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arXiv 2021年

作者： Yuan, Ye Liu, Jun Jin, Dou Yue, Zuogong Chen, Ruijuan Wang, Maolin Sun, Chuan Xu, Lei Hua, Feng He, Xin Yi, Xinlei Yang, Tao Zhang, Hai-Tao Sui, Shaochun Ding, Han School of Artificial Intelligence and Automation Huazhong University of Science and Technology School of Mechanical Science and Engineering Huazhong University of Science and Technology Department of Applied Mathematics University of Waterloo Canada State Key Laboratory of Synthetical Automation for Process Industries Northeastern University United States School of Electrical Engineering and Computer Science Digital Futures Kth Royal Institute of Technology Co. Ltd.

Traditional machine learning relies on a centralized data pipeline, i.e., data are provided to a central server for model training. In many applications, however, data are inherently fragmented. Such a decentralized nature of these databases presents the biggest challenge for collaboration: sending all decentralized datasets to a central server raises serious privacy concerns. Although there has been a joint effort in tackling such a critical issue by proposing privacy-preserving machine learning frameworks, such as federated learning, most state-of-the-art frameworks are built still in a centralized way, in which a central client is needed for collecting and distributing model information (instead of data itself) from every other client, leading to high communication pressure and high vulnerability when there exists a failure at or attack on the central client. Here we propose a principled decentralized federated learning algorithm (DeceFL), which does not require a central client and relies only on local information transmission between clients and their neighbors, representing a fully decentralized learning framework. It has been further proven that every client reaches the global minimum with zero performance gap and achieves the same convergence rate O(1/T) (where T is the number of iterations in gradient descent) as centralized federated learning when the loss function is smooth and strongly convex. Finally, the proposed algorithm has been applied to a number of applications to illustrate its effectiveness for both convex and nonconvex loss functions, demonstrating its applicability to a wide range of real-world medical and industrial applications. Copyright © 2021, The Authors. All rights reserved.

关键词： Gradient methods

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A Filtering Approach Based on MMAE for a SINS/CNS Integrated Navigation System

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IEEE/CAA Journal of Automatica Sinica 2018年第6期5卷 1113-1120页

作者： Fangfang Zhao Cuiqiao Chen Wei He Shuzhi Sam Ge School of Computer Science and Engineering and Center for RoboticsUniversity of Electronic Science and Technology of ChinaChengdu 611731China School of Automation and Electrical Engineering University of Science and Technology of BeijingBeijing 100083China Social Robotics Laboratory Interactive Digital Media Instituteand Department of Electrical and Computer EngineeringNational University of SingaporeSingapore 117576Singapore Singapore 117576 Singapore

This paper explores multiple model adaptive estimation(MMAE) method, and with it, proposes a novel filtering algorithm. The proposed algorithm is an improved Kalman filter— multiple model adaptive estimation unscented Kalman filter(MMAE-UKF) rather than conventional Kalman filter methods,like the extended Kalman filter(EKF) and the unscented Kalman filter(UKF). UKF is used as a subfilter to obtain the system state estimate in the MMAE method. Single model filter has poor adaptability with uncertain or unknown system parameters,which the improved filtering method can overcome. Meanwhile,this algorithm is used for integrated navigation system of strapdown inertial navigation system(SINS) and celestial navigation system(CNS) by a ballistic missile's motion. The simulation results indicate that the proposed filtering algorithm has better navigation precision, can achieve optimal estimation of system state, and can be more flexible at the cost of increased computational burden.

关键词： Celestial navigation integrated navigation multiple model adaptive estimation unscented Kalman filter （MMAEUKF） strap-down inertial navigation

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Robust Output Agreement of Multi-Agent Systems with Flexible Topologies ⁎

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IFAC-PapersOnLine 2020年第2期53卷 5647-5652页

作者： Zhanxiu Wang Tengfei Liu Zhong-Ping Jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110004 China Department of Electrical and Computer Engineering New York University 370 Jay Street Brooklyn NY 11201 USA

This paper studies the robust output agreement problem for second-order multi-agent systems with flexible topologies subject to measurement disturbances. A new distributed control law is proposed to guarantee the robust output agreement in the sense of input-to-state stability (ISS) as long as the union of the interconnection graphs satisfies a standard connectivity condition. It is proved that, robust output agreement can be achieved in the presence of any bounded measurement disturbances if the functions of the distributed control laws are radially unbounded, and a local result can still be guaranteed if the condition of radial unboundedness is not satisfied. Numerical simulations are employed to show the effectiveness of the main result.

关键词： Multi-agent systems robust output agreement measurement disturbance flexible topology

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Distributed Optimization of Nonlinear Uncertain Systems: An Adaptive Backstepping Design ⁎

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IFAC-PapersOnLine 2020年第2期53卷 5653-5658页

作者： Zhengyan Qin Tengfei Liu Zhong-Ping Jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110004 China Department of Electrical and Computer Engineering New York University 370 Jay Street Brooklyn NY 11201 USA

This paper proposes a Lyapunov-based adaptive backstepping approach to distributed optimization of nonlinear uncertain multi-agent systems. The model of each agent is in the strict-feedback form with parametric uncertainties. By only using local objective functions, this paper aims to solve the distributed optimization problem for the multi-agent system such that the outputs of the agents converge to the optimizer of the total objective function. Based on the idea of adaptive backstepping, the distributed optimization problem for the high-order multi-agent system is decomposed into solving the optimization or control problem for multiple first-order subsystems. The technical contributions lie in a Lyapunov-based design for distributed optimization, and a refined nonlinear damping design to deal with the newly appearing nonlinear uncertain terms caused by optimization. Based on the new designs, a Lyapunov function is constructed for the entire system, and the LaSalle-Yoshizawa Theorem is employed for convergence analysis. It is shown that the objective of distributed optimization is achievable if the local objective functions are convex with at least one of them being strongly convex. computer-based numerical simulation is employed to show the effectiveness of the proposed design.

关键词： Distributed optimization adaptive control multi-agent systems nonlinear strict-feedback systems parametric uncertainty

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Distributed Observer-Based Time-Varying Formation Control Under Switching Topologies

Distributed Observer-Based Time-Varying Formation Control Un...

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European Control Conference (ECC)

作者： Sandeep Kumar Soni Garima Soni Siyuan Wang Driss Boutat Mohamed Djemai Sorin Olaru Johann Reger Daniel Geha INSA Centre Val de Loire University d’Orleans PRISME EA 4229 Bourges Cedex France Department of Electrical and Electronics Engineering BIT Raipur Chhattisgarh India Université Polytechnique Hauts-de-France LAMIH CNRS UMR 8201 Valenciennes Cedex 9 France QUARTZ Laboratory EA 7393 ENSEA Cergy 6 Avenue du Ponceau Cergy France Laboratoire des Signaux et Systèmes CentraleSupélec Université Paris Saclay 3 rue Joliot Curie Gif-sur-Yvette France Control Engineering Group Technische Universität Ilmenau Ilmenau Germany Industrial Automation Robotics 1 Chemin de la Bourellerie Thenioux France

This paper proposes the distributed observer-based control approach to achieve time-varying formation of second-order autonomous unmanned systems (AUSs) under switching topologies. It is assumed that each AUS has access only to the positions of its neighbouring AUS agents. An observer is then designed to estimate the velocity of neighbouring AUS agents. Furthermore, a distributed control for the AUSs is designed using information of position and estimated velocity. A common Lyapunov function is employed in order to establish sufficient conditions for the stability of closed-loop systems. In order to address the effect of switching topologies, a dwell-time condition is has been considered. Moreover, the observer-based time-varying formation controller satisfies the separation principle. Finally, simulation results are presented to illustrate the effectiveness of the proposed approach.

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Extremum Seeking for Nonlinear Uncertain Systems: A Small-Gain Synthesis

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IFAC-PapersOnLine 2020年第2期53卷 5411-5416页

作者： Qiyue Wang Zhengyan Qin Tengfei Liu Zhong-Ping Jiang State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang 110004 China Department of Electrical and Computer Engineering New York University 370 Jay Street Brooklyn NY 11201 USA

This paper studies the extremum seeking problem for static maps with the inputs of the maps generated by a nonlinear uncertain system. A new small-gain approach is developed which uses an extremum seeking strategy to generate a reference signal, and employs a control law for reference-tracking of the nonlinear uncertain systems. The notions of input-to-state stability (ISS) and input-to-output stability (IOS) are used to characterize the interconnection between the extremum seeking strategy and the reference-tracking controller, and the nonlinear small-gain theorem is employed to guarantee the stability of the closed-loop extremum seeking system. With the proposed approach, the extremum seeking problem for a complex nonlinear system is solvable as long as one can design a proper reference-tracking controller for the system. Examples are given to show the feasibility of the proposed approach, and a numerical simulation is employed to show the effectiveness of the proposed design.

关键词： Extremum seeking small-gain theorem semi-global practical stabilization

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Sparse Representation Based Image Super-resolution Using Large Patches

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Chinese Journal of Electronics 2018年第4期27卷 813-820页

作者： LIU Ning ZHOU Pan LIU Wenju KE Dengfeng National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Department of Electrical and Computer Engineering National University of Singapore

This paper addresses the problem of generating a high-resolution image from a low-resolution image. Many dictionary based methods have been proposed and have achieved great success in super resolution application. Most of these methods use small patches as dictionary atoms, and utilize a unified dictionary pair to conduct reconstruction for each patch, which may limit the super resolution performance. We use large patches instead of small ones to combine a dictionary and to conduct patch reconstruction. Since a large patch contains more meaningful information than a small one, the reconstruction result may have more high frequency details. To guarantee the completeness of the dictionary with large patch, the scale of the dictionary should be large as well. To handle the storage and calculation problems with large dictionaries,we adopt a binary encoding method. This method can preserve local information of patches. For each patch in the low-resolution image, we search its similar patches in the low-resolution dictionary to obtain a sub-dictionary. We compute its sparse representation to get the corresponding high-resolution version. Global reconstruction constraint is enforced to eliminate the discrepancy between the SR result and the ground truth. Experimental results demonstrate that our method outperforms other super resolution methods, especially when the magnification factor is large or the image is blurred by white Gaussian noise.

关键词： Super resolution Sparse representations Binary encoding

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In-flight positional and energy use data set of a DJI matrice 100 quadcopter for small package delivery

arXiv

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arXiv 2021年

作者： Rodrigues, Thiago A. Patrikar, Jay Choudhry, Arnav Feldgoise, Jacob Arcot, Vaibhav Gahlaut, Aradhana Lau, Sophia Moon, Brady Wagner, Bastian Matthews, H. Scott Scherer, Sebastian Samaras, Constantine Department of Civil and Environmental Engineering Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States Robotics Institute Carnegie Mellon University 5000 Forbes Avenue Pittsburgh15213 United States Dietrich College of Humanities and Social Sciences Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States General Robotics Automation Sensing and Perception Laboratory University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213 United States Ravensburg Germany

We autonomously direct a small quadcopter package delivery Uncrewed Aerial Vehicle (UAV) or "drone" to take off, fly a specified route, and land for a total of 209 flights while varying a set of operational parameters. The vehicle was equipped with onboard sensors, including GPS, IMU, voltage and current sensors, and an ultrasonic anemometer, to collect high-resolution data on the inertial states, wind speed, and power consumption. Operational parameters, such as commanded ground speed, payload, and cruise altitude, are varied for each flight. This large data set has a total flight time of 10 hours and 45 minutes and was collected from April to October of 2019 covering a total distance of approximately 65 kilometers. The data collected were validated by comparing flights with similar operational parameters. We believe these data will be of great interest to the research and industrial communities, who can use the data to improve UAV designs, safety, and energy efficiency, as well as advance the physical understanding of in-flight operations for package delivery drones. Copyright © 2021, The Authors. All rights reserved.

关键词： Industrial research

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Incremental Non-Gaussian Inference for SLAM Using Normalizing Flows

arXiv

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arXiv 2021年

作者： Huang, Qiangqiang Pu, Can Khosoussi, Kasra Rosen, David M. Fourie, Dehann How, Jonathan P. Leonard, John J. MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA02139 United States MIT Department of Nuclear Science and Engineering CambridgeMA02139 United States Robotics and Autonomous Systems Group DATA61 CSIRO BrisbaneQLD4069 Australia Northeastern University Department of Electrical and Computer Engineering BostonMA02115 United States NavAbility BostonMA02110 United States MIT Department of Aeronautical and Astronautical Engineering CambridgeMA02139 United States

This paper presents normalizing flows for incremental smoothing and mapping (NF-iSAM), a novel algorithm for inferring the full posterior distribution in SLAM problems with nonlinear measurement models and non-Gaussian factors. NF-iSAM exploits the expressive power of neural networks, and trains normalizing flows to model and sample the full posterior. By leveraging the Bayes tree, NF-iSAM enables efficient incremental updates similar to iSAM2, albeit in the more challenging non-Gaussian setting. We demonstrate the advantages of NF-iSAM over state-of-the-art point and distribution estimation algorithms using range-only SLAM problems with data association ambiguity. NF-iSAM presents superior accuracy in describing the posterior beliefs of continuous variables (e.g., position) and discrete variables (e.g., data association). Copyright © 2021, The Authors. All rights reserved.

关键词： Gaussian noise (electronic)

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Exponential Convergence for Distributed Optimization Under the Restricted Secant Inequality Condition ⁎

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IFAC-PapersOnLine 2020年第2期53卷 2672-2677页

作者： Xinlei Yi Shengjun Zhang Tao Yang Tianyou Chai Karl H. Johansson School of Electrical Engineering and Computer Science KTH Royal Institute of Technology 100 44 Stockholm Sweden Department of Electrical Engineering University of North Texas Denton TX 76203 USA State Key Laboratory of Synthetical Automation for Process Industries Northeastern University 110819 Shenyang China

This paper considers the distributed optimization problem of minimizing a global cost function formed by a sum of local smooth cost functions by using local information exchange. A standard assumption for proving exponential/linear convergence of existing distributed first-order methods is strong convexity of the cost functions. This does not hold for many practical applications. In this paper, we propose a continuous-time distributed primal-dual gradient descent algorithm and show that it converges exponentially to a global minimizer under the assumption that the global cost function satisfies the restricted secant inequality condition. This condition is weaker than strong convexity and the global minimizer is not necessarily unique. Moreover, a discrete-time distributed primal-dual algorithm is developed from the continuous-time algorithm by Euler’s approximation method, which also linearly converges to a global minimizer under the same condition. The theoretical results are illustrated by numerical simulations.

关键词： Distributed optimization exponential convergence primal-dual algorithm restricted secant inequality

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