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LMI-based robust adaptive neural network control for Euler-Bernoulli beam with uncertain parameters and disturbances

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INTERNATIONAL JOURNAL OF control 2022年第1期95卷 1-10页

作者： Xing, Xueyan Yang, Hongjun Liu, Jinkun Wang, Shuquan Beihang Univ Sch Automat Sci & Elect Engn Beijing Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Technol & Engn Ctr Space Utilizat Beijing Peoples R China

This paper is concerned with the stabilisation problem of an Euler-Bernoulli beam with uncertain parameters and disturbances. To correctly represent the beam's behaviour, the partial differential equations model is utilised for the control design of the beam without missing any high-order mode information. Then the linear matrix inequalities (LMIs) method is applied to the robust adaptive neural network control design to cope with systematic uncertainties and stabilise the beam system with disturbance compensation. Through resolving LMIs, feasible sets of designed control parameters can be effectively obtained without model linearisation. Finally, numerical simulations are done to validate the effectiveness of the proposed control.

关键词： Distributed parameter systems boundary control LMIs robust adaptive neural network control

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Data cleaning and restoring method for vehicle battery big data platform

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APPLIED ENERGY 2022年 320卷

作者： Li, Shuangqi He, Hongwen Zhao, Pengfei Cheng, Shuang Beijing Inst Technol Sch Mech Engn Natl Engn Lab Elect Vehicles Beijing 100081 Peoples R China Univ Bath Dept Elect & Elect Engn Bath England Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Battery is one of the most important and costly devices in electric vehicles (EVs). Developing an efficient battery management method is of great significance to enhancing vehicle safety and economy. Recently developed bigdata and cloud platform computing technologies bring a bright perspective for efficient utilization and protection of vehicle batteries. However, a reliable data transmission network and a high-quality cloud battery dataset are indispensable to enable this *** paper makes the first effort to systematically solve data quality problems in cloud-based vehicle battery monitoring and management by developing a novel integrated battery data cleaning framework. In the first stage, the outlier samples are detected by analyzing the temporal features in the battery data time series. The outlier data in the dataset can be accurately detected to avoid their impacts on battery monitoring and management. Then, the abnormal samples, including the noise polluted data and missing value, are restored by a novel future fusion data restoring model. The real electric bus operation data collected by a cloud-based battery monitoring and management platform are used to verify the performance of the developed data cleaning method. More than 93.3% of outlier samples can be detected, and the data restoring error can be limited to 2.11%, which validates the effectiveness of the developed methods. The proposed data cleaning method provides an effective data quality assessment tool in cloud-based vehicle battery management, which can further boost the practical application of the vehicle big data platform and Internet of vehicle.

关键词： Big data Internet of vehicle Electric vehicles Data cleaning Battery management system Battery state estimation

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Rethinking Appearance-Based Deep Gait Recognition: Reviews, Analysis, and Insights From Gait Recognition Evolution

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2025年第6期36卷 9777-9797页

作者： Li, Jingqi Zhang, Yuzhen Zeng, Yi Ye, Changxin Xu, Wenzheng Ben, Xianye Wang, Fei-Yue Zhang, Junping Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Shandong Univ Shenzhen Res Inst Shenzhen 518063 Peoples R China Shandong Univ Sch Informat Sci & Engn Jinan 250100 Peoples R China Obuda Univ DeSci Ctr Parallel Intelligence H-1034 Budapest Hungary Macau Univ Sci & Technol Intelligent Syst Robot & Automat Lab Macau Peoples R China CASIA State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Gait recognition is a prominent biometric recognition technique extensively employed in public security. Appearance-based and model-based gait recognition are two categories of methods commonly used. Specifically, appearance-based methods, which use silhouettes to represent body information, typically outperform model-based methods that rely on skeleton data, making them more popular. Recently, the shift from single-frame templates to multiframe silhouettes has advanced appearance-based gait recognition with better spatiotemporal representation. However, there is a notable lack of comprehensive studies that deepen the understanding of multiframe appearance-based gait recognition methods. This article reviews various methods to trace the evolution of gait recognition. Furthermore, we unify various performant models in one framework, study the overlooked effects on data arrangement, and explore the scaling ability of existing methods. Besides the advancement in gait recognition, we also summarize the current challenges and future prospects to foster future research.

关键词： Gait recognition Feature extraction Data models Reviews Training Skeleton Shape Analytical models Deep learning Clothing Appearance-based biometric deep learning gait recognition GaitBase

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An FM*-Based Comprehensive Path Planning system for Robotic Floating Garbage Cleaning

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION systemS 2022年第12期23卷 23821-23830页

作者： Kong, Shihan Wu, Zhengxing Qiu, Changlin Tian, Manjun Yu, Junzhi Peking Univ Coll Engn Dept Adv Mfg & Robot BIC ESATState Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China 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 Minist Publ Secur PRC Res Inst 1 Beijing 100048 Peoples R China

A heuristic fast marching (FM*)-based comprehensive path planning system involving task allocation, initial planning, and replanning is presented for the robotic floating garbage cleaning mission. There are three primary contributions in this paper. First, to tackle the invalidation of the Euclidean distance metric in the obstacle environment, the task allocation is modeled as a travelling salesman problem (TSP) employing the FM*-based distance metric in order to obtain an optimal travel sequence. Second, to meet the maneuverability constraint from the surface robot and avoid the collision, a Gaussian filter is employed to adjust the curvature radius of the generated path. Third, for an efficient replanning, a neural network-based replanning point generator with the input of garbage movement vector is provided to strike a compromise for the distance cost and the computational burden. Moreover, a case study and a virtual obstacle experiment in the laboratory water tank demonstrate the feasibility of the proposed comprehensive path planning system. This work lays a firm foundation for the development of intelligent equipment for aquatic environment protection.

关键词： Task analysis Resource management Cleaning Path planning Robots Measurement Planning Autonomous vehicles path planning fast marching method task allocation replanning

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Drill the Cork of Information Bottleneck by Inputting the Most Important Data

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systemS 2022年第11期33卷 6360-6372页

作者： Peng, Xinyu Zhang, Jiawei Wang, Fei-Yue Li, Li Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100080 Peoples R China Tsinghua Univ BNRist Dept Automat Beijing 100084 Peoples R China

Deep learning has become the most powerful machine learning tool in the last decade. However, how to efficiently train deep neural networks remains to be thoroughly solved. The widely used minibatch stochastic gradient descent (SGD) still needs to be accelerated. As a promising tool to better understand the learning dynamic of minibatch SGD, the information bottleneck (IB) theory claims that the optimization process consists of an initial fitting phase and the following compression phase. Based on this principle, we further study typicality sampling, an efficient data selection method, and propose a new explanation of how it helps accelerate the training process of the deep networks. We show that the fitting phase depicted in the IB theory will be boosted with a high signal-to-noise ratio of gradient approximation if the typicality sampling is appropriately adopted. Furthermore, this finding also implies that the prior information of the training set is critical to the optimization process, and the better use of the most important data can help the information flow through the bottleneck faster. Both theoretical analysis and experimental results on synthetic and real-world datasets demonstrate our conclusions.

关键词： Training Signal to noise ratio Mutual information Optimization Convergence Deep learning Tools Information bottleneck (IB) theory machine learning minibatch stochastic gradient descent (SGD) typicality sampling

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A Spatiotemporal Neural Network Model for Estimated-Time-of-Arrival Prediction of Flights in a Terminal Maneuvering Area

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IEEE INTELLIGENT TRANSPORTATION systemS MAGAZINE 2023年第1期15卷 285-299页

作者： Ma, Yan Du, Wenbo Chen, Jun Thong, Yu Lv, Yisheng Cao, Xianbin Beihang Univ Key Lab Adv Technol Near Space Informat Syst Sch Elect & Informat Engn Beijing 100191 Peoples R China Queen Mary Univ London Sch Engn & Mat Sci London E1 4NS England Univ S Florida Dept Civil & Environm Engn Tampa FL 33620 USA Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China

Affected by the nondeterministic nature of flight trajectories, the external environment, and airport operational conditions, the prediction of the estimated time of arrival (ETA) is one of the most challenging tasks for air traffic control in the terminal maneuvering area (TMA). Previous studies lack adequate utilization of the spatial and temporal behaviors embedded in continuous trajectories. We propose a novel spatiotemporal neural network model for estimating the time of arrival (STNN-ETA), which consists of three components: 1) trajectory pattern recognition, which classifies historical trajectories into several patterns/clusters; 2) trajectory prediction, which predicts a target flight’s subsequent positions based on trajectory pattern matching; and 3) arrival time prediction, in which nonlinear function and recurrent units are adopted to capture spatiotemporal features for prediction purposes. In the proposed model, we also utilize spatial and temporal attention mechanisms to focus on important features from radar echo maps and trajectory series, respectively, and suppress unnecessary ones for ETA prediction. To validate the effectiveness of the proposed method, we apply it to predict the ETA of flights within the Beijing TMA. Extensive experiments show that STNN-ETA outperforms the state-of-the-art models in terms of the mean absolute error.

关键词： Trajectory Predictive models Boosting Atmospheric modeling Spatiotemporal phenomena Meteorology Prediction algorithms

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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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Learning strategies for underwater robot autonomous manipulation control

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JOURNAL OF THE FRANKLIN INSTITUTE 2024年第7期361卷

作者： Huang, Hai Jiang, Tao Zhang, Zongyu Sun, Yize Qin, Hongde Li, Xinyang Yang, Xu Harbin Engn Univ Natl Key Lab Sci & Technol Underwater Vehicle Harbin 150001 Peoples R China Inst Automat Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Autonomous manipulation operations represent the high intelligent coordination from robotic vision and control, it is also a symbol of the advances of robotic intelligence. The limitations of visual sensing and the increasingly complex experimental conditions make autonomous manipulation operations more difficult, particularly for deep reinforcement learning methods, which can enhance robotic control intelligence but require a lot of training process. Due to the highdimensional continuous state space and continuous action space characteristics of underwater operations, this paper adopts a policy -based reinforcement learning method as the foundational approach. To address the issues of instability and low convergence efficiency in traditional policybased reinforcement learning algorithms during the learning process, this paper proposes a novel policy learning method. This method adopts the Proximal Policy Optimization algorithm (PPO - Clip) and optimizes it through an actor -critic network. The aim is to improve the stability and effectiveness of convergence in the learning process. In the underwater training environment, a new reward shaping scheme has been designed to address the issue of reward sparsity during the training process. The manually crafted dense reward function is utilized as attractive and repulsive potential functions for goal manipulation and obstacle avoidance. On the highly complex underwater manipulation and training environment, transferred learning algorithm has been established to reduce the training times and compensate the differences between the simulation and experiment. Simulations and tank experiments have verified the performance of the proposed strategy learning method.

关键词： Underwater vehicle manipulator system Strategy learning Autonomous manipulation Deep reinforcement learning Transferred learning

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A Collision-Free Planning and control Framework for a Biomimetic Underwater Vehicle in Dynamic Environments

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2023年第3期28卷 1415-1424页

作者： Lv, Jiaqi Wang, Yu Wang, Shuo Bai, Xuejian Wang, Rui Tan, Min Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techno Shanghai 200031 Peoples R China

In this article, a collision-free planning and control framework for a biomimetic underwater vehicle (BUV) in dynamic environments is presented. It consists of obstacle avoidance planning, arctangent nonsingularity terminal sliding mode (ANTSM) control, and fuzzy inference. A fuzzy artificial potential field with a velocity component is designed for obstacle avoidance planning. An ANTSM controller with an arctangent function is proposed to guarantee a shorter convergence time of system states. The stability of the system is analyzed by the Lyapunov theory. A fuzzy inference module is given to construct the nonlinear relationship between the control parameters of the flippers and force/torque. Finally, comparative simulations, robot operating system-based simulations, and underwater obstacle avoidance experiments of the BUV in a swimming pool are conducted to validate the performance of the proposed collision-free planning and control algorithm.

关键词： Arctangent nonsingularity terminal sliding mode (ANTSM) control biomimetic underwater vehicle (BUV) collision-free in dynamic environments fuzzy artificial potential field (FAPF) planning

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Hierarchical Motion Learning for Goal-Oriented Movements With Speed-Accuracy Tradeoff of a Musculoskeletal system

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第11期52卷 11453-11466页

作者： Zhou, Junjie Zhong, Shanlin Wu, Wei Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Chinese Acad Sci Beijing Key Lab Res & Applicat Robot Intelligence Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Generating various goal-oriented movements via the flexible muscle model of the musculoskeletal system as fast and accurately as possible is a pressing problem, which is also the basis of most human adaptive behaviors, such as reaching, catching, interception, and pointing. This article focuses on the adaptive motion generation of fast goal-oriented motion on the musculoskeletal system by implementing the speed-accuracy tradeoff (SAT) in a hierarchical motion learning framework. First, we introduce Fitts' Law into the modified basal ganglia circuit-inspired iterative decision-making model for achieving dynamic and adaptive decision making. Then, as a time constraint, the decision is decomposed into a series of supervised terms by the proposed striatal FSI-SPN interneuron circuit-inspired velocity modulator to implement the tradeoff smoothly on the musculoskeletal system. Finally, an improved policy gradient algorithm is suggested to generate the muscle excitations of the modulated motion via the proposed muscle co-contraction policy, which promotes general cooperation between flexor and extensor muscles. In experiments, a redundant musculoskeletal arm model is trained to perform the adaptive quick pointing movements. By combining the muscle co-contraction policy with SAT, our algorithm shows the most efficient training and the best performance in the adaptive motion generation among the other three popular reinforcement learning algorithms on the musculoskeletal model.

关键词： Muscles Adaptation models Task analysis Arms Mathematical model Integrated circuit modeling Basal ganglia Brain-inspired decision making Fitts' law motion generation musculoskeletal system speed-accuracy tradeoff (SAT)

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