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Adaptive Fuzzy Asymptotic Tracking control of state-Constrained High-Order Nonlinear Time-Delay systems and Its Applications

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第3期52卷 1671-1680页

作者： Wu, You Xie, Xue-Jun Hou, Zeng-Guang Qufu Normal Univ Inst Automat Qufu 273165 Shandong Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This article discusses the adaptive fuzzy asymptotic tracking control for high-order nonlinear time-delay systems with full-state constraints. Fuzzy-logic systems and a separation principle are utilized to relax growth assumptions imposed on unknown nonlinearities. The adverse effect caused by unknown time delays is eliminated by choosing appropriate Lyapunov-Krasovskii functionals. By integrating nonlinear-transformed functions with a key coordinate transformation into the control design and constructing a specific compact set on the initial values of system states, the desired trajectory and parameter estimates, it is rigorously proved that all closed-loop signals are semiglobally bounded, the fuzzy approximation is valid, the full-state constraints are not violated without feasibility conditions on virtual controllers, and asymptotic tracking is achieved. The effectiveness and advantages of this control scheme are confirmed by two examples including a single-link robotic system.

关键词： Adaptive fuzzy asymptotic tracking control feasibility conditions full-state constraints high-order nonlinear time-delay systems

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What Does ChatGPT Say:The DAO from Algorithmic Intelligence to Linguistic Intelligence

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IEEE/CAA Journal of Automatica Sinica 2023年第3期10卷 575-579页

作者： Fei-Yue Wang Qinghai Miao Xuan Li Xingxia Wang Yilun Lin School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Virtual Reality Fundamental Research Laboratory Department of Mathematics and TheoriesPeng Cheng LaboratoryShenzhen 518000China Urban Computing Lab Department of Applied ResearchShanghai AI LaboratoryShanghai 200232China IEEE

THE well-known ancient Chinese philosopher Lao Tzu(老子)or Laozi(6th~4th century BC during the Spring and Autumn period)started his classic Tao Teh Ching《道德经》or Dao De Jing(see Fig.1)with six Chinese characters:"道(Dao)可(Ke)道(Dao)非(Fei)常(Chang)道(Dao)",which has been traditionally interpreted as“道可道,非常道”or"The Dao that can be spoken is not the eternal Dao".

关键词： DAO Intelligence Spring

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Shared control of Tendon-Driven Continuum Robots Using Visibility-Guaranteed Optimization for Endoscopic Surgery

IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS

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IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 2024年第2期6卷 487-497页

作者： Deng, Zhen Wei, Xiaoxiao Pan, Chuanchuan Li, Guotao Hu, Ying Fuzhou Univ Dept Mech Engn & Automat Fuzhou 350108 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518055 Peoples R China

Tendon-driven continuum robots (TDCRs) with mechanical compliance have gained popularity in natural orifice transluminal endoscopic surgery (NOTES). Teleoperation problems of the TDCRs involve performance objectives in addition to the visibility constraint. Handling the coupling between potentially conflicting objectives and the visibility constraint remains challenging for surgeons when operating TDCRs. This paper presents a shared control method to assist in the teleoperation of the TDCRs, which guarantees visual targets remain within the field of view (FoV) of the TDCR. The visibility constraint is explicitly defined using a zeroing control barrier function, which is specified in terms of the forward invariance of a visible set. To ensure accuracy, the Jacobian matrix of the system is approximated online using sensing data. Then, the visibility constraint, along with the robot's physical constraints, is integrated into a quadratic program (QP) framework. This allows for the optimization of the control input of the operator subject to constraints, thus preserving visibility. Finally, simulations and experiments were conducted to demonstrate the effectiveness of the proposed approach under two teleoperation modes. The results show that the proposed method achieved a reduction of approximately 70% in ITP and 43% in MAE compared to direct teleoperation.

关键词： Robotic endoscopic surgery continuum robot optimal visual control control barrier function

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Enhancing Representation Power of Deep Neural Networks With Negligible Parameter Growth for Industrial Applications

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2024年第11期54卷 6837-6848页

作者： Chen, Liangming Jin, Long Shang, Mingsheng Wang, Fei-Yue Chinese Acad Sci Chongqing Key Lab Big Data & Intelligent Comp Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China Univ Chinese Acad Sci Chongqing Sch Chongqing 400714 Peoples R China Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

In industrial applications where computational resources are finite and data noises are prevalent, the representation power of deep neural networks (DNNs) is crucial. Traditional network structures often require a significant increase in the parameter amount to enhance the representation power, making it difficult to achieve effective representation under parameter amount constraints. In order to alleviate this problem, this work leverages the ordinary differential equation (ODE) interpretation of deep residual networks, elucidating the relationship between the fine-grained connectivity modes of blocks in DNNs and the representation power. We build a bridge from the order of numerical methods and the order of ODEs to the representation power of DNNs. Besides, we show that higher-order ODEs can be approximated by k-step methods incorporating trainable coefficients. Empirically, we validate our theoretical insights by demonstrating the superior representation power of our proposed network structures through enhanced performance on industrial tasks, such as surface defect detection, critical temperature prediction of superconductors, and image classification under noises. The proposed method provides a new approach to the design of network structures for robust and accurate DNNs, enhancing the representation power with a negligible number of additional parameters. The code is publicly available at https://***/LongJin-lab/Order-and-Representation-Power.

关键词： Residual neural networks Vectors Noise Biological neural networks Medical services Defect detection Agriculture Deep neural networks (DNNs) industrial applications ordinary differential equation (ODE) representation power

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Dependency-Aware Vehicular Task Scheduling Policy for Tracking Service VEC Networks

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第3期8卷 2400-2414页

作者： Li, Chao Liu, Fagui Wang, Bin Chen, C. L. Philip Tang, Xuhao Jiang, Jun Liu, Jie South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Peng Cheng Lab Cyberspace Secur Res Ctr Shenzhen 518066 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100080 Peoples R China

In this paper, we study a tracking service vehicular edge computing (VEC) network that provides computation offloading service for Intelligent vehicles, where computational tasks with different urgency and dependency are required to be completed efficiently within strict time constraints. We consider the actual scenario where the environmental parameters fluctuate randomly and their distributions are unknown, thus, a long-term scheduling policy optimization problem needs to be solved. For this motivation, we first define a queueing criterion to sort the subtasks into a scheduling queue, and then model a specific Markov decision process (MDP) according to the scheduling queue. Furthermore, we propose our vehicular task scheduling policy optimizing (VTSPO) algorithm based on the most advanced policy-based deep reinforcement learning (DRL). The experimental results compared with known value-based DRL algorithms verify the advantages of the proposed VTSPO algorithm.

关键词： Task analysis Intelligent vehicles Optimization Processor scheduling Vehicle dynamics Heuristic algorithms Costs Deep reinforcement learning (DRL) scheduling policy tracking service vehicular edge computing (VEC)

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Dynamic Multiaction Recognition and Expert Movement Mapping for Closed Pelvic Reduction

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第8期19卷 8667-8678页

作者： Pan, Ming-Zhang Deng, Ya-Wen Li, Zhen Chen, Yuan Liao, Xiao-Lan Bian, Gui-Bin Guangxi Univ Sch Mech Engn Nanning 530004 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 200092 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Pelvic fractures are one of the most serious traumas in orthopedic care, and reduction during routine surgery is a significant challenge. Because there are so many vital organs, blood vessels, and nerves around the pelvis, and the reduction force is large, the operational requirements for the surgeon are extremely strict and require extensive experience and surgical skills. This article proposes a method for collecting and digitizing doctors' reduction movements, which aims to help intelligent devices recognize surgeons' reduction actions and provides a means to learn from expert experience to improve the accuracy of surgery. First, the convolutional bidirectional long short-term memory algorithm with multilayer cross-fused features is proposed. It extracts time and spatial correlations between multimodal data in a hierarchical manner. Second, discrete dynamic motion primitives are adopted for mapping the surgeon's palm movement trajectory. Finally, this article constructs a data acquisition platform and collects data from surgeons with varying proficiency in closed reduction. Experiment results show that the closed reduction action recognition accuracy is 99% and posture recognition accuracy is 95.5%. The recognition algorithm proposed by this article is significantly higher than the commonly used algorithms in terms of Accuracy, Precision, Recall, and F1-Score. This article provides methods and means for the digitization of surgical expertise and transfers learning for robot-assisted surgery.

关键词： Action recognition conv-BiLSTM closed pelvic reduction dynamic movement primitives multimodal data fusion surgeon movement mapping

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Deep Learning-based Moving Object Segmentation:Recent Progress and Research Prospects

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Machine Intelligence Research 2023年第3期20卷 335-369页

作者： Rui Jiang Ruixiang Zhu Hu Su Yinlin Li Yuan Xie Wei Zou College of Information Engineering Shanghai Maritime UniversityShanghai201306China School of Computer Science and Technology East China Normal UniversityShanghai200062China Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing100190China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing100190China

Moving object segmentation(MOS),aiming at segmenting moving objects from video frames,is an important and challenging task in computer vision and with various *** the development of deep learning(DL),MOS has also entered the era of deep models toward spatiotemporal feature *** paper aims to provide the latest review of recent DL-based MOS methods proposed during the past three ***,we present a more up-to-date categorization based on model characteristics,then compare and discuss each category from feature learning(FL),and model training and evaluation *** FL,the methods reviewed are divided into three types:spatial FL,temporal FL,and spatiotemporal FL,then analyzed from input and model architectures aspects,three input types,and four typical preprocessing subnetworks are *** terms of training,we discuss ideas for enhancing model *** terms of evaluation,based on a previous categorization of scene dependent evaluation and scene independent evaluation,and combined with whether used videos are recorded with static or moving cameras,we further provide four subdivided evaluation setups and analyze that of reviewed *** also show performance comparisons of some reviewed MOS methods and analyze the advantages and disadvantages of reviewed MOS methods in terms of ***,based on the above comparisons and discussions,we present research prospects and future directions.

关键词： Moving object segmentation(MOS) change detection background subtraction deep learning(DL) video understanding

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Hybrid neural network-aided strong wind speed prediction along rail network

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JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS 2024年 252卷

作者： Liu, Yuhang Zhang, Zhipeng Huang, Yujie Zhao, Wenqiang Dai, Lei Shanghai Jiao Tong Univ Sch Ocean & Civil Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ State Key Lab Ocean Engn Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Urban Complex Risk Control & Resilience Go Shanghai Emergency Management Shanghai 200240 Peoples R China Zhejiang Commun Investment Grp Co Ltd Hangzhou 310000 Peoples R China

With the advent of networked rail operations and increasing risks caused by extreme weather conditions, there is an urgent need to enhance the safety early warning and emergency protection for rail under extreme wind environments. By deploying anemometers along the railway and monitoring the surrounding wind speeds, dispatchers can proactively take protective measures to mitigate the impact of severe weather on rail transportation. In particular, accurate time-series forecasting during periods when wind speeds exceed the threshold is critical for railway anomaly monitoring. This study employs Extreme Value Theory (EVT) to determine the threshold for strong winds. In order to identify high wind speeds, memory networks are used to memorize strong wind events in historical data. By combining the Gated Recurrent Unit (GRU) module with the attention mechanism, this study ultimately develops a hybrid neural network model in the prediction of wind speed. Based on real-world datasets of wind speeds along the Hangzhou-Haining intercity railway and along the Norway rail network via meteorological stations, the effectiveness of the hybrid neural network model has been verified by comparing base models. The proposed strong wind speed prediction has practical applications in enhancing early warning and rail safety under complex wind climate.

关键词： Strong wind Rail safety Prediction Neural network Attention mechanism

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Event-Triggered Near-Optimal control for Unknown Discrete-Time Nonlinear systems Using Parallel control

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第3期53卷 1890-1904页

作者： Lu, Jingwei Wei, Qinglai Zhou, Tianmin Wang, Ziyang Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

This article uses parallel control to investigate the problem of event-triggered near-optimal control (ETNOC) for unknown discrete-time (DT) nonlinear systems. First, to achieve parallel control, an augmented nonlinear system (ANS) with an augmented performance index (API) is proposed to introduce the control input into the feedback system. The control stability relationship between the ANS and the original system is analyzed, and it is shown that, by choosing a proper API, optimal control of the ANS with the API can be seen as near-optimal control of the original system with the original performance index (OPI). Second, based on parallel control, a novel event-triggered scheme is proposed, and then a novel ETNOC method is developed using the time-triggered optimal value function of the ANS with the API. The control stability is proved, and an upper bound, which is related to the design parameter, is provided for the actual performance index in advance. Then, to implement the developed ETNOC method for unknown DT nonlinear systems, a novel online learning algorithm is developed without reconstructing unknown systems, and neural network (NN) and adaptive dynamic programming (ADP) techniques are employed in the developed algorithm. The convergence of the signals in the closed-loop system (CLS) is shown using the Lyapunov approach, and the assumption of boundedness of input dynamics is not required. Finally, two simulations justify the theoretical conjectures.

关键词： Nonlinear systems Optimal control control systems Performance analysis Stability criteria Iterative algorithms Heuristic algorithms Adaptive dynamic programming (ADP) event-triggered control near-optimal control parallel control reinforcement learning (RL) unknown nonlinear systems

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Completely Stealthy FDI Attack Against state Estimation in Networked control systems

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IEEE TRANSACTIONS ON CIRCUITS AND systems II-EXPRESS BRIEFS 2023年第3期70卷 1114-1118页

作者： Liu, Yifa Cheng, Long Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Peng Cheng Lab Dept Math & Theories Shenzhen 518055 Peoples R China

An effective false data injection attack should cause enough damage to the system while avoiding being spotted by detection methods. This brief proposes an extremely stealthy attack strategy, which can make the residual almost always unchanged to invalidate both the widely used chi(2) detector and the advanced summation detector and can eventually cause unbound state deviations. Compared with the existing attack methods, the proposed attack strategy can reduce the sum of cumulative residual increments by 85.39% while causing 9817001 times of the state deviations in the numerical simulations.

关键词： Detectors Sensors Networked control systems Symbols Observers Mathematical models Estimation error False data injection attack estimation complete stealthiness detection networked control systems

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