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Proportional-Integral Observer-Based PID control of Positive Switched systems

Journal of systems Science & Complexity 2025年第3期 1047-1071页

作者： XING Wei ZHOU Xiaoyue ZHANG Junfeng JIA Xuan School of Information and Communication Engineering Hainan University Key Laboratory of System Control and Information Processing Ministry of Education School of Automation Hangzhou Dianzi University

This paper presents a new proportional-integral-derivative control approach for positive switched systems based on a positive proportional-integral observer. First, a positive proportional-integral observer is constructed. By using the observer state, the proportional-integral-derivative control and the corresponding integral part are designed, respectively. Using 1-norm inequality, two dynamic event-triggering conditions are established for the proportional-integral observer and proportional-integral-derivative controller, respectively. A dynamic event-triggered proportional-integral observer-based dynamic event-triggered proportional-integral-derivative controller is proposed by combining the sample state and the integral of the weighted sample output estimation error. Under the designed event-triggering conditions, an interval system with upper and lower bounds is introduced. The positivity and ?1-gain stability are achieved by realizing the according properties of the lower and upper bound systems in terms of multiple copositive Lyapunov function, respectively. All gain matrices are designed by a matrix decomposition approach and the corresponding conditions are solved by linear programming. Finally, two examples are provided to illustrate the validity of the results.

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A Method for Detecting the Reducers' Grease Failure Based on Vibration Signal Analysis

A Method for Detecting the Reducers' Grease Failure Based on...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Wang, Ditong Zhang, Hong Zheng, Ying Huazhong University of Science and Technology Belt and Road Joint Laboratory on Measurement and Control Technology Wuhan China Huazhong University of Science and Technology National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Wuhan China

ISBN: (纸本)9798350337754

Real time detection of the lubricating grease's effectiveness is the best method to solve wear faults, which can achieve the purpose of predictive maintenance and improve the service life of mechanical equipment. A new online detection method for gearbox lubricating grease failure is proposed to address the issues about long offline detection cycles and high labor costs associated with phased sampling analysis. Compared with online detection methods such as inductive and photoelectric, our method is difficult to detect the specific physicochemical parameters of the grease. However, it has the advantage of simple operation without touching the grease or changing the structure of the tested object. It only needs to attach the vibration sensor table to the mechanical arm reducer to measure and analyze the vibration signal. This article briefly analyzes the impact of grease failure on vibration signals, and then we introduce a novel approach that uses support vector machines along with feature extraction through wavelet packet decomposition for our detection methodology. Next transfer the commonly used spectral image recognition algorithms for temporal signal classification to achieve failed grease detection as a baseline and compare with our traditional machine learning methods. The experimental findings indicate that the recognition accuracy of both methods are above 90%, with traditional machine learning performing better on this task. © 2023 IEEE.

关键词： Support vector machines

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LHMap-loc: Cross-Modal Monocular Localization Using LiDAR Point Cloud Heat Map

LHMap-loc: Cross-Modal Monocular Localization Using LiDAR Po...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Xinrui Wu Jianbo Xu Puyuan Hu Guangming Wang Hesheng Wang Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China Department of Engineering University of Cambridge Cambridge U.K

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Localization using a monocular camera in the pre-built LiDAR point cloud map has drawn increasing attention in the field of autonomous driving and mobile robotics. However, there are still many challenges (e.g. difficulties of map storage, poor localization robustness in large scenes) in accurately and efficiently implementing cross-modal localization. To solve these problems, a novel pipeline termed LHMap-loc is proposed, which achieves accurate and efficient monocular localization in LiDAR maps. Firstly, feature encoding is carried out on the original LiDAR point cloud map by generating offline heat point clouds, by which the size of the original LiDAR map is compressed. Then, an end-to-end online pose regression network is designed based on optical flow estimation and spatial attention to achieve real-time monocular visual localization in a pre-built map. In addition, a series of experiments have been conducted to prove the effectiveness of the proposed method. Our code is available at: https://***/IRMVLab/LHMap-loc.

关键词： Location awareness Point cloud compression Visualization Laser radar Accuracy Robot vision systems Real-time systems

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K-BMPC: Derivative-based Koopman Bilinear Model Predictive control For Tractor-trailer Trajectory Tracking With Unknown Parameters

K-BMPC: Derivative-based Koopman Bilinear Model Predictive C...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zehao Wang Han Zhang Jingchuan Wang Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Nonlinear dynamics bring difficulties to controller design for control-affine systems such as tractor-trailer vehicles, especially when the parameters in the dynamics are unknown. To address this constraint, we propose a derivative-based lifting function construction method, show that the corresponding infinite dimensional Koopman bilinear model over the lifting function is equivalent to the original control-affine system. Further, we analyze the propagation and bounds of state prediction errors caused by the truncation in derivative order. The identified finite dimensional Koopman bilinear model would serve as predictive model in the next step. Koopman Bilinear Model Predictive control (K-BMPC) is proposed to solve the trajectory tracking problem. We linearize the bilinear model around the estimation of the lifted state and control input. Then the bilinear Model Predictive control problem is approximated by a quadratic programming problem. Further, the estimation is updated at each iteration until the convergence is reached. Moreover, we implement our algorithm on a tractor-trailer system, taking into account the longitudinal and side slip effects. The open-loop simulation shows the proposed Koopman bilinear model captures the dynamics with unknown parameters and has good prediction performance. Closed-loop tracking results show the proposed K-BMPC exhibits elevated tracking precision with the commendable computational efficiency. The experimental results demonstrate the feasibility of K-BMPC.

关键词： Trajectory tracking Computational modeling Estimation Predictive models control systems Data models Nonlinear dynamical systems

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Data selection strategy in data-driven model predictive control of uncertain systems

Data selection strategy in data-driven model predictive cont...

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第34届中国过程控制会议

作者： Lingyi Yang dewei Li Department of Automation Shanghai Jiao Tong UniversityKey Laboratory of System Control and Information ProcessingMinistry of Education of ChinaShanghai Engineering Research Center of Intelligent Control and Management

In this paper,a novel data-driven methodology for MPC with data selection strategies is *** novel selection strategy involves two ***,we check the similarity between the current data and the past data items already existing in the data *** enables the controller to pick a set of informative data items which contain sufficient information on each direction of the signal ***,for the data items within the data oracle,we integrate multiple similar data vectors into a single vector to cut down amount of *** simulation examples displays the upgraded performance and smaller amount of calculation of selection strategies.

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A Real-time Local Planner Design for Smart Wheelchairs Based on Koopman Bilinear Model Predictive control

A Real-time Local Planner Design for Smart Wheelchairs Based...

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IEEE International Conference on Real-time Computing and Robotics (RCAR)

作者： Guanfeng Yu Han Zhang Zehao Wang Jingchuan Wang Department of Automation Institute of Medical Robotics 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 Shanghai China

ISBN: (数字)9798350372601

ISBN: (纸本)9798350372618

The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming to achieve superior real-time performance. In particular, we employ the Extended Dynamic Mode Decomposition (EDMD) method to approximate the infinite dimensional Koopman operator. To tackle the challenge of designing lifting functions, we systematically utilize the derivatives of known nonlinear structures to methodically derive lifting functions. This systematic approach facilitates the identification of a precise bilinear model. Next, we formulate the corresponding Koopman bilinear MPC problem. Finally, an iterative algorithm is employed to efficiently solve the KBMPC problem. This method linearizes the bilinear model around estimations and iteratively solves the resulting quadratic programming problem to refine estimations. This allows KBMPC to function as a real-time local planner for smart wheelchairs. The prediction of nonlinear terms shows that the identified Koopman bilinear model increases the prediction accuracy. Furthermore, experiments demonstrate the advantages of the designed KBMPC local planner, highlighting its superior real-time performance and predictive capabilities.

关键词： Pedestrians Accuracy systematics Wheelchairs Estimation Predictive models Real-time systems

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Spatial Dynamics of Chickenpox Outbreaks in Rapidly Developing Regions:Implications for Global Public Health

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Biomedical and Environmental Sciences 2024年第7期37卷 687-697页

作者： Li Wang Miaomiao Wang Chengdong Xu Peihan Wang Meiying You Zihan Li Xinmei Chen Xinyu Liu Xudong Li Yuanyuan Wang Yuehua Hu Dapeng Yin College of Geography and Environmental Science Henan UniversityKaifeng 475001HenanChina State Key Laboratory of Resources and Environmental Information System Institute of Geographic Science and Natural Resource ResearchChinese Academy of SciencesBeijing 100101BeijingChina Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions(Henan University) Ministry of EducationKaifeng 475001HenanChina Office of Epidemiology Chinese Center for Disease Control and PreventionBeijing 102206BeijingChina University of Chinese Academy of Sciences(UCAS) Beijing 100101BeijingChina Weifang Municipal Center for Disease Control and Prevention Weifang 261061ShandongChina Hainan Center for Disease Control and Prevention Haikou 570203HainanChina

Objective The occurrence of chickenpox in rapidly developing areas poses substantial seasonal risk to ***,certain factors influencing local chickenpox outbreaks have not been ***,we examined the relationship between spatial clustering,heterogeneity of chickenpox outbreaks,and socioeconomic factors in Southern *** We assessed chickenpox outbreak data from Southern China between 2006 and 2021,comprising both relatively fast-growing parts and slower sub-regions,and provides a representative sample of many developing *** analyzed the spatial clustering attributes associated with chickenpox outbreaks using Moran’s I and local indicators of spatial association and quantified their socioeconomic determinants using Geodetector q *** There were significant spatial heterogeneity in the risk of chickenpox outbreaks,with strong correlations between chickenpox risk and various factors,particularly demographics and living ***,interactive effects among specific are factors,such as population density and per capita residential building area,percentage of households with toilets,percentage of rental housing,exhibited q statistics of 0.28,0.25,and 0.24,*** This study provides valuable insights into the spatial dynamics of chickenpox outbreaks in rapidly developing regions,revealing the socioeconomic factors affecting disease *** implications extend the formulation of effective public health strategies and interventions to prevent and control chickenpox outbreaks in similar global contexts.

关键词： Chickenpox Spatial dynamics Socioeconomic factors

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Research on a microwave bandpass filter with L-shaped structure 2

Research on a microwave bandpass filter with L-shaped struct...

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2nd International Conference on Laser, Optics and Optoelectronic Technology, LOPET 2022

作者： Hou, Yifeng Qian, Jun School of electronic and information engineering Wuzhou University Guangxi Wuzhou543001 China Guangxi Key Laboratory of Image Processing and Intelligent Information System Wuzhou University Guangxi Wuzhou543001 China

ISBN: (纸本)9781510657588

In this paper, a novel microwave bandpass filter with L-shaped structure is introduced, which belongs to a kind of artificial surface plasmon polaritons (SSPPs) type microwave bandpass *** filter adopts a symmetrical three-stage structure. The first section is a slot-line waveguide section, which realizes the input/output of microwave signals. The second section is a transition section, which adopts the impedance gradient structure to achieve a good connection with the third section. The third section is the SSPPs section composed of periodic symmetric trapezoidal groove structure and 90 degree bend structure. The passband width and stopband characteristics of the filter can be adjusted by adjusting the geometric size of the trapezoidal groove and its air gap, also the microwave subwavelength binding effect can be further improved, which makes the SSPPs filter more excellent in resisting spatial electromagnetic interference. The filter can be used in L~S band civil microwave communication system. © 2022 SPIE.

关键词： Bandpass filters

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Dual-branch Transformer with Series Decomposition for Long-term Orbit Correction

Dual-branch Transformer with Series Decomposition for Long-t...

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2024 International Technical Meeting of The Institute of Navigation, ITM 2024

作者： Liu, Ziyu Wang, Jingran Zheng, Shaolong Wei, Shiyi Li, Zhenni Xie, Shengli Wang, Mingwei School of Automation Guangdong University of Technology Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China 111 Center for Intelligent Batch Manufacturing Based on IoT Technology Guangzhou510006 China Techtotop Microelectronics Technology Co. Ltd Guangzhou510006 China Guangdong Key Laboratory of IoT Information Technology Guangzhou510006 China Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education China

ISBN: (纸本)9780936406367

Time to First Fix (TTFF) is one of the most important measures of GNSS receiver performance. Under the hot-start mode, the receiver needs only 1 to 2 seconds for positioning. However, under the cold start mode, it takes more than 20 seconds to receive the ephemeris to obtain the accurate satellite position. This is due to the fact that current satellite orbit prediction method cannot provide position information accurately based on the old ephemeris, which is detrimental to fast positioning. To address the above challenge, we propose a novel Transformer-based model for long-term orbit corrections. The model makes full use of the time-series information of historical orbit errors and is able to capture long-term dependencies from orbit error series in different lengths. The predicted orbits can be corrected according to the predicted error components, thereby achieving accurate satellite orbit prediction. Due to the temporal patterns in the orbit error series, the features and information hidden in their complex temporal patterns are often difficult to be extracted and utilised. To tackle the intricate temporal patterns, we introduce an inner series decomposition block to deconstruct the seasonal part and trend-cyclical part from orbit errors series, which also empowers the model with immanent decomposition capacity. In addition, in order to extract the cross-time dependency within the curves as well as the cross-dimension dependency between the curves, we propose the Dual-Branch Attention (DBA) layer to capture the cross-time and cross-dimension dependency. Integrating the proposed series decomposition block and the Dual-Branch Attention(DBA) layer into Transformer, we build a Dual-branch Transformer with series decomposition (DBSD-Transformer) for long-term orbit corrections. Experimental results on satellite orbit error data in 2022 from the BeiDou MEO-10 satellite show the effectiveness of our method. Compared to the prediction method based on the Kepler model, t

关键词： Orbits

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A Multi-scale Framework for Out-of-Distribution Detection in Dermoscopic Images 1

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Zhongzheng Wang, Tao Cai, Yuanzheng Liang, Lingyu College of Computer and Data Science Fuzhou University Fuzhou350108 China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control College of Computer and Control Engineering and International Digital Economy College Minjiang University Fuzhou350108 China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions Wuyi University Wuyishan354300 China Fujian Yilian-Health Nursing Information Technology Co. Ltd. Fuzhou350003 China School of Electronic and Information Engineering South China University of Technology Guangzhou510641 China

ISBN: (数字)9783031200960

ISBN: (纸本)9783031200953

The automatic detection of skin diseases via dermoscopic images can improve the efficiency in diagnosis and help doctors make more accurate judgments. However, conventional skin disease recognition systems may produce high confidence for out-of-distribution (OOD) data, which may become a major security vulnerability in practical applications. In this paper, we propose a multi-scale detection framework to detect out-of-distribution skin disease image data to ensure the robustness of the system. Our framework extracts features from different layers of the neural network. In the early layers, rectified activation is used to make the output features closer to the well-behaved distribution, and then an one-class SVM is trained to detect OOD data;in the penultimate layer, an adapted Gram matrix is used to calculate the features after rectified activation, and finally the layer with the best performance is chosen to compute a normality score. Experiments show that the proposed framework achieves superior performance when compared with other state-of-the-art methods in the task of skin disease recognition. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Chemical activation

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