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第三十九届中国控制会议

作者： Lu Cheng Yin-Bo Liu Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and ControlSchool of Electrical and Information EngineeringTianjin University

To detect parts per billion（ppb）-level volatile organic compounds（VOCs） in automobiles, a pre-concentrator is generally needed for electronic noses（e-noses）, which can enrich ppb-level concentration to parts per million（ppm） level. However, the current e-nose chamber structure could dilute the pre-concentrated gases and then the detection performance is reduced. To solve this problem, a novel chamber design was proposed in this paper. The core design idea was to reduce the space for the gas flow in the chamber, taking into account the shape and size of the gas sensors. Specially, for the pin-type gas sensors, a series of cylindrical structure design was adopted, and for the micro-electro-mechanical system（MEMS） patch-type sensors, the headspace was compressed. The results of computational fluid dynamics（CFD） based simulation showed that the concentration of pre-concentrated gases in the novel chamber was higher than that of the traditional chamber. The designed chambers were manufactured using polylactic acid（PLA） material by 3D printing. Experimental gases were prepared from polyurethane（PU） leather and polyvinyl chloride（PVC） leather to verify the performance of the chambers, and the results demonstrated that the proposed new e-nose chamber was superior to the traditional one in terms of the pre-concentration effect, repeatability and classification performance.

关键词： e-nose chamber pre-concentrator parts per billion（ppb） volatile organic compounds（VOCs） repeatability classification

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Attitude Adjustment of a Multi-joint AUV Using an Improved LADRC

Attitude Adjustment of a Multi-joint AUV Using an Improved L...

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第三十九届中国控制会议

作者： Lin Yu Ke-Xian Liu Qing-Hao Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and ControlSchool of Electrical and Information Engineering Tianjin University

To achieve vertical profile exploration of deep-sea hybrid structures, a multi-joint autonomous underwater vehicle(MJ-AUV) with the orthogonal joints structure was designed, and its virtual prototype was built using Automatic Dynamic Analysis of Mechanical systems(ADAMS). This paper focuses on the attitude control of the designed MJ-AUV which is a nonlinear system with strong coupling, big hysteresis, large inertia, and time-varying disturbance. According to the special structure of the MJ-AUV, an error based variable gain linear active disturbance rejection controller(EVG-LADRC) was proposed and compared with the traditional proportional-integral-derivative(PID) controller and LADRC. Simulation results demonstrated that EVG-LADRC outperformed the PID and LADRC in terms of rapidity, anti-interference, and accuracy.

关键词： MJ-AUV virtual prototype attitude control EVG-LADRC

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Modeling of Shape Memory Alloy Actuated System Using a Modified Rate-dependent Prandtl-Ishlinskii Hysteresis Model

Modeling of Shape Memory Alloy Actuated System Using a Modif...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Mingwei Liang Ying Feng School of Automation Science and Engineering Key Laboratory of Autonomous Systems and Networked Control South China University of Technology Guangzhou China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

Shape memory alloys (SMAs) are widely used as actuators to construct various robotics systems, such as, minimally invasive surgery robots, micro in-pipe robots and biomimetic micro robots. However, the nonlinear hysteresis behaviors in SMA actuators seriously degrade the control accuracy and desired performances of SMA-actuated control systems. In this paper, an experimental platform based on SMA actuator is conducted. To make the platform capable of achieving precise control, the complex hysteresis characteristics of the SMA actuators are studied. The SMA actuator in this study exhibits complicated nonlinear properties with saturation nonlinearity and asymmetric hysteresis influenced by both the duty cycle of the input voltage and the load mass, which are different from the classical rate-dependent hysteresis behaviors. To explore the nonlinear properties of the SMA actuated system, open-loop experiments under different duty cycles and loads are testified, and a modified Rate-dependent Prandtl-Ishlinskii (RDPI) model is developed to enhance modeling abilities. Fitting effects of the modified RDPI model is validated by the comparison between the modeling output and experimental data.

关键词： Hysteresis Load modeling Robots Actuators Wires Parameter estimation Heat transfer

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Adaptive Backstepping control for Piezoelectric Micro Actuating System with Input Saturation

Adaptive Backstepping Control for Piezoelectric Micro Actuat...

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International Conference on Advanced Robotics and Mechatronics (ICARM)

作者： Ying Li Ying Feng School of Automation Science and Engineering Key Laboratory of Autonomous Systems and Networked Control South China University of Technology Guangzhou China

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

Piezoelectric actuators have been attached more importance in the field of high-precision micro actuating due to their excellent performance. However, the presence of the hysteresis nonlinearity and input saturation problems in this smart material actuators have caused adverse effects on their performance, including reduced operational accuracy and damage to the actuators. To eliminate the negative effects of hysteresis and compensate for input saturation, an adaptive backstepping controller with an anti-windup auxiliary compensation system is developed in this paper. The proposed approach shows good tracking performance for the piezoelectric micro actuating system introducing, which also proves that the anti-windup technology can handle the input saturation effects well.

关键词： Hysteresis Piezoelectric actuators Adaptation models Backstepping Adaptive systems

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FD-SLAM: Real-time tracking and mapping in dynamic environments

FD-SLAM: Real-time tracking and mapping in dynamic environme...

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2019 IEEE International Conference on Unmanned systems and Artificial Intelligence, ICUSAI 2019

作者： Xu, Hua Yang, Chenguang Feng, Ying Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9781728158594

A robot needs the ability of Simultaneous Localization and Mapping(SLAM) in an unknown environment. It can help the robot navigation and tracking. The traditional method only considers that the robot works in a static environment. But in most cases, the robot should work in a highly dynamic environment like people. This paper presents a novel SLAM system named FD-SLAM, building on ORB-SLAM2 [1], which adds the abilities of dynamic object detection and fusion of depth image and semantic image to better estimate the position of people in an image. To improve the real-time performance of FD-SLAM, it has five threads that run simultaneously in FD-SLAM: tracking, semantic segmentation, local mapping, loop closing, and point cloud mapping. FD-SLAM uses the deep neural network algorithm to detect the dynamic object and combine the depth image with the corresponding semantic image to obtain a more accurate position of a dynamic object thus reducing the impact of dynamic objects to more accurately estimate the pose of the camera. Finally, the point cloud thread uses each keyframe to create a point cloud map. We experiment with a public dataset. The results show that the absolute trajectory error of FD-SLAM is smaller than that of ORB-SLAM and DS-SLAM, and it has better real-time performance than other SLAM systems for a dynamic environment. © 2019 IEEE.

关键词： Semantics

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Correction: A scale-adaptive estimation for mixed geographically and temporally weighted regression models

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Journal of Geographical systems 2025年第2期27卷 337-337页

作者： Hong, Zhimin Wang, Zhiwen Wang, Huhu Wang, Ruoxuan Department of Mathematics School of Science Inner Mongolia University of Technology Hohhot China Statistics Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Hohhot China Institute for Infectious Disease and Endemic Disease Control Inner Mongolia Autonomous Region Center for Disease Control and Prevention Hohhot China

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Observability of Switched Boolean control networks via Set controllability

Observability of Switched Boolean Control Networks via Set C...

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IEEE Conference on Decision and control

作者： Xiao Zhang Min Meng Yuanhua Wang Daizhan Cheng National Center for Mathematics and Inter-Disciplinary Sciences & the Key Laboratory of Systems and Control Academy of Mathematics and Systems Sciences Chinese Academy of Sciences Beijing P.R. China Tongji University Shanghai P. R. China Shanghai Research Institute of Intelligent Autonomous Systems Tongji University Shanghai P.R. China Business School Shandong Normal University Jinan P.R. China Key Laboratory of Systems and Control Academy of Mathematics and Systems Sciences Chinese Academy of Sciences Beijing P.R. China

ISBN: (纸本)9781665436601

The paper considers observability of switched Boolean control networks (SBCNs), for two different observability definitions. First, utilizing the semi-tensor product (STP) method, a coupled system based on the initial SBCN is constructed. Then the observability conditions corresponding to diverse definitions are converted into the corresponding set controllability goals for the coupled system. By adopting the set controllability technique of SBCNs, easily verified conditions for observability of SBCNs are obtained. Finally, an example is provided to demonstrate the effectiveness of the proposed results.

关键词： Conferences Switches controllability Observability

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Heatmap-based vanishing point boosts lane detection

arXiv

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arXiv 2020年

作者： Liu, Yin-Bo Zeng, Ming Meng, Qing-Hao Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Process Measurement and Control School of Electrical and Information Engineering Tianjin University China

Vision-based lane detection (LD) is a key part of autonomous driving technology, and it is also a challenging problem. As one of the important constraints of scene composition, vanishing point (VP) may provide a useful clue for lane detection. In this paper, we proposed a new multi-task fusion network architecture for high-precision lane detection. Firstly, the ERFNet was used as the backbone to extract the hierarchical features of the road image. Then, the lanes were detected using image segmentation. Finally, combining the output of lane detection and the hierarchical features extracted by the backbone, the lane VP was predicted using heatmap regression. The proposed fusion strategy was tested using the public CULane dataset. The experimental results suggest that the lane detection accuracy of our method outperforms those of state-of-the-art (SOTA) *** Codes 68T45 (Primary) 68T07 (Secondary) Copyright © 2020, The Authors. All rights reserved.

关键词： network architecture

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Robust Beamforming for Localization-Aided Millimeter Wave Communication systems

arXiv

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arXiv 2022年

作者： Sun, Junchang Ma, Shuai Li, Shiyin Yang, Ruixin Min, Minghui Seco-Granados, Gonzalo The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The Department of Telecommunications and Systems Engineering Universitat Autònoma de Barcelona Barcelona08193 Spain The Engineering Research Center of Intelligent Control for Underground Space Ministry of Education China University of Mining and Technology Xuzhou221116 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Shanxi Key Laboratory of Information Communication Network and Security Xian University of Posts and Telecommunications Xian710121 China

In this letter, we investigate a robust beamforming problem for localization-aided millimeter wave (mmWave) communication systems. To handle this problem, we propose a novel restriction and relaxation (R&R) method. The proposed R&R method aims at minimizing the total transmit power while the positioning error follows a Gaussian distribution. Specifically, in the restriction phase of R&R, the probabilistic constraint is transformed into the deterministic form by using the Bernstein-type inequality. In the relaxation phase of R&R, the non-convex optimization problem is reformulated into a convex semidefinite program (SDP) by using semidefinite relaxation (SDR) and first-order Taylor expansion methods. To the best of our knowledge, we first consider the impact of the distribution of the positioning error on the channel state information (CSI), which further influences the data rate. Numerical results present the trade-off of the beamforming between the communication and positioning. © 2022, CC BY.

关键词： Beamforming

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Deep samplable observation model for global localization and kidnapping

arXiv

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arXiv 2020年

作者： Chen, Runjian Yin, Huan Jiao, Yanmei Dissanayake, Gamini Wang, Yue Xiong, Rong The State Key Laboratory of Industrial Control Technology and Institute of Cyber-Systems and Control Zhejiang University Zhejiang China The Center for Autonomous System University of Technology Sydney Australia

Global localization and kidnapping are two challenging problems in robot localization. The popular method, Monte Carlo Localization (MCL) addresses the problem by iteratively updating a set of particles with a "sampling-weighting" loop. Sampling is decisive to the performance of MCL [1]. However, traditional MCL can only sample from a uniform distribution over the state space. Although variants of MCL propose different sampling models, they fail to provide an accurate distribution or generalize across scenes. To better deal with these problems, we present a distribution proposal model named Deep Samplable Observation Model (DSOM). DSOM takes a map and a 2D laser scan as inputs and outputs a conditional multimodal probability distribution of the pose, making the samples more focusing on the regions with higher likelihood. With such samples, the convergence is expected to be more effective and efficient. Considering that the learning-based sampling model may fail to capture the accurate pose sometimes, we furthermore propose the Adaptive Mixture MCL (AdaM MCL), which deploys a trusty mechanism to adaptively select updating mode for each particle to tolerate this situation. Equipped with DSOM, AdaM MCL can achieve more accurate estimation, faster convergence and better scalability than previous methods in both synthetic and real scenes. Even in real environments with long-term changes, AdaM MCL is able to localize the robot using DSOM trained only by simulation observations from a SLAM map or a blueprint map. Source code for this paper is available here. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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