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Consistent Extended Kalman Filter Design for Maneuvering Target Tracking and Its Application on Hand Position Tracking

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Guidance, Navigation and control 2022年第2期2卷 33-58页

作者： Lin Tian Yang Xu Wenchao Xue Long Cheng The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190P.R.China School of Arti-cial Intelligence University of Chinese Academy of SciencesBeijing 100049P.R.China The Key Laboratory of Systems and Control National Center for Mathematics and Interdisciplinary SciencesAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190P.R.China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049P.R.China

This paper propees the consistent extended Kalman flter(CEKF)for the maneuvering target tracking(MTT)with nonlinear uncertain dynamics,and applies it on hand poition *** general modlel of the MTT system i presented with unmodleled dynamics in terms of nonlinear unknown function of *** CEKF is propoeed to ensure that the bounds of the estimation error's covariance matrix are av ailable through the flter *** a result,the creponding accuracy of the flter approach can be achieved ***,a CEKF-baaed MTT algorithm is constructed via the tumning aw of the critical parameter matrix QE Finally,the efectiveness of CEKF i verified by MTT numerical simulations and hand tacking expeiments under dilferent ***,two indices are employed to compare the CEKF with extended Kalman filter(EKF):the mean square errors(MSEa)and the bounded percentage,ie the percentage of the rang w bere the estimation error is encboed by the bound calculated by *** MSEs of CEKF are smaller than thoee of EKF,where the worst MSEa of CEKF and EKF are0.14 and 418 in the simulation,a8 well 80.11 and 059 in the expeiments,respectively;all bounded percentages of CEKF are larger than thoee of EKF,where the wonst average bounded percentages of CEKF and EKF ame 87.86%and 14.58%,8 well as 97.41%and 41.79%in the experiments,reapectively.

关键词： Maneuvering target tracking hand position tracking extended Kalman¯lter(EKF) consistency

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COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment-Based Topic Modeling

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JOURNAL OF MEDICAL INTERNET RESEARCH 2022年第2期24卷 e31726页

作者： Huangfu, Luwen Mo, Yiwen Zhang, Peijie Zeng, Daniel Dajun He, Saike San Diego State Univ Fowler Coll Business San Diego CA 92182 USA San Diego State Univ Ctr Human Dynam Mobile Age San Diego CA 92182 USA Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst 95 Zhongguancun East Rd Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

Background: COVID-19 vaccines are one of the most effective preventive strategies for containing the pandemic. Having a better understanding of the public's conceptions of COVID-19 vaccines may aid in the effort to promptly and thoroughly vaccinate the community. However, because no empirical research has yet fully explored the public's vaccine awareness through sentiment-based topic modeling, little is known about the evolution of public attitude since the rollout of COVID-19 vaccines. Objective: In this study, we specifically focused on tweets about COVID-19 vaccines (Pfizer, Moderna, AstraZeneca, and Johnson & Johnson) after vaccines became publicly available. We aimed to explore the overall sentiments and topics of tweets about COVID-19 vaccines, as well as how such sentiments and main concerns evolved. Methods: We collected 1, 122, 139 tweets related to COVID-19 vaccines from December 14, 2020, to April 30, 2021, using Twitter's application programming interface. We removed retweets and duplicate tweets to avoid data redundancy, which resulted in 857, 128 tweets. We then applied sentiment-based topic modeling by using the compound score to determine sentiment polarity and the coherence score to determine the optimal topic number for different sentiment polarity categories. Finally, we calculated the topic distribution to illustrate the topic evolution of main concerns. Results: Overall, 398, 661 (46.51%) were positive, 204, 084 (23.81%) were negative, 245,976 (28.70%) were neutral, 6899 (0.80%) were highly positive, and 1508 (0.18%) were highly negative sentiments. The main topics of positive and highly positive tweets were planning for getting vaccination (251, 979/405, 560, 62.13%), getting vaccination (76,029/405,560, 18.75%), and vaccine information and knowledge (21,127/405, 560, 5.21%). The main concerns in negative and highly negative tweets were vaccine hesitancy (115, 206/205, 592, 56.04%), extreme side effects of the vaccines (19,690/205,592, 9.58%),

关键词： COVID-19 COVID-19 vaccine sentiment evolution topic modeling social media text mining

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Nearly optimal stabilization of unknown continuous-time nonlinear systems: A new parallel control approach

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NEUROCOMPUTING 2024年 578卷

作者： Lu, Jingwei Wang, Xingxia Wei, Qinglai Wang, Fei-Yue Tsinghua Univ Dept Ind Engn Beijing 100084 Peoples R China Qingdao Acad Intelligent Ind Qingdao 266114 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This paper develops a novel online nearly optimal control (ONOC) method for unknown continuous -time (CT) nonaffine nonlinear systems without recovering unknown systems. First, a dynamic control law is proposed for CT nonaffine nonlinear systems using parallel control. To achieve the proposed dynamic control law, an affine augmented system (AAS) is constructed according to the original system, and an augmented performance index (API) is constructed on the basis of the original performance index (OPI). Then, the stability relationship between the original system and the AAS is provided, and it is proven that, by selecting a suitable parameter in the API, optimal control of the AAS with the API is equivalent to near -optimal control of the original system with the OPI. Subsequently, based on the proposed dynamic control law, we extend integral reinforcement learning (IRL) to completely unknown CT nonaffine systems, and it is further proved that closed -loop signals are uniformly ultimately bounded (UUB) without the assumption that the input dynamics are bounded. Furthermore, the OPI can be set to an arbitrary positive -definite form, and the UUB bound for the state vector can be predetermined. Lastly, simulations are offered to exhibit the correctness of the developed ONOC method. Source code of this paper is available at: https://***/lujingweihh/Adaptive-dynamic-programmingalgorithms/tree/main/model_free_integral_reinforcement_learning.

关键词： Adaptive dynamic programming (ADP) Integral reinforcement learning (IRL) Nearly optimal control Nonaffine nonlinearity Parallel control Unknown nonlinear systems

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The Development of AgriVerse: Past, Present, and Future

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第6期53卷 3718-3727页

作者： Kang, Mengzhen Wang, Xiujuan Wang, Haoyu Hua, Jing de Reffye, Philippe Wang, Fei-Yue Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Beijing 100190 Peoples R China Univ Montpellier AMAP CIRAD CNRSINRAEIRD F-34398 Montpellier France Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Macao Inst Syst Engn Macau Peoples R China

Agricultural metaverse (AgriVerse) aims to optimize the production chain by saving costs, increasing efficiencies, and breaking information silos, in order to achieve sustainable agriculture. While AgriVerse is featured by the virtual-real interaction of the agriculture-related processes based on heterogeneous data, knowledge, and models, the link between AgriVerse and the intensively studied plant modeling is vague. This article presents briefly the research contents of plant modeling, analyzes the ongoing transition at the age of artificial intelligence (AI), and envisions future AgriVerse with the support of the agricultural foundation model, the decentralized agricultural organization (DAO) and the decentralized science (DeSci) of the plant model. Three AgriVerse application scenarios are presented. The opportunities and challenges of AgriVerse are discussed. This work is expected to identify the key research issues of AgriVerse and bring practitioners of diverse backgrounds together into the AgriVerse community.

关键词： Computational modeling Biological systems Biomass Biological system modeling Production Plants (biology) Metaverse Agricultural metaverse (AgriVerse) agriculture cyber-physical-social system (CPSS) artificial systems computational experiments and parallel execution (ACP) decentralized science (DeSci) knowledge-and-data driven modeling parallel agriculture plant model

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Iterative assist-as-needed control with interaction factor for rehabilitation robots

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Science China(Technological Sciences) 2021年第4期64卷 836-846页

作者： CAO Ran CHENG Long YANG ChenGuang DONG Zhe School of Artificial Intelligence University'of Chinese Academy of SciencesBeijing 100049China State Key Laboratory for Management and Control of Complex Systems Institute of Automation.Chinese Academy of SciencesBeijing 100190China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China School of Electrical and Control Engineering North China Universityof TechnologyBeijing 100144China

Rehabilitation robots for stroke patients have drawn considerable attention because they can reduce the economic and labor costs brought by traditional rehabilitation. control methods for rehabilitation robots have been developed to stimulate the active motion of patients and to improve the effectiveness of rehabilitation care. However, current control methods can only roughly adjust the system’s stiffness and may fail in achieving satisfactory performance. To this end, this paper introduces a novel cost function consisting of the tracking error term and the stiffness term. The cost function contains an interaction factor that represents the patient’s motion intention to balance the weight of these two terms. When the patients try to actively do training tasks, the weight of stiffness term increases, which leads to the larger allowable tracking error and lower stiffness eventually. An iterative updating law of the stiffness matrix is given to reduce the proposed cost function. Theoretical analysis based on the Lyapunov theory is given to ensure the feasibility of the proposed algorithm. Furthermore, a force estimation is used to improve interaction control performance. Finally, simulation experiments are provided to show the effectiveness of the proposed algorithm.

关键词： human-robot interaction impedance stiffness control rehabilitation

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Automatic 3D Seam Extraction Method for Welding Robot Based on Monocular Structured Light

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IEEE SENSORS JOURNAL 2021年第14期21卷 16359-16370页

作者： Lu, Zhenfeng Fan, Junfeng Hou, Zhanxin Deng, Sai Zhou, Chao Jing, Fengshui China Univ Min & Technol Sch Mech Elect & Informat Engn Dept Mech Engn Beijing 100083 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The teaching programming mode and offline programming mode of welding robot are essential parts in today's manufacturing industry. However, these modes can't meet the automation requirements and adaptive ability of welding robot. To achieve 3D path acquisition of weld seam for robot autonomous programming, a fast and accurate offline 3D seam extraction method is proposed based on monocular structured light sensor. In this method, gray code and phase-shift code raster images are projected to the workpiece, and the 3D coordinates of the weld are calculated by collecting the workpiece images with raster patterns. The path fitting of robot is completed by polynomial fitting method. The novel monocular structured light sensor used by this paper has compact structure and small volume, and can adapt to a variety of welding working scenes. The combination of gray code and phase shift code makes the measurement accurate and fast, and achieves the effect of full resolution decoding. The experimental results show that the proposed method can complete the task of 3D weld extraction and path fitting, which has the characteristics of high efficiency and strong robustness.

关键词： Structured light weld extraction visual sensor 3D reconstruction path model

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LILAC: Learning a Leader for Cooperative Reinforcement Learning

LILAC: Learning a Leader for Cooperative Reinforcement Learn...

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IEEE Conference on Games (CoG)

作者： Fu, Yuqian Chai, Jiajun Zhu, Yuanheng Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Wuhan Univ Elect Informat Sch Wuhan Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (数字)9781665459891

ISBN: (纸本)9781665459891

In cooperative multi-agent reinforcement learning, role-based learning promises to reach satisfactory policy learning through the decomposition of complicated tasks using roles. Different roles are responsible for different aspects of the task. However, how this group of roles can be quickly identified is not clear. To address this problem, we propose a novel framework, LearnIng a LeAder for Cooperative reinforcement learning (LILAC), which introduces a leader to integrate information to assign roles. Leaders take a broad view of the whole task and feed the integrated information into a Gaussian mixture model to sample role embedding distribution. It enables LILAC to assign appropriate roles to different agents and improves cooperative performance. In order to evaluate the cooperation of multiple agents, a mixing network, inputted by individual local utility networks, is constructed to estimate the global action value. Two loss functions, temporal difference loss and mean divergence loss, are adopted by LILAC to learn network parameters and to encourage diversity of policies for different roles. By virtue of the leader module, LILAC outperforms the StarCraft II micromanagement benchmark in our experiments, especially on challenging tasks.

关键词： multi-agent reinforcement learning role-based method deep learning game AI

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Recent developments of the reconstruction in magnetic particle imaging

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Visual Computing for Industry,Biomedicine,and Art 2022年第1期5卷 290-302页

作者： Lin Yin Wei Li Yang Du Kun Wang Zhenyu Liu Hui Hui Jie Tian CAS Key Laboratory of Molecular Imaging the State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijing 100190China Beijing Key Laboratory of Molecular Imaging Beijing 100190China University of Chinese Academy of Sciences Beijing 100049China Medical Imaging Center the First Affiliated HospitalJinan UniversityGuangdong 510632China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of MedicineBeihang UniversityBeijing 100083China

Magnetic particle imaging(MPI)is an emerging molecular imaging technique with high sensitivity and temporal-spatial *** reconstruction is an important research topic in MPI,which converts an induced voltage signal into the image of superparamagnetic iron oxide particles concentration *** reconstruction primarily involves system matrix-and x-space-based *** this review,we provide a detailed overview of the research status and future research trends of these two *** addition,we review the application of deep learning methods in MPI reconstruction and the current open sources of ***,research opinions on MPI reconstruction are *** hope this review promotes the use of MPI in clinical applications.

关键词： Magnetic particle imaging Image reconstruction System matrix X-space

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A New Approach to Finite-Horizon Optimal control for Discrete-Time Affine Nonlinear systems via a Pseudolinear Method

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IEEE TRANSACTIONS ON AUTOMATIC control 2022年第5期67卷 2610-2617页

作者： Wei, Qinglai Zhu, Liao Li, Tao Liu, Derong Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China

In this article, a new time-varying adaptivedynamic programming (ADP) algorithm is developed to solve finite-horizon optimal control problems for a class of discrete-time affine nonlinear systems. Inspired by the pseudolinear method, the nonlinear system can be approximated by a series of time-varying linear systems. In each iteration of the time-varying ADP algorithm, the optimal control law for the time-varying linear system is obtained. For an arbitrary initial state, it is proven that states of the time-varying linear systems converge to the states of discrete-time affine nonlinear systems. It is also shown that the iterative value functions and the iterative control laws converge to the optimal value function and the optimal control law, respectively. Finally, numerical results are presented to verify the effectiveness of the present method.

关键词： Time-varying systems Nonlinear systems Optimal control Heuristic algorithms Dynamic programming Neural networks Linear systems Adaptive dynamic programming approximate dynamic programming finite horizon nonlinear systems optimal control pseudolinear approximation

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Data-driven adaptive-critic optimal output regulation towards water level control of boiler-turbine systems?

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EXPERT systems WITH APPLICATIONS 2022年 207卷

作者： Wei, Qinglai Wang, Xin Liu, Yu Xiong, Gang 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 Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau 999078 Peoples R China

This paper considers a new adaptive-critic optimal output regulation scheme for continuous-time general linear systems with unknown dynamics and unmeasurable disturbance. The objective of researching the optimal output regulation problem is pursuing the stability of the closed-loop system while enabling the output to optimally track the reference signal with disturbance rejection. The problem is solved by employing an approximate optimal regulator that consist of optimal feedback control obtained by a data-driven learning algorithm and optimal feedforward control achieved by solving regulator equation. The missing system matrices of the plant are exactly figured out by employing state/input data. Moreover, by employing the minimal polynomial of the exosystem matrix, the parameter of interference in the output equation is available. Then, a predefined cost function is presented based on the regulator equation that aims to design the optimal control law. The stability analysis demonstrate that the presented control scheme can stabilize the closed -loop system, meanwhile the output asymptotically tracks the reference signal. Finally, the effectiveness of the developed optimal control scheme is validated through an application towards the drum water level control of boiler-turbine systems.

关键词： Adaptivecriticdesigns Adaptivedynamicprogramming Outputregulation Unknowndynamicsystem Waterlevelcontrol Optimalcontrol

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