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IEEE International Conference on Robotics and Biomimetics

作者： Chenguang Yang Rongxin Cui Zhijun Li Sanjay Sharma Xingang Zhao Center for Robotics and Neural Systems Plymouth University Ply-mouth UK The MOE Key Lab of Autonomous System and Network Control South China University of Technology Guangzhou China School of Marine Science and Technology Northwestern Polytechnical University Xi'an China School of Marine Science and Engineering University of Plymouth Plymouth UK Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9781479973989

In this paper, adaptive control of unmanned marine surface vessel has been investigated in the presence of uncertain dynamics as well as external disturbance. Variable structure technique has been utilized in the control design to make the dynamic response and desired motion tracking as quickly as possible. In practice, usually the desired trajectories are given as step points, such that direct immediate tracking would cause large acceleration and huge control effort, while the latter may be out of the actuators' limitation. Therefore, a reference model approach is employed, such that instead of direct tracking of the desired set point, the control objective becomes to shape the closed-loop dynamics to match a reference model. The reference model is built using optimization technique which tries to minimizes both tracking error and motion acceleration, and subsequently control effort. The smooth tracking is thus guaranteed in an optimal manner.

关键词： Tracking Acceleration Trajectory Adaptation models Performance analysis Mathematical model Dynamics

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Restoring Connectivity of Damaged Sensor networks for Long-Term Survival in Hostile Environments

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IEEE INTERNET OF THINGS JOURNAL 2020年第2期7卷 1205-1215页

作者： Liu, Xuxun Liu, Anfeng Qiu, Tie Dai, Bin Wang, Tian Yang, Lei South China Univ Technol Coll Elect & Informat Engn Guangzhou 510640 Peoples R China South China Univ Technol Key Lab Autonomous Syst & Network Control Minist Educ Guangzhou 510640 Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China Tianjin Univ Sch Comp Sci & Technol Tianjin 300350 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China South China Univ Technol Sch Software Engn Guangzhou 510640 Peoples R China

Connectivity restoration plays an important role in maintaining the long-term operation in wireless sensor networks (WSNs), especially, in environment-harsh cases. However, current solutions lack the ability to handle the second damages and the capacity of designing requirement-different connectivity approaches according to different needs. In this article, we propose a durability-based connectivity establishment (DBCE) scheme for disjoint segments of WSNs. This scheme includes three approaches regarding segment evaluation or segment selection: 1) a segment shape evaluation approach;2) a region different connectivity approach;and 3) a data traffic transfer approach, for their respective objectives. The unique characteristics of this article are twofold. On the one hand, this is the first attempt to investigate segment shapes, which we demonstrate have great impact on the robustness of the network. On the other hand, distinguished from the existing networks with uniform connectivity rule, the network is divided into two parts and different connectivity sequences and connectivity approaches are designed according to disparate features and requirements of the network. The performance of DBCE is validated through extensive simulation experiments.

关键词： Connectivity recovery data traffic durability segment shape wireless sensor networks (WSNs)

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Swarm-Intelligence-Based Rendezvous Selection via Edge Computing for Mobile Sensor networks

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IEEE INTERNET OF THINGS JOURNAL 2020年第10期7卷 9471-9480页

作者： Liu, Xuxun Qiu, Tie Dai, Bin Yang, Lei Liu, Anfeng Wang, Jiangtao South China Univ Technol Coll Elect & Informat Engn Key Lab Autonomous Syst & Network Control Guangzhou 510641 Peoples R China South China Univ Technol Minist Educ China Guangzhou 510641 Peoples R China Tianjin Univ Coll Intelligence & Comp Sch Comp Sci & Technol Tianjin 300350 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 611756 Peoples R China South China Univ Technol Coll Soft Engn Guangzhou 510006 Peoples R China Cent South Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China Univ Lancaster Sch Comp & Commun Lancaster LA1 4YW England

Mobile-edge nodes, as an efficient approach to the performance improvement of wireless sensor networks (WSNs), play an important role in edge computing. However, existing works only focus on connected networks and suffer from high calculational costs. In this article, we propose a rendezvous selection strategy for data collection of disjoint WSNs with mobile-edge nodes. The goal is to achieve full network connectivity and minimize path length. From the perspective of the application scenario, this article is distinctive in two aspects. On the one hand, it is specially designed for partitioned networks which are much more complex than conventional connected scenarios. On the other hand, this article is specially designed for delay-harsh applications rather than usual energy-oriented scenarios. From the viewpoint of the implementation method, a simplified ant colony optimization (ACO) algorithm is performed and displays two characteristics. The first one is the path segmenting mechanism, simplifying the path construction of each part and consequently reducing the computational cost. The second one is the candidate grouping mechanism, reducing the search space and accordingly speeding up the convergence speed. Simulation results demonstrate the feasibility and advantages of this approach.

关键词： Delays Wireless sensor networks Data collection Trajectory Internet of Things Clustering algorithms Computational efficiency Ant colony optimization (ACO) disconnected network mobile-edge node rendezvous selection wireless sensor network (WSN)

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Importance-Different Charging Scheduling Based on Matroid Theory for Wireless Rechargeable Sensor networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第5期20卷 3284-3294页

作者： Ouyang, Wenyu Obaidat, Mohammad S. Liu, Xuxun Long, Xiaoting Xu, Wenzheng Liu, Tang South China Univ Technol Coll Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China South China Univ Technol Key Lab Autonomous Syst & Network Control Minist Educ China Guangzhou 510641 Guangdong Peoples R China Univ Sharjah Coll Comp & Informat Sharjah 27272 U Arab Emirates Univ Jordan King Abdullah II Sch Informat Technol Amman 11942 Jordan Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Sichuan Univ Coll Comp Sci Chengdu 610041 Peoples R China Sichuan Normal Univ Coll Comp Sci Chengdu 610068 Peoples R China

Charging scheduling plays a significant role in wireless rechargeable sensor networks (WRSNs), which benefit from stable and reliable energy supplements via wireless charging. This paper proposes an importance-different charging scheduling (IDCS) strategy for improving charging utility as well as reducing the data loss. The unique feature of IDCS is that, it distinguishes nodes by means of different importance of data delivery. The Matroid theory is used to achieve our goals. First, two important factors are determined in the Matroid model, i.e., the deadline of the task and the penalty value of the task. Moreover, a greedy algorithm of task classification is designed to minimize the data loss. All tasks are divided into the early tasks and the delayed tasks, and the node with greater importance and shorter deadline has a higher priority of being included into the early tasks. In addition, a charging sequence adjustment approach is proposed to maximize the charging utility. This approach aims to exchange the sequence of different nodes in the trajectory of the mobile charger for exploring a shorter path. Several simulations verified the effectiveness and advantages of our charging scheduling strategy in terms of the node failure rate and total data loss.

关键词： Task analysis Wireless sensor networks Wireless communication Trajectory Robot sensing systems Optimal scheduling Vehicle dynamics Wireless rechargeable sensor network mobile charging charging scheduling Matroid theory data loss

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Bioinspired control design using cerebellar model articulation controller network for omnidirectional mobile robots

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ADVANCES IN MECHANICAL ENGINEERING 2018年第8期10卷

作者： Jiang, Yiming Yang, Chenguang Wang, Min Wang, Ning Liu, Xiaofeng South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Network Control Minist Educ Guangzhou Guangdong Peoples R China Univ Plymouth Ctr Robot & Neural Syst Plymouth Devon England Hohai Univ Coll Internet Things Engn Changzhou Peoples R China

As a learning mechanism that emulates the structure of the cerebellum, cerebellar model articulation controllers have been widely adopted in the control of robotic systems because of the fast learning ability and simple computational structure. In this article, a cerebellar model articulation controller-based neural network controller is developed for an omnidirectional mobile robot. With the powerful learning ability of cerebellar model articulation controller, a cerebellar model articulation controller neural network is constructed to learn the complex dynamics of the omnidirectional mobile robot such that the robot is controlled without a priori knowledge of the robot dynamics. In addition, to overcome the limitation of the neural network controller, a global control technique with a group of smooth switching functions is designed such that the global ultimately uniformly boundedness of cerebellar model articulation controller is achieved instead of conventional semi-global ultimately uniformly boundedness. Moreover, smooth decreasing boundary functions are synthesized into the controller to guarantee the transient control performance. Based on an omnidirectional mobile robot, numerical experiments have been conducted to demonstrate the effectiveness of the proposed cerebellar model articulation controller controller.

关键词： Omnidirectional wheeled mobile robot cerebellar model articulation controller global neural control barrier Lyapunov function

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A novel hybrid neural network-based SOH and RUL estimation method for lithium-ion batteries

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JOURNAL OF ENERGY STORAGE 2024年 98卷

作者： Chen, Baoliang Liu, Yonggui Xiao, Bin South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Network Control Minist Educ Guangzhou 510641 Peoples R China Guangzhou Vocat Coll Technol & Business Informat Engn Sch Guangzhou 511442 Peoples R China Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou 510006 Peoples R China

The long-term usage of lithium-ion batteries (LiBs) easily results in battery aging and poses safety risks. Effectively assessing the state of health (SOH) and the remaining useful life (RUL) is crucial for the management of LiBs. In this paper a novel hybrid neural network is designed for accurate estimation of SOH and RUL, which integrates multi-channel parallel structure of convolutional neural network (CNN), bidirectional structure of long short-term memory (LSTM) and gated recurrent unit (GRU), and attention mechanism. To make prediction more accurate, the outlier removal is firstly performed according to the Pauta criterion, which can ensure smoother decay trajectory of capacity while preserving valuable information. Then, multiple health features are extracted from battery charging process, followed by the correlation analysis to evaluate the relevance between health features and capacity degradation. Additionally, an ablation study is performed for multi- feature selection. To verify the effectiveness and superior performance of the proposed method in predicting battery SOH and RUL, comparative tests are conducted at different starting points. The experimental results demonstrate that the proposed hybrid multi-channel method outperforms existing methods, confirming its exceptional predictive accuracy and strong generalization capability compared to the single-channel variant of the proposed method, CNN and bidirectional LSTM.

关键词： Lithium-ion batteries State of health Remaining useful life Convolutional neural network Long short-term memory Gated recurrent unit

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A varying-parameter complementary neural network for multi-robot tracking and formation via model predictive control

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NEUROCOMPUTING 2024年 609卷

作者： Li, Xingru Ren, Xiaohui Zhang, Zhijun Guo, Jinjia Luo, Yamei Mai, Jiajie Liao, Bolin South China Univ Technol Sch Automat Sci & Engn Guangzhou 510640 Peoples R China South China Univ Technol Sch Automat Sci & Engn Key Lab Autonomous Syst & Network Control Minist Educ Guangzhou 510640 Peoples R China Inst Super Robot Huangpu Guangzhou 510555 Peoples R China Nanchang Univ Jiangxi Thousand Talents Plan Nanchang 330031 Peoples R China Jishou Univ Coll Comp Sci & Engn Jishou 416000 Peoples R China Pazhou Lab Guangdong Artificial Intelligence & Digital Econ Guangzhou 510335 Peoples R China Shaanxi Univ Technol Sch Mech Engn Shaanxi Prov Key Lab Ind Automat Hanzhong 723001 Peoples R China Changsha Normal Univ Sch Informat Sci & Engn Changsha 410100 Peoples R China Guangdong Univ Petrochem Technol Inst Artificial Intelligence & Automat Sch Automat Sci & Engn Maoming 525000 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China

In this paper, a varying-parameter complementary neural network (VPCNN) is designed and combined with model predictive control (MPC) to solve the multi-robot tracking and formation problems via a leader-follower strategy. First, multi-robot tracking and formation problems are transformed into quadratic programming (QP) problems employing an MPC approach. Second, a nonlinear complementary function approach, i.e., the Fischer-Burmeister function, is used to map the Karush-Kuhn-Tucker conditions of the QP problem with double-ended inequality constraints to a system of nonlinear equations. Finally, the VPCNN is designed to solve the multi-robot tracking and formation problem. The effectiveness of the proposed method is demonstrated by numerical simulations, and the advantage of VPCNN in terms of solution speed is indicated by comparisons with a primal-dual neural network.

关键词： Neural networks Multi-robot system Tracking and formation problems Model predictive control

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Adaptive control of Unmanned Surface Vessels Matching an Optimized Reference Model

Adaptive Control of Unmanned Surface Vessels Matching an Opt...

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2014 IEEE International Conference on Robotics and Biomimetics (ROBIO)

作者： Yang, Chenguang Cui, Rongxin Li, Zhijun Sharma, Sanjay Zhao, Xingang South China Univ Technol Sch Automat Sci & Engn MOE Key Lab Autonomous Syst & Network Control Guangzhou 510641 Guangdong Peoples R China Univ Plymouth Ctr Robot & Neural Syst Plymouth PL4 8AA Devon England Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Univ Plymouth Sch Marine Sci & Engn Plymouth PL4 8AA Devon England Chinese Acad Sci Shenyang Inst Automat Shenyang 110016 Peoples R China

ISBN: (纸本)9781479973972

关键词： Unmanned surface vehicles

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End-to-End AI-Based Point-of-Care Diagnosis system for Classifying Respiratory Illnesses and Early Detection of COVID-19

arXiv

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arXiv 2020年

作者： Belkacem, Abdelkader Nasreddine Ouhbi, Sofia Lakas, Abderrahmane Benkhelifa, Elhadj Chen, Chao Department of Computer and Network Engineering CIT UAEU UAE Department of Computer Science and Software Engineering CIT UAEU UAE Cloud Computing and Applications Reseach Lab Staffordshire University United Kingdom Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China

Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty breathing. However, early diagnosis of the type of the virus, can be crucial, especially in cases such as the recent COVID-19 pandemic. One of the factors that contributed to the spread of the pandemic, was the late diagnosis or confusing it with regular flu-like symptoms. Science has proved that one of the possible differentiators of the underlying causes of these different respiratory diseases is coughing, which comes in different types and forms. Therefore, a reliable lab-free tool for early and more accurate diagnosis that can differentiate between different respiratory diseases is very much needed. This paper proposes an end-to-end portable system that can record data from patients with symptom, including coughs (voluntary or involuntary) and translate them into health data for diagnosis, and with the aid of machine learning, classify them into different respiratory illnesses, including COVID-19. With the ongoing efforts to stop the spread of the COVID-19 disease everywhere today, and against similar diseases in the future, our proposed low cost and user-friendly solution can play an important part in the early diagnosis. Copyright © 2020, The Authors. All rights reserved.

关键词： Diagnosis

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Channel Resource Scheduling for Stringent Demand of Emergency Data Transmission in WBANs

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第4期20卷 2341-2352页

作者： Liang, Baowen Liu, Xuxun Zhou, Huan Leung, Victor C. M. Liu, Anfeng Chi, Kaikai South China Univ Technol Coll Elect & Informat Engn Guangzhou 510641 Peoples R China South China Univ Technol Key Lab Autonomous Syst & Network Control Minist Educ China Guangzhou 510641 Peoples R China China Three Gorges Univ Coll Comp & Informat Technol Yichang 443002 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Cent South Univ Sch Informat Sci & Engn Changsha 410083 Peoples R China Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou 310023 Peoples R China

Media access control (MAC) plays a pivotal role in ensuring proper operation in wireless body area networks (WBANs). However, current solutions still cannot satisfy the stringent requirements of low power and low delay for emergency data reporting. In this paper, we propose an energy-efficient and emergency-aware MAC (EEEA-MAC) protocol for meeting such a rigorous demand. First, we design a node-different channel access scheme, in which source nodes use the CSMA/CA pattern while relay nodes adopt the hybrid CSMA/CA-TDMA pattern. Second, we devise an emergency-first time-slot allocation scheme, in which channel sensing is performed and the emergency data is handled by relay nodes according to different cases. EEEA-MAC has two striking features. One is that, source nodes adopt the CSMA/CA scheme instead of the conventional CSMA/CA-TDMA scheme, ensuring the requirement because there are almost no collisions and no confirmation messages in this scheme. The other is that, relay nodes use a sensing-based emergency data handling mechanism instead of the traditional empty-slot occupying mechanism, further guaranteeing the requirement owing to the immediate handling of emergency data and the short time of channel sensing. Extensive simulations demonstrate the advantages of EEEA-MAC in terms of energy dissipation and latency.

关键词： Media Access Protocol Wireless communication Delays Relays Wireless sensor networks Sensors Protocols WBAN MAC emergency data energy delay

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