into coupled ODE-PDE target systems with sampled data information. Then, Lyapunov-Krasovskii functions are constructed to demonstrate the exponential stability of the MASs. Simulations verify the validity of the proposed results. © 2024, CC BY-NC-ND.

关键词： Feedback control

Graph Optimization based Visual SLAM fusing KeyPoints and Markers

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Graph Optimization based Visual SLAM fusing KeyPoints and Ma...

第三十九届中国控制会议

作者： Zhen Chen Yang Zhou Fengdi Zhang Min Xu Xiangdong Liu Zhen Li School of Automation and Key Laboratory for Intelligent Control & Decision on Complex Systems Beijing Institute of Technology Beijing Aerospace Automatic Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligence Control

Simultaneous localization and mapping（SLAM） plays an important role in autonomous navigation for mobile *** of the visual SLAM approaches use keypoints for tracking, whose performance however suffers from the unstable landmarks during task due to uncertain light condition and frequently changeable viewpoint. The situation even becomes worse for visual SLAM in low texture environment especially in indoor buildings, where the supplementary artificial markers can be used to improve the robust detection under a wider range of circumstances. Inspired by this thought, this paper developed a visual SLAM system integrated with keypoints and artificial markers. A graph optimization problem is constructed to optimize the trajectory by taking both the reprojection error of keypoints and the influence of markers into consideration. The experimental results on SPM dataset demonstrate the superior accuracy of the proposed graph optimization algorithm compared with the start-of-the-art ORB-SLAM2.

关键词： Visual simultaneous localization and mapping（SLAM） graph optimization keypoints artificial markers

Distributionally Robust Fault Detection by using Kernel Density Estimation ⁎

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IFAC-PapersOnLine 2020年第2期53卷 652-657页

作者： Ting Xue Maiying Zhong Lijia Luo Linlin Li Steven X. Ding Institute for Automatic Control and Complex Systems University of Duisburg-Essen Germany College of Electrical Engineering and Automation Shandong University of Science and Technology China Institute of Process Equipment and Control Engineering Zhejiang University of Technology China School of Automation and Electrical Engineering University of Science and Technology Beijing China

In this paper, a method of distributionally robust fault detection (FD) is proposed for stochastic linear discrete-time systems by using the kernel density estimation (KDE) technique. For this purpose, an H 2 optimization-based fault detection filter is constructed for residual generation. Towards maximizing the fault detection rate (FDR) for a prescribed false alarm rate (FAR), the residual evaluation issue regarding the design of residual evaluation function and threshold is formulated as a distributionally robust optimization problem, wherein the so-called confidence sets are constituted to model the ambiguity of distribution knowledge of residuals in fault-free and faulty cases. A KDE based solution, robust to the estimation errors in probability distribution of residual caused by the finite number of samples, is further developed to address the targeting problem such that the residual evaluation function, threshold as well as the lower bound of FDR can be achieved simultaneously. A case study on a vehicle lateral control system demonstrates the applicability of the proposed FD method.

关键词： Fault detection distributionally robust optimization kernel density estimation

Communication Compression for Distributed Nonconvex Optimization

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arXiv 2022年

作者： Yi, Xinlei Zhang, Shengjun Yang, Tao Chai, Tianyou Johansson, Karl H. Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Digital Futures Stockholm100 44 Sweden Department of Electrical Engineering University of North Texas DentonTX76203 United States State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China

This paper considers distributed nonconvex optimization with the cost functions being distributed over agents. Noting that information compression is a key tool to reduce the heavy communication load for distributed algorithms as agents iteratively communicate with neighbors, we propose three distributed primal–dual algorithms with compressed communication. The first two algorithms are applicable to a general class of compressors with bounded relative compression error and the third algorithm is suitable for two general classes of compressors with bounded absolute compression error. We show that the proposed distributed algorithms with compressed communication have comparable convergence properties as state-of-the-art algorithms with exact communication. Specifically, we show that they can find first-order stationary points with sublinear convergence rate O(1/T) when each local cost function is smooth, where T is the total number of iterations, and find global optima with linear convergence rate under an additional condition that the global cost function satisfies the Polyak–Lojasiewicz condition. Numerical simulations are provided to illustrate the effectiveness of the theoretical results. Copyright © 2022, The Authors. All rights reserved.

关键词： Iterative methods

Nonsingular field-of-view constrained cooperative guidance with improved time-to-go estimation

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Chinese Journal of Aeronautics 2025年

作者： Xiaofei YANG Yunjie WU Weibo XIA Xiaodong LIU Haibin DUAN School of Automation Science and Electrical Engineering Beihang University Beijing 100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing 100191 China Beĳing Aerospace Automatic Control Institute Beĳing 100854 China

This paper proposes a distributed nonsingular cooperative guidance law for multiple flight vehicles with Field-of-View (FOV) constraints. First, a novel time-to-go estimation is developed based on a FOV-constrained Proportional Navigation Guidance (FPNG) law. Then, the FPNG law is augmented with a cooperative guidance term to achieve consensus of time-to-go with predefined-time convergence prior to the impact time. A continuous auxiliary function is introduced in the bias term to avoid the singularity of guidance command. Moreover, the proposed guidance law is extended to the three-dimensional guidance scenarios and the moving target with the help of a predicted interception point. Finally, several numerical simulations are conducted, and the results verify the effectiveness, robustness, and advantages of the proposed cooperative guidance law.

关键词： Flight vehicles Cooperative guidance Field-of-view constraints Time-to-go estimation Predefined-time convergence