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第43届中国控制会议

作者： Yiqiang Zhang Tao Zhang Yong Li Hualiang Zhang Guopeng Zhang Hao Wang School of Electrical Engineering Shenyang University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The traditional finite set model predictive control has the advantages of fast response and strong robustness. The threevector model predictive current control(TV-MPCC) improves the problem of large current ripple based on single-vector model predictive current control(MPCC) and can realize deadbeat current control. Still, it cannot avoid the influence of parameter disturbance. To solve this problem, this paper constructs an ultra-local model based on the input and output information of the motor and designs an adaptive sliding mode observer to estimate the unknown part of the model. Finally, the designed threevector model-free predictive current control with adaptive sliding mode observer(TV-MFPCC-ASMO) is simulated and compared with the traditional three-vector model predictive control. It has been proved that the proposed method can effectively suppress the system disturbance when the motor parameters are mismatched and improve the motor's steady-state and dynamic performance during operation.

关键词： Permanent magnet synchronous motor Three-vector model predictive control Ultra-local model Adaptive sliding mode observer

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Robust Tracking for Electromagnetic-Actuated Microrobot Via Sliding Mode Control

Robust Tracking for Electromagnetic-Actuated Microrobot Via ...

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Annual Conference of Industrial Electronics Society

作者： Yihang Wu Ziyao Qu Xiaocong Chang Zhijian Hu Rongni Yang Renjie Ma State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Control Science and Engineering Shandong University Jinan China

Microrobots can work in small, enclosed spaces and complete complex tasks. It has potential applications in the field of biomedical and life sciences. This paper focuses on investigating robust dynamic tracking for electromagnetic-actuated microrobot via sliding mode control. First, we generalize the planar motion to the case of spatial trajectory tracking and then establish the three-dimensional tracking error model of microrobot. Then, we deploy a novel sliding control strategy and ensure the stability of sliding mode dynamics, once the robotic state reaches the sliding surface and sustains there in the sequel. Finally, the numerical simulation is conducted, compared to the traditional proportion-integration-differentiation control strategy, to verify the effectiveness and advantages of our proposed control scheme.

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Modeling and Planar Trajectory Tracking Control of an MJ-AUV

Modeling and Planar Trajectory Tracking Control of an MJ-AUV

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Chinese Control Conference (CCC)

作者： Zakarya Omar Ke-Xian Liu Chao Ren Qing-Hao Meng Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin China Faculty of Engineering Sana'a University Sana'a Yemen

A Multi-Joint Autonomous Underwater Vehicle (MJ-AUV) is designed and equipped with multiple movable segments or joints that allow for greater maneuverability and flexibility in the water. These joints allow the MJ-AUV to change its shape, orientation, and direction of movement, so it can access tight spaces or navigate in complex underwater environments, making it useful for a variety of tasks in the deep sea, such as surveillance, exploration, and maintenance. However, the multiple joint structure of the MJ-AUV complicates the dynamics of the system. Furthermore, the joints' rotation causes misalignment of the cabins, causing deviations in the desired forces and torques. To address these issues, this study focuses on the 2-dimensional (2D) modeling as well as the trajectory tracking controller of the MJ-AUV. In this setting, the proposed controller utilizes a hierarchically structured robust nonlinear back-stepping and sliding-mode approach. Hence, this controller is simulated under various uncertainties and disturbances and the results show that it presents a global finite-time convergence to a bounded neighborhood of the origin for both position and velocity tracking errors, demonstrating its effectiveness and robustness.

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Reprojection methods for Koopman-based modelling and prediction

arXiv

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arXiv 2023年

作者： van Goor, Pieter Mahony, Robert Schaller, Manuel Worthmann, Karl Systems Theory and Robotics Group School of Engineering Australian National University Canberra Australia Optimization-based Control Group Institute of Mathematics Technische Universität Ilmenau Germany

Extended Dynamic Mode Decomposition (eDMD) is a powerful tool to generate data-driven surrogate models for the prediction and control of nonlinear dynamical systems in the Koopman framework. In eDMD a compression of the lifted system dynamics on the space spanned by finitely many observables is computed, in which the original space is embedded as a low-dimensional manifold. While this manifold is invariant for the infinite-dimensional Koopman operator, this invariance is typically not preserved for its eDMD-based approximation. Hence, an additional (re-)projection step is often tacitly incorporated to improve the prediction capability. We propose a novel framework for consistent reprojectors respecting the underlying manifold structure. Further, we present a new geometric reprojector based on maximum-likelihood arguments, which significantly enhances the approximation accuracy and preserves known finite-data error bounds. © 2023, CC BY-NC-ND.

关键词： Dynamic mode decomposition

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Comparison of Facial Expression Sensors and Skin Potential Response Sensors for Emotion Estimation by Communication Robots

Comparison of Facial Expression Sensors and Skin Potential R...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Naoya Hasegawa Rin Hirahara Yoshihiko Takahashi Department of Comprehensive Engineering - Mechanical Systems Course Kindai University Technical College Nabari Mie Japan Department of Robotics and Mechatronics Kanagawa Institute of Technology Atsugi Kanagawa Japan

A communication system that quantifies human emotions and provides feedback control was fabricated for a communication robot. The robot can provide suggestions to users and relay transmissions during human-to-human communication. For instance, if the user is excited, the robot can calm them down, and if the user is sad, the robot can cheer them up. The reliability in estimating emotions from facial expressions of a proposed sensor was compared with that of galvanic skin reflex (GSR) and skin potential response (SPR) sensors, which have been reported in several papers. A GSR sensor can measure tension and agitation in humans. The sensor estimating emotions from facial expressions can measure five facial expressions: “Happiness”, “Surprise”, “Anger”, “Sadness”, and “Neutrality”. Furthermore, the method is non-contact and suitable for practical use because it uses images. As the emotion-estimated values are output, they can be used without any privacy issues. The changes in emotions toward subjects using an entertainment robot that can eject soap bubbles were evaluated. An experimental chamber was created to prevent disturbances and measure only the effects of the soap bubble ejection and cessation events. This study reports that SPR sensors and emotion estimation sensors can be handled similarly.

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Compensation Control of PMSMs Based on a High-order Sliding Mode Observer with Inertia Identification

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IEEE Journal of Emerging and Selected Topics in Power Electronics 2025年

作者： Li, Ke Ding, Jie Sun, Xiaodong Lei, Gang Dianov, Anton Demidova, Galina Prakht, Vladimir Jiangsu University School of Electrical and Information Engineering Zhenjiang212013 China Jiangsu University Automotive Engineering Research Institute Zhenjiang212013 China University of Technology Sydney School of Electrical and Data Engineering NSW2007 Australia Wenzhou University Pingyang Institute of Intelligent Manufacturing Wenzhou325035 China ITMO University Faculty of Control Systems and Robotics Petersburg197101 Russia Ural Federal University Department of Electrical Engineering Ekaterinburg620062 Russia

This article proposes a new sliding mode control (NSMC) reaching law to improve the speed tracking and anti-interference performance of permanent magnet synchronous motors (PMSMs). The NSMC improves the sliding mode control (SMC) in approaching speed and reducing chattering. Firstly, considering that the external load interference will adversely affect the control system, a high-order sliding mode observer (HOSMO) is designed to observe the load torque, and the observed value is compensated to the output of the speed controller. Then, combined with the Landau algorithm, a variable adaptive gain algorithm is introduced to decrease the impact of inertia mismatch on the system control performance. The algorithm identifies the rotational inertia online, reduces the effect of load disturbance on the identification performance, and updates the identification results to the observation in processors and controllers. Experimental results show that the proposed control method reduces the speed and current fluctuations of the system when load torque and inertia vary and improves the system's robustness. © 2013 IEEE.

关键词： Permanent magnets

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IMPS: Informative Map Point Selection for Visual-Inertial SLAM

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 5595-5605页

作者： Changxiang Liu Hongshan Yu Qiang Fu Xieyuanli Chen Naveed Akhtar Zhi-Hong Mao College of Electrical and Information Engineering School of Robotics Quanzhou Institute of Industrial Design and Machine Intelligence Innovation Hunan University Changsha China National University of Defense Technology Changsha China School of Computing and Information Systems University of Melbourne Melbourne VIC Australia Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA USA

Visual-inertial SLAM (VINS) stands at the forefront of advancements in computer vision, robotics, and autonomous driving, revolutionizing the way we perceive and navigate the world. The typical approach in optimization-based methods for VINS is to estimate camera poses by minimizing the reprojection errors of all corresponding map points. However, not all map points are suitable for accurate pose estimation. Some map points introduce noise and can compromise the accuracy of the estimation. Current methods primarily focus on removing 2D noise points, which proves to be ineffective since triangulation can increase the uncertainty of map points. To address this problem, we propose a new method for Informative Map Point Selection (IMPS) in VINS systems. IMPS identifies the most informative map points by utilizing mutual and geometric information as the optimization target. We integrate IMPS into a VINS system and evaluate its performance using publicly available EuRoC, TUM and M2DGR datasets, as well as our own data. Experimental results demonstrate that our method surpasses existing VINS methods and achieves state-of-the-art pose estimation performance. Importantly, IMPS functions as an independent module with strong generalization capabilities, allowing for easy integration into other VINS systems and enhancing pose estimation performance. This integration not only elevates pose estimation accuracy but also holds great promise for advancing applications in intelligent driving and unmanned systems.

关键词： Simultaneous localization and mapping Optimization Cameras Pose estimation Robots Mutual information Intelligent vehicles

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Adapt-FuseNet: Context-aware Multimodal Adaptive Fusion of Face and Gait Features using Attention Techniques for Human Identification

Adapt-FuseNet: Context-aware Multimodal Adaptive Fusion of F...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Ashwin Prakash S Thejaswin Athira Nambiar Alexandre Bernardino Department of Computational Intelligence Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

Biometrics plays a significant role in vision-based surveillance applications. Soft biometrics such as gait is widely used with face in surveillance tasks like person recognition and re-identification. Nevertheless, in practical scenarios, classical fusion techniques respond poorly to the changes in individual users, external environment and varying contexts such as viewpoints. To this end, we propose a novel context-aware adaptive multi-biometric fusion strategy viz., ‘Adapt-FuseNet’ for the dynamic incorporation of gait and face biometric cues leveraging attention techniques. In particular, we investigate the impact of attention models such as parallel co-attention & keyless attention, along with various fusion strategies such as naïve fusion & adaptive fusion for human identification. Extensive experiments are carried out on two publically available large gait datasets i.e. CASIA-A and CASIA-B. Results show the superior performance of our proposed context-aware adaptive fusion model compared with the state-of-the-art models.

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Detecting Worker Attention Lapses in Human-Robot Interaction: An Eye Tracking and Multimodal Sensing Study 28

Detecting Worker Attention Lapses in Human-Robot Interaction...

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28th International Conference on Automation and Computing, ICAC 2023

作者： Dai, Zhuangzhuang Park, Jinha Kaszowska, Aleksandra Li, Chen School of Engineering and Applied Science Aston University Dept. of Computer Science Engineering and Physical Science BirminghamB4 7ET United Kingdom The Maersk Mc Kinney Moller Institute University of Southern Denmark Sdu Robotics Odense5230 Denmark Aalborg University Department of Electronic Systems Aalborg9220 Denmark Aalborg University The Department of Materials and Production Aalborg9220 Denmark

ISBN: (纸本)9798350335859

The advent of industrial robotics and autonomous systems endow human-robot collaboration in a massive scale. However, current industrial robots are restrained in co-working with human in close proximity due to inability of interpreting human agents' attention. Human attention study is non-trivial since it involves multiple aspects of the mind: perception, memory, problem solving, and consciousness. Human attention lapses are particularly problematic and potentially catastrophic in industrial workplace, from assembling electronics to oper-ating machines. Attention is indeed complex and cannot be easily measured with single-modality sensors. Eye state, head pose, posture, and manifold environment stimulus could all play a part in attention lapses. To this end, we propose a pipeline to annotate multimodal dataset of human attention tracking, including eye tracking, fixation detection, third-person surveil-lance camera, and sound. We produce a pilot dataset containing two fully annotated phone assembly sequences in a realistic manufacturing environment. We evaluate existing fatigue and drowsiness prediction methods for attention lapse detection. Experimental results show that human attention lapses in production scenarios are more subtle and imperceptible than well-studied fatigue and drowsiness. © 2023 IEEE.

关键词： Eye tracking

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Robust Finite-Time Containment of Networked Heterogeneous Nonlinear systems with Intermittent Measurement Only

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IEEE Transactions on Network Science and engineering 2025年

作者： Tian, Biao Zhang, Hao Cui, Peiyu Wang, Zhuping Yan, Huaicheng Shanghai Key Laboratory of Wearable Robotics and Human-Machine Interaction Tongji University College of Electronic and Information Engineering Department of Control Science and Engineering Shanghai201804 China Ministry of Education Shanghai Institute of Intelligent Science and Technology National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems China East China University of Science and Technology School of Information Science and Engineering Shanghai200237 China

The robust finite-time containment problem of fully heterogeneous multiagent systems with uncertainties is often challenging, especially when only intermittent output measurement is used. To address the issue posed by the nonidentical system dynamics of multiple leaders, which makes the traditional distributed estimator form for the convex hull inapplicable, a distributed finite-time estimator is constructed for each follower to extract the system matrices and states of leaders. Then, in the output-triggering setting, a finite-time extended state observer driven by intermittent output measurement is developed to reconstruct the uncertainties and unmeasurable states of followers. Meanwhile, the non-continuous output measurement will lead to the differentiation of virtual control laws undefined. To solve this issue, a filtering compensation scheme-based finitetime controller via the backstepping technique is developed for nonlinear followers, ensuring practical finite-time stability (PFTS) of the closed-loop system. It is shown that the proposed algorithm steers each follower into the preset convex combination spanned by the positions of multiple leaders. The capability of the exploited control protocol is verified through simulations and experiments. 2013 IEEE.

关键词： Backstepping

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