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Adaptive Uniform Performance control of Strict-Feedback Nonlinear systems With Time-Varying control Gain

IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 451-461页

作者： Kai Zhao Changyun Wen Yongduan Song Frank L.Lewis IEEE the Department of Electrical and Computer Engineering National University of SingaporeSingapore 119077Singapore the School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore 639798Singapore the State Key Laboratory of Power Transmission Equipment&System Security and New Technology Chongqing Key Laboratory of Intelligent Unmanned SystemsSchool of AutomationChongqing UniversityChongqing 400044China the UTA Research Institute University of Texas at ArlingtonFort WorthTX 76118 USA

In this paper,we present a novel adaptive performance control approach for strict-feedback nonparametric systems with unknown time-varying control coefficients,which mainly includes the following ***,by introducing several key transformation functions and selecting the initial value of the time-varying scaling function,the symmetric prescribed performance with global and semi-global properties can be handled uniformly,without the need for control ***,to handle the problem of unknown time-varying control coefficient with an unknown sign,we propose an enhanced Nussbaum function(ENF)bearing some unique properties and characteristics,with which the complex stability analysis based on specific Nussbaum functions as commonly used is no longer ***,by utilizing the core-function information technique,the nonparametric uncertainties in the system are gracefully handled so that no approximator is ***,simulation results verify the effectiveness and benefits of the approach.

关键词： Adaptive control enhanced Nussbaum function(ENF) strict-feedback systems unified prescribed performance

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Non-Rigid 3D Point Set Registration With Reliable Hybrid Mixture Model

Non-Rigid 3D Point Set Registration With Reliable Hybrid Mix...

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2023 IEEE International Conference on Robotics and Biomimetics, ROBIO 2023

作者： Huang, Haocheng Zhang, Zhengyan Min, Zhe Meng, Max Q.-H. Song, Shuang Wang, Jiaole Harbin Institute of Technology School of Mechanical Engineering and Automation Shenzhen518055 China Shandong University School of Control Science and Engineering Jinan250061 China Southern University of Science and Technology in Shenzhen Department of Electronic and Electrical Engineering China The Chinese University of Hong Kong Department of Electronic Engineering Hong Kong Shenzhen Research Institute of the Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9798350325706

Non-rigid point set registration is an essential technique in fields such as robotics, computer vision, image-guided surgery, and augmented reality. However, effectively addressing non-rigid deformations and handling noise interference remains a significant challenge in this context. This paper introduces a novel hybrid mixture model (HMM), combining a Gaussian mixture model (GMM) to describe positional features and a Fisher mixture model (FMM) to represent orientation features. This hybrid mixture model offers a solution to the non-rigid point set registration (PSR) problem. The proposed approach iteratively optimizes model parameters through the maximum expectation (EM) algorithm. It comprehensively explores registration performance, accounting for non-rigid deformations amidst isotropic and anisotropic positional noise. The proposed approach further incorporates reliable normal vectors for evaluating orientation features. Extensive experiments on non-rigid PSR demonstrates the improvements our algorithm offers in terms of both robustness and accuracy. © 2023 IEEE.

关键词： Nonrigid point set registration partial hybrid mixture model surgical navigation

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Asynchronous State Estimator Design for Markov Switched systems with Incomplete Probabilities and Redundant Channels 42

Asynchronous State Estimator Design for Markov Switched Syst...

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42nd Chinese control Conference, CCC 2023

作者： Chen, Haiyang Gao, Fangzheng Mao, Xueyi School of Control Science and Engineering Tiangong University Tianjin300387 China School of Automation Nanjing Institute of Technology Nanjing211167 China Nanjing Communications Institute of Technology Publicity Department Nanjing211188 China

ISBN: (纸本)9789887581543

This paper is concerned about the H∞ asynchronous state estimator design for Markov switched systems (MSSs) with incomplete transition probabilities and redundant channels. A hidden Markov chain (HMC) is introduced to characterize the asynchronous degree between the state estimator mode and the system mode. Firstly an asynchronous full-order state estimator is devised for the MSSs. Then, by Lyapunov functional and stochastic analysis, new criteria are derived such that the augmented system consisting of the target system and state estimator is H∞ stochastically finite-time boundedness. After that, with matrix decoupling technique, a design algorithm is obtained in form of linear matrix inequalities (LMIs). Finally, the practical one-link robotic manipulator is utilized to validate the developed results. © 2023 Technical Committee on control Theory, Chinese Association of automation.

关键词： Linear matrix inequalities

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Plant Disease Detection Using Machine Learning

Plant Disease Detection Using Machine Learning

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Intelligent Technologies (CONIT), International Conference on

作者： Amit Kumar Parida Albert Alexander Stonier Department of Control and Automation School of Electrical Engineering VIT University Vellore School of Electrical Engineering Vellore Institute of Technology Vellore

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Plant diseases are a serious threat toworld agriculture, resulting in lower agricultural yields and financial losses. This study examines how machine learning methods could revolutionize the identification of plant diseases. The shortcomings of conventional approaches and suggest combining machine learning models with cutting-edge imaging and data processing technologies to improve the diagnosis’s speed and precision. This research demonstrates how well machine learning methods, such as CNN and SVM, can distinguish between healthy and unhealthy plants. Also gone through how transfer learning can be used, especially when data is scarce, to modify previously trained models for illness detection.

关键词： Support vector machines Plant diseases Transfer learning Imaging Data processing Data models Agriculture

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Observation-Driven Multiple UAV Coordinated Standoff Target Tracking Based on Model Predictive control

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Tsinghua Science and Technology 2022年第6期27卷 948-963页

作者： Shun Sun Yu Liu Shaojun Guo Gang Li Xiaohu Yuan Department of Control Science and Technology Naval Aviation UniversityYantai 264001China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China National Institute of Defense Technology Innovation Academy of Military Sciences PLABeijing 100091China Department of Automation Tsinghua UniversityBeijing 100084China

An observation-driven method for coordinated standoff target tracking based on Model Predictive control(MPC)is proposed to improve observation of multiple Unmanned Aerial Vehicles(UAVs)while approaching or loitering over a *** acquiring a fusion estimate of the target state,each UAV locally measures the observation capability of the entire UAV system with the Fisher Information Matrix(FIM)determinant in the decentralized *** facilitate observation optimization,only the FIM determinant is adopted to derive the performance function and control constraints for coordinated standoff ***,a modified iterative scheme is introduced to improve the iterative efficiency,and a consistent circular direction control is established to maintain long-term observation performance when the UAV approaches its *** experiments with simulated and real trajectories validate that the proposed method can improve observation of the UAV system for target tracking and adaptively optimize UAV trajectories according to sensor performance and UAV-target geometry.

关键词： coordinated tracking standoff tracking observation-driven Model Predictive control(MPC) multiple UAVs Fisher Information Matrix(FIM)

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Distributed Nash equilibrium for pursuit-evasion game with one evader and multiple pursuers

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Science China(Information Sciences) 2025年

作者： Libing WANG Zhixin LIU Shuo YUAN Zhiqiang PU Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences Department of Electrical and Computer Engineering Wayne State University Institute of Automation Chinese Academy of Sciences

This paper investigates the pursuit-evasion problem involving one evader and multiple pursuers with limited sensing capability, where the evader tries to maximize the distance with the pursuers, while the pursuers have different objectives based on whether they can receive the information of the evader. The subgroup of pursuers who can observe the evader(called leaders) tries to be close to the evader, and the other subgroup of pursuers(called followers) tries to synchronize with their neighbors. When the subgraph formed by all leaders is complete, sufficient conditions are given to guarantee that the pursuers capture the evader and the pursuit-evasion game composed of the evader and leaders reaches Nash equilibrium. Furthermore, for the incomplete subgraph case, the distributed observers are proposed to estimate the relative positions between the evader and all leaders. It is shown that the distributed control strategy based on the observers converges exponentially to the Nash equilibrium solution, and makes the pursuers capture the evader. Finally, simulation examples are provided to verify the effectiveness of the proposed strategies.

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A novel feature enhancement module for small object detection

A novel feature enhancement module for small object detectio...

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2022 IEEE International Conference on Industrial Technology, ICIT 2022

作者： Wang, Ruogu Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (纸本)9781728119489

Generic object detection has made great progress in recent years, yet small object detection is still facing a serious problem of missed detection. The problem is caused by two reasons: low resolution in small targets and feature loss during deep convolution downsampling. Some previous studies used feature pyramid structures and multi-scale training predictions to alleviate this problem, but still cannot efficiently utilise the feature information of small targets. In this paper, firstly we propose a novel and effective feature enhancement module to extract more features;secondly, a filter size design method is presented to extract features of matching objects and incorporate context information;finally, an adaptive fusion method is used to process outputs of our module and improve model robustness. We select YOLOv3 as base model and carry out several experiments on a special Small Object Dataset and the results show that our work can achieve an increase of about seven percentage points (35.73%) compared to 28.78% on baseline. © 2022 IEEE.

关键词： Object detection

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Position Errors and Interference Prediction-Based Trajectory Tracking for Snake Robots

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1810-1821页

作者： Dongfang Li Yilong Zhang Ping Li Rob Law Zhengrong Xiang Xin Xu Limin Zhu Edmond Q.Wu IEEE School of Electrical Engineering and Automation Fuzhou UniversityFuzhou 350108 the Sino-US Global Logistics Institute Shanghai Jiao Tong UniversityShanghai 200240 the Key Laboratory for Agricultural Machinery Intelligent Control and Manufacturing of Fujian Education Institutions Fuzhou 350108China School of Biological Science and Medical Engineering and the Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education Beihang UniversityBeijing 100191China University of Macao Macao 999078China School of Automation Nanjing University of Science and TechnologyNanjing 210094China College of Intelligence Science and Technology National University of Defence TechnologyChangsha 410073China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

This work presents a trajectory tracking control method for snake *** method eliminates the influence of time-varying interferences on the body and reduces the offset error of a robot with a predetermined *** optimized line-of-sight(LOS)guidance strategy drives the robot’s steering angle to maintain its anti-sideslip ability by predicting position errors and ***,the predictions of system parameters and viscous friction coefficients can compensate for the joint torque control *** compensation is adopted to enhance the compatibility of a robot within ever-changing *** and experimental outcomes show that our work can decrease the fluctuation peak of the tracking errors,reduce adjustment time,and improve accuracy.

关键词： Anti-sideslip compensation snake robot trajectory tracking

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Simultaneous Estimation of Human Motion Intention and Time-Varying Arm Stiffness for Enhanced Human-Robot Interaction

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IEEE Transactions on Cognitive and Developmental systems 2024年第3期17卷 510-524页

作者： Wu, Huayang Zhu, Chengzhi Cheng, Long Yang, Chenguang Li, Yanan University of Sussex Department of Engineering and Design BrightonBN1 9RH United Kingdom South China University of Technology Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering Guangzhou510640 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China University of Liverpool Department of Computer Science LiverpoolL69 3BX United Kingdom

Recent advances in physiological human motor control research indicate that human endpoint stiffness magnitude increases linearly with grasp force. Based on these findings, a scheme was proposed in this paper to integrate the linear quadratic estimation (LQE) filter with the stiffness model inferred from grasp force, which can simultaneously estimate the human arm's stiffness and motion intention. Then, an online variable impedance controller (VIC) was designed based on previous estimations for physical human-robot interaction (pHRI). The proposed stiffness model and estimation method were validated through experiments using a planar robotic interface. In order to assess its performance in practical pHRI tasks, the implementation of human arm stiffness and intention estimation combining with VIC was extended to a teleoperation peg-in-hole and robot-assisted rehabilitation tasks. The experimental results demonstrate that the proposed method can effectively estimate human motion intention and arm stiffness simultaneously. Compared to existing methods, the proposed VIC enhances pHRI in terms of increased flexibility, effective guidance, and reduced human effort. © 2016 IEEE.

关键词： Human robot interaction

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Online Learning Enabled Hierarchical Distributionally Robust Model Predictive control of Green-Hydrogen Microgrids under Uncertainties 5

Online Learning Enabled Hierarchical Distributionally Robust...

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5th IEEE International Electrical and Energy Conference, CIEEC 2022

作者： Ning, Chao Li, Longyan Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665411042

ISBN: (纸本)9781665411042

This paper proposes a novel online-learning enabled hierarchical operation framework for a green-hydrogen microgrid (MG) with a high penetration of renewable energy (RE) and hybrid energy storage. The operation framework features a bi-level multi-timescale structure which integrates a day-ahead optimal scheduling with online learning distributionally robust model predictive control (OL-DRMPC). Day-ahead scheduling optimization is employed at the high level to provide MG energy dispatch references, while the OL-DRMPC is carried out in the low level for reference-tracking under uncertainties incurred by RE and load forecast errors. To confront the imperfect knowledge of forecast-error distributions, distributionally robust conditional value-at-risk constraints are imposed on MG system states, namely the storage of battery and hydrogen, for a family of distributions called ambiguity set. By exploiting a Dirichlet process mixture model, a streaming-data-driven ambiguity set for uncertain parameters is devised over the runtime of controller and flexibly captures multimodal structure and fine-grained moment information of each mixture component. Enabled by online learning, the proposed strategy enjoys the merit of offering a reliable and intelligent MG operation. Case studies are used to demonstrate its effectiveness and superiority. © 2022 IEEE.

关键词： Energy storage

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