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arXiv 2020年

作者： Zhang, Qingqing Wang, Gang Giannakis, Georgios B. Liu, Qingwen Chen, Jie College of Electronic and Information Engineering Tongji University Shanghai201800 China Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN 55455 United States State Key Lab of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Tongji University Shanghai200092 China

—Internet of things (IoT) is powering up smart cities by connecting all kinds of electronic devices. The power supply problem of IoT devices constitutes a major challenge in current IoT development, due to the poor battery endurance as well as the troublesome cable deployment. The wireless power transfer (WPT) technology has recently emerged as a promising solution. Yet, existing WPT advances cannot support free and mobile charging like Wi-Fi communications. To this end, the concept of mobile energy transfer (MET) is proposed, which relies critically on an resonant beam charging (RBC) technology. The adaptive (A) RBC technology builds on RBC, but aims at improving the charging efficiency by charging devices at device preferred current and voltage levels adaptively. A mobile ARBC scheme is developed relying on an adaptive source power control. Extensive numerical simulations using a 1,000mAh Li-ion battery show that the mobile ARBC outperforms simple charging schemes such as the constant power charging, the profile-adaptive charging, and the distance-adaptive charging in saving energy. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy transfer

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Biomimetic inspiration for PKM torso design in humanoid robots

Biomimetic inspiration for PKM torso design in humanoid robo...

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IEEE International Conference on Mechatronics and Automation

作者： Hulin Huang Ceccarelli Marco Weimin Zhang Qiang Huang Zhangguo Yu Xuechao Chen Ministry of Education Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Beijing China Laboratory of Robotics and Mechatronics University of Cassino and South Latium Cassino Italy Ministry of Education International Joint Research Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing China

ISBN: (纸本)9781479970995

A new torso design of humanoid robot is proposed as a 3-DOF PKM (Parallel Kinematic Manipulator) structure by using results of a biomimetic analysis of torso in humans and animals. Main characteristics of torso in nature are presented from which design requirements are outlined for a solution in humanoid robot. The PKM torso is consist of two platforms and four UPU mechanisms. The usefulness of such a biomimetic inspiration is proved by a numerical simulation of the proposed PKM torso in its basic movements.

关键词： Torso Humanoid robots Joints Thorax Horses Payloads

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${H}_{\infty }$ Filtering for Fuzzy Jumping Genetic Regulatory Networks With Round-Robin Protocol: A Hidden-Markov-Model-Based Approach

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IEEE Transactions on Fuzzy systems 2019年第1期28卷 112-121页

作者： Hao Shen Yunzhe Men Jinde Cao Ju H. Park School of Electrical and Information Engineering Anhui University of Technology Ma’anshan China School of Mathematics Southeast University Nanjing China Key Laboratory of Intelligent Control and Decision of Complex Systems and School of Automation Beijing Institute of Technology Beijing China Jiangsu Provincial Key Laboratory of Networked Collective Intelligence and School of Mathematics Southeast University Nanjing China Department of Electrical Engineering Yeungnam University Kyongsan South Korea

In this article, a method to design the filter for fuzzy jumping genetic regulatory networks is explored. The case when the filters cannot directly utilize the mode information of the plant is taken into account. A hidden Markov model is introduced to address such a problem. Furthermore, a mature scheduling method, namely round-robin protocol, is employed to optimize the data transmission in genetic regulatory networks. On the basis of the fuzzy model approach and the stochastic analysis technique, some novel conditions ensuring the H ∞ performance and stochastic stability of the error system are established. The parameters of the filter can be presented via addressing the convex optimization problem. The feasibility of results is finally illustrated by considering a repressilator model subject to stochastic jumping parameters.

关键词： Hidden Markov models Proteins Genetics Protocols Markov processes Data communication

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Event-triggered Extended Kalman Filter for UAV Monitoring System

Event-triggered Extended Kalman Filter for UAV Monitoring Sy...

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Data Driven control and Learning systems (DDCLS)

作者： Yunge Zang Yan Li Yuting Duan Xiangyu Li Xi Chang Zhen Li Beijing Aerospace Automatic Control Institute Beijing P. R. China National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing P. R. China Beijing Aerospace Institute for Metrology and Measurement Technology Beijing P. R. China Key Laboratory for Intelligent Control … Decision on Complex Systems School of Automation Beijing Institute of Technology Beijing China

To facilitate ground station monitoring and command uploading, unmanned aerial vehicles (UAVs) need to frequently exchange individual state data between units. However, this results in a significant usage of communication bandwidth. To address this issue, on the basis of an event-triggered strategy, this paper proposes an Extended Kalman Filter (EKF). aimed at reducing the communication burden of UAVs while maintaining high accuracy. Specifically, a state measurement triggered by an event is selected for filtering only if it contains innovation, thereby reducing the amount of data that needs to be communicated. Since UAV systems are nonlinear, EKF is adopted to fully utilize the information obtained from event-triggered strategies, thereby enhancing the estimation performance. In this paper, a physical UAV was used to verify the proposed algorithm, and it proved to have robust dynamic performance and to effectively reduce the communication rate.

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Isochronous Temporal Metric for Neighbourhood Analysis in Classification Tasks

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SN Computer Science 2023年第6期4卷 1-12页

作者： Kumar, Amit Krishan Mai, Nguyễn Ngọc Tian, Ke Xia, Yuanqing Institute of Research and Development Duy Tan University Da Nang Vietnam Faculty of Electrical-Electronic Engineering Duy Tan University Da Nang Vietnam Faculty of Chinese Language School of Foreign Language Duy Tan University Da Nang Vietnam State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing China

Machine learning, classification, and clustering techniques use the distance functions to evaluate the proximity between data entries and deduce the best neighbouring element and the closest matching entry. The best neighbour is not only the closest neighbour but a neighbour that is quick to respond. In view of that, a time-based isochronous metric is introduced to evaluate the best neighbours and form linkages by grouping similar entities. The proposed method uses parametric equations of the fastest descent and solves the time variables for attributes localised in curved space–time. The time metric is compared with commonly used distance metrics for accuracy in classification and clustering using benchmark and commonly used datasets. The nearest-neighbour technique is used for evaluating classification accuracy, and an adjusted random index (ARI) is used to evaluate clustering. The proposed method shows better accuracy and ARI in comparison to distance functions. It also assigns better weights to attributes of the dataset and easily identifies repeated patterns in noisy time series data.

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Distance- And velocity-based collision avoidance for time-varying formation control of second-order multi-agent systems

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IEEE Transactions on Circuits and systems II: Express Briefs 2021年第4期68卷 1253-1257页

作者： Pang, Zhong-Hua Zheng, Chang-Bing Sun, Jian Han, Qing-Long Liu, Guo-Ping Key Laboratory of Fieldbus Technology and Automation of Beijing North China University of Technology Beijing100144 China State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China School of Software and Electrical Engineering Swinburne University of Technology MelbourneVIC3122 Australia Department of Artificial Intelligence and Automation Wuhan University Wuhan430072 China

This brief addresses the time-varying formation control problem with collision avoidance for second-order multi-agent systems. By taking both distances and velocities between agents into account, a novel collision avoidance scheme is proposed on the basis of conventional distance-based artificial potential field methods. Compared with the latter, the proposed scheme can more accurately determine possible collision situations and thus significantly reduce possible collision areas, which consequently not only can reduce energy consumption, but also has as few adverse effects on formation maintenance as possible. Then a time-varying formation control protocol is designed so as to achieve zero steady-state position and velocity formation tracking errors without collision. Finally, theoretical analysis, numerical demonstration, as well as comparative simulation are provided to verify the effectiveness of the proposed method. © 2004-2012 IEEE.

关键词： Multi agent systems

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Adaptive Neural Network control for a Lower-Limb Exoskeleton using Variable Stiffness Transferring

Adaptive Neural Network Control for a Lower-Limb Exoskeleton...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Longbin Zhang Yingbai Hu Hang Su Jiehao Li Salih Ertug Ovur KTH MoveAbility Lab and KTH BioMEx Center KTH Royal Institute of Technology Stockholm Sweden Technical University of Munich Munich Germany Politecnico di Milano Milano Italy State Key Laboratory of Complex System Intelligent Control and Decision Beijing Institute of Technology Beijing China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

Lower-limb exoskeletons are increasingly used in gait assistance applications to assist patients with locomotive disorders in rehabilitation and daily life. To provide assistance based on the user's variable joint stiffness with human-like characteristics is a growing and challenging field. This paper proposed an adaptive controller for a lower-limb exoskeleton with time-varying delay using variable stiffness transferring strategy. Neural network compensation was applied to estimate the nonlinearity caused by the time-varying delay and the model uncertainties in the lower-limb exoskeleton systems. To compensate for the delay, an integral Lyapunov function was introduced and an adaptive Lyapunov-based control structure was constructed. In the controller design, a variable stiffness transferring strategy was integrated and the stiffness was transferred from an able-bodied individual estimated with muscle forces and musclotendinous stiffness. By utilizing the proposed adaptive neural network control strategy and adaptive Lyapunov-based control structure, the global stability of lower-limb exoskeleton system was guaranteed and the tracking errors converged to the zero. The proposed control strategy was validated on 2-DOFs knee and ankle lower limb exoskeleton in simulation, demonstrating effective results in trajectories tracking, and thus is crucial for determining strategies for exoskeleton control.

关键词： Exoskeletons Robots Muscles Adaptive systems Force Mathematical model Mechatronics

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Distributed synchronous and asynchronous algorithms for semi-definite programming with diagonal constraints

arXiv

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arXiv 2021年

作者： Jiang, Xia Zeng, Xianlin Sun, Jian Chen, Jie Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Ministry of Education Beijing100081 China School of Electronic and Information Engineering Tongji University Shanghai200082 China

This paper develops distributed synchronous and asynchronous algorithms for the large-scale semi-definite programming with diagonal constraints, which has wide applications in combination optimization, image processing and community detection. The information of the semi-definite programming is allocated to multiple interconnected agents such that each agent aims to find a solution by communicating to its neighbors. Based on low-rank property of solutions and the Burer-Monteiro factorization, we transform the original problem into a distributed optimization problem over unit spheres to reduce variable dimensions and ensure positive semi-definiteness without involving semi-definite projections, which are computationally expensive. For the distributed optimization problem, we propose distributed synchronous and asynchronous algorithms, both of which reduce computational burden and storage space compared with existing centralized algorithms. Specifically, the distributed synchronous algorithm almost surely escapes strict saddle points and converges to the set of optimal solutions to the optimization problem. In addition, the proposed distributed asynchronous algorithm allows communication delays and converges to the set of critical points to the optimization problem under mild conditions. By applying proposed algorithms to image segmentation applications, we illustrate the efficiency and convergence performance of the two proposed algorithms. © 2021, CC BY.

关键词： Optimization

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Distributed stochastic gradient tracking algorithm with variance reduction for non-convex optimization

arXiv

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arXiv 2021年

This paper proposes a distributed stochastic algorithm with variance reduction for general smooth non-convex finite-sum optimization, which has wide applications in signal processing and machine learning communities. In distributed setting, large number of samples are allocated to multiple agents in the network. Each agent computes local stochastic gradient and communicates with its neighbors to seek for the global optimum. In this paper, we develop a modified variance reduction technique to deal with the variance introduced by stochastic gradients. Combining gradient tracking and variance reduction techniques, this paper proposes a distributed stochastic algorithm, GT-VR, to solve large-scale non-convex finite-sum optimization over multi-agent networks. A complete and rigorous proof shows that the GT-VR algorithm converges to first-order stationary points with O(k1) convergence rate. In addition, we provide the complexity analysis of the proposed algorithm. Compared with some existing first-order methods, the proposed algorithm has a lower O(PMΕ−1) gradient complexity under some mild condition. By comparing state-of-the-art algorithms and GT-VR in experimental simulations, we verify the efficiency of the proposed algorithm. © 2021, CC BY.

关键词： Stochastic systems

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Distributed proximal gradient algorithm for non-smooth non-convex optimization over time-varying networks

arXiv

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arXiv 2021年

This note studies the distributed non-convex optimization problem with non-smooth regularization, which has wide applications in decentralized learning, estimation and control. The objective function is the sum of different local objective functions, which consist of differentiable (possibly non-convex) cost functions and non-smooth convex functions. This paper presents a distributed proximal gradient algorithm for the non-smooth non-convex optimization problem over time-varying multi-agent networks. Each agent updates local variable estimate by the multi-step consensus operator and the proximal operator. We prove that the generated local variables achieve consensus and converge to the set of critical points with convergence rate O(1/T). Finally, we verify the efficacy of proposed algorithm by numerical simulations. © 2021, CC BY.

关键词： Time varying networks

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