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Fixed-Time Sliding Mode control With Varying Exponent Coefficient for Modular Reconfigurable Flight Arrays

IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 514-528页

作者： Jianquan Yang Chunxi Yang Xiufeng Zhang Jing Na Faculty of Mechanical and Electrical Engineering Kunming University of Science and TechnologyKunming 650500 Yunnan International Joint Laboratory of Intelligent Control and Aoolication of Advanced Equipment Kunming 650500China IEEE

The modular system can change its physical structure by self-assembly and self-disassembly between modules to dynamically adapt to task and environmental requirements. Recognizing the adaptive capability of modular systems, we introduce a modular reconfigurable flight array(MRFA) to pursue a multifunction aircraft fitting for diverse tasks and requirements,and investigate the attitude control and the control allocation problem by using the modular reconfigurable flight array as a platform. First, considering the variable and irregular topological configuration of the modular array, a center-of-mass-independent flight array dynamics model is proposed to allow control allocation under over-actuated situations. Secondly, in order to meet the stable, fast and accurate attitude tracking performance of the MRFA, a fixed-time convergent sliding mode controller with state-dependent variable exponent coefficients is proposed to ensure fast convergence rate both away from and near the system equilibrium point without encountering the singularity. It is shown that the controller also has fixed-time convergent characteristics even in the presence of external disturbances. Finally,simulation results are provided to demonstrate the effectiveness of the proposed modeling and control strategies.

关键词： control allocation dynamic model fixed-time stabilization modular reconfigurable flight array(MRFA) sliding mode

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Predicting circRNA-Disease Associations by Using Multi-Biomolecular Networks Based on Variational Graph Auto-Encoder with Attention Mechanism

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Chinese Journal of Electronics 2024年第6期33卷 1526-1537页

作者： Jing YANG Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology

CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder(VGAE) network to learn the latent distributions of the nodes, a fully connected neural network is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease *** a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.

关键词： Knowledge engineering Attention mechanisms Computational modeling Biological system modeling Predictive models Feature extraction Heterogeneous networks Topology Data mining Diseases

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Parameter estimation in solar power plant systems: a comparative study of recursive and iterative techniques

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Sustainable Energy Research 2024年第1期11卷 1-12页

作者： Sadeghi, Kiavash Hossein Shafikhah, Mahmood Marashian, Arash Roshandel, Emad Razminia, Abolhassan Department of Electrical Engineering Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Bushehr Iran Process Control Laboratory Faculty of Natural Sciences and Engineering Åbo Akademi University Turku Finland College of Science and Engineering Flinders University Adelaide Australia

The development of a dynamic model for a popular implemented solar power plant is a critical task for power engineers aiming to enhance the plant’s performance and reliability. In this study, we utilized the prediction error method (PEM), a robust algorithm for system identification, to capture the plant’s operational characteristics with precision. Additionally, we employed both recursive and hierarchical algorithms to identify the system parameters effectively. The results from each identification method are rigorously quantified and analyzed, allowing for a detailed comparison of their performance. This comprehensive evaluation not only highlights the strengths and weaknesses of each approach, but also provides valuable insights into their practical applications in the context of solar power systems. © The Author(s) 2024.

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Polyphonic sound event localization and detection based on Multiple Attention Fusion ResNet

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Mathematical Biosciences and engineering 2024年第2期21卷 2004-2023页

作者： Zhang, Shouming Zhang, Yaling Liao, Yixiao Pang, Kunkun Wan, Zhiyong Zhou, Songbin Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650500 China Institute of Intelligent Manufacturing Guangdong Academy of Science Guangdong Key Laboratory of Modern Control Technology Guangzhou510030 China

Sound event localization and detection have been applied in various fields. Due to the polyphony and noise interference, it becomes challenging to accurately predict the sound event and their occurrence locations. Aiming at this problem, we propose a Multiple Attention Fusion ResNet, which uses ResNet34 as the base network. Given the situation that the sound duration is not fixed, and there are multiple polyphonic and noise, we introduce the Gated Channel Transform to enhance the residual basic block. This enables the model to capture contextual information, evaluate channel weights, and reduce the interference caused by polyphony and noise. Furthermore, Split Attention is introduced to the model for capturing cross-channel information, which enhances the ability to distinguish the polyphony. Finally, Coordinate Attention is introduced to the model so that the model can focus on both the channel information and spatial location information of sound events. Experiments were conducted on two different datasets, TAU-NIGENS Spatial Sound Events 2020, and TAU-NIGENS Spatial Sound Events 2021. The results demonstrate that the proposed model significantly outperforms state-of-the-art methods under multiple polyphonic and noise-directional interference environments and it achieves competitive performance under a single polyphonic environment. ©2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License.

关键词： Deep learning

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Study on the assimilation of the sulphate reaction rates based on WRF-Chem/DART

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science China Earth sciences 2023年第10期66卷 2239-2253页

作者： Congwu HUANG Chaoqun MA Tijian WANG Yawei QU Mengmeng LI Shu LI Bingliang ZHUANG Min XIE Faculty of Resources and Environmental Science Hubei UniversityWuhan430062China School of Atmospheric Sciences Nanjing UniversityNanjing210023China College of Intelligent Science and Control Engineering Jinling Institute of TechnologyNanjing211169China

Sulphate(SO_(4)^(2-))is a main component of PM_(2.5)in *** chemical formation mechanisms of sulphate are complex,and many air quality models have been used to analyse these *** improve the accuracy of Weather Research Forecast-Chemistry(WRF-Chem)on sulphate,an alternative method is proposed in this ***,data assimilation is performed to adjust the chemical reaction rates of *** on the original reactions,a new sulphate parameterisation scheme,which includes two hypothetical reactions and six undetermined parameters,was *** on the WRF-Chem/DART(Data Assistance Research Testbed)system,the near-ground concentrations of SO_(4)^(2-),SO_(2),NO_(2),O_(3)and particulate matter are assimilated to adjust the six *** adjusting the parameters,the new scheme can effectively solve the underestimation of SO_(4)^(2-)and overestimation of SO_(2).The simulation of SO_(4)^(2-)improved as the mean bias changed from-13.1μg m^(-3)to 3.5μg m^(-3)while SO_(2)improved from 17.0μg m^(-3)to 6.3μg m^(-3).The temporal and spatial variation characteristics predicted by the new scheme are consistent with the theoretical research results,indicating that the complex mechanism of sulphate formation could be replaced by the temporal and spatial variation characteristics predicted by the new scheme and that the parameters can be adjusted by data ***,the reaction rates of the SO_(4)^(2-)parameterisation scheme of the WRF-Chem model are improved in this study,and a new method for improving the accuracy of the air quality model is provided.

关键词： Sulphate Chemical reaction rate WRF-Chem/DART Data assimilation Air quality forecast

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PSMFNet:Lightweight Partial Separation and Multiscale Fusion Network for Image Super-Resolution

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Computers, Materials & Continua 2024年第10期81卷 1491-1509页

作者： Shuai Cao Jianan Liang Yongjun Cao Jinglun Huang Zhishu Yang Institute of Intelligent Manufacturing GDASGuangdong Key Laboratory of Modern Control TechnologyGuangzhou510030China School of Mechanical&Automotive Engineering South China University of TechnologyGuangzhou511442China Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China School of Faculty of Intelligent Manufacturing Wuyi UniversityJiangmen529020China

The employment of deep convolutional neural networks has recently contributed to significant progress in single image super-resolution(SISR)***,the high computational demands of most SR techniques hinder their applicability to edge devices,despite their satisfactory reconstruction *** methods commonly use standard convolutions,which increase the convolutional operation cost of the *** this paper,a lightweight Partial Separation and Multiscale Fusion Network(PSMFNet)is proposed to alleviate this ***,this paper introduces partial convolution(PConv),which reduces the redundant convolution operations throughout the model by separating some of the features of an image while retaining features useful for image ***,it is worth noting that the existing methods have not fully utilized the rich feature information,leading to information loss,which reduces the ability to learn feature *** by self-attention,this paper develops a multiscale feature fusion block(MFFB),which can better utilize the non-local features of an *** can learn long-range dependencies from the spatial dimension and extract features from the channel dimension,thereby obtaining more comprehensive and rich feature *** the role of the MFFB is to capture rich global features,this paper further introduces an efficient inverted residual block(EIRB)to supplement the local feature extraction ability of PSMFNet.A comprehensive analysis of the experimental results shows that PSMFNet maintains a better performance with fewer parameters than the state-of-the-art models.

关键词： Deep learning single image super-resolution lightweight network multiscale fusion

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Robust Fixed-time Composite controller Design with the Unknown System Dynamics Estimator for Nonlinear Robotic Manipulator Systems 24

Robust Fixed-time Composite Controller Design with the Unkno...

引用

2nd International Conference on Frontiers of intelligent Manufacturing and Automation, CFIMA 2024

作者： Li, Siyan Xing, Yashan Na, Jing Li, Qi Faculty of Mechanical and Electrical Engineering Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Yunnan China

ISBN: (纸本)9798400710681

In this paper, a robust fixed-time composite control scheme based on an Unknown System Dynamics Estimator (USDE) is proposed. The proposed control method guarantees the tracking error convergence in a fixed-time for nonlinear robotic manipulators with unknown dynamics and disturbance. To address the existence of uncertainties, first, a simple structure of USDE by a low-pass filtering operation is constructed without using acceleration signals, Which is adjusted for only one filter coefficient. Then, the USDE compensate the estimated value into the robust fixed-time composite controller. The composite controller contains a fixed time control term, a robust control term, and a computed torque control term. By the fixed-time control term, the upper bound on the convergence time is independent of the initial conditions of the system, which ensures fast-tracking tasks for the robot manipulator. And the robust control term is used to reduce the impact of USDE estimation errors. The stability of the closed-loop system is demonstrated through Lyapunov criteria. The validity of the proposed method is verified by simulation. © 2024 Copyright held by the owner/author(s).

关键词： Stability criteria

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Data-Driven Group Formation control of Cyber-Physical Systems via Distributed Cloud Computing

IEEE Transactions on Industrial Cyber-Physical Systems

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IEEE Transactions on Industrial Cyber-Physical Systems 2025年 3卷 341-350页

作者： Ren, Hongru Long, Yinren Li, Hongyi Huang, Tingwen Guangdong University of Technology School of Automation Guangdong-Hong Kong Joint Laboratory for Intelligent Decision and Cooperative Control Guangdong Provincial Key Laboratory for Intelligent Decision and Cooperative Control Guangzhou510006 China Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

This paper investigates the group formation control problem for cyber-physical systems (CPSs) with random communication constraints. The distributed cloud computing system is constructed to divide agents into groups and establish communication between agents. A data-driven predictive control strategy is proposed by combining networked predictive control and model-free adaptive control method. The desired group formation control performance can be achieved and the three-channel random communication constraints of CPSs are actively compensated. Thisstrategy does not require the system model and relies solely on the system's I/O data for adaptive learning. Further analyses concludes the conditions for simultaneous reach stability and group formation of the closed-loop CPSs using the data-driven predictive control strategy. The effectiveness of the proposed strategy is validated by simulation results. © 2023 IEEE.

关键词： Distributed cloud

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Kinematic Parameter Identification of Industrial Robots Based on an Improved Zebra Algorithm 16th

Kinematic Parameter Identification of Industrial Robots Base...

引用

16th International Conference on Modelling, Identification and control, ICMIC 2024

作者： Zhou, Houchen Gao, Guanbin Wang, Xi Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming650500 China

ISBN: (纸本)9789819617760

Kinematic calibration is one of the main methods to improve the absolute positioning accuracy of industrial robots. Parameter identification is the primary step in determining the calibration effect. However, due to the large number of kinematic parameters of industrial robots and the complex coupling between parameters, the existing parameter identification methods easily fall into local optimal solutions, and it is difficult to obtain accurate kinematic parameters. Therefore, this paper proposes a Particle Swarm Somersault Foraging Zebra Optimization Algorithm (PSSFZOA) for kinematic parameter identification of industrial robots. The proposed PSSFZOA introduces the foraging strategy into the Zebra Optimization Algorithm (ZOA) and linearizes the kinematic error model so that it does not fall into the local optimum in the later stage of optimization, and improves the population diversity in the early stage. In the first stage of the algorithm, the particle swarm strategy is used to provide local optimal particles, which ensures that the algorithm achieves a balance between global search and local search. It has both the ability of deep search and the diversity of breadth search better to find the best in the complex search space. Simulations are carried out to verify the effectiveness of the method. The results show that compared with the existing Grey Wolf Optimization (GWO) and ZOA, the PSSFZOA can avoid falling into local optimum prematurely, accelerate the convergence speed of the algorithm, and improve the absolute positioning accuracy of the industrial robot after parameter identification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Industrial robots

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Adaptive Parameter Estimation with Directional Forgetting for Nonlinear Systems 16th

Adaptive Parameter Estimation with Directional Forgetting f...

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16th International Conference on Modelling, Identification and control, ICMIC 2024

作者： Wen, Guoli Xing, Yashan Na, Jing Gao, Guanbin Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming650500 China Yunnan Key Laboratory of Intelligent Control and Application Kunming University of Science and Technology Kunming650500 China

ISBN: (纸本)9789819617760

This paper proposes a novel adaptive parameter estimation (APE) with directional forgetting (DF) based on the parameter estimation error for nonlinear systems. Most of APE methods are designed to forget the information of the regressor matrix in all direction in order to update the system information. However, some directions of the regressor matrix for systems are insufficient such that forgetting in all directions may trigger windup phenomenon. To prevent this phenomenon, the proposed method in this paper is to divide the regressor matrix into a forgotten part and a retained part via oblique projection. Then, the constant forgetting factor is applied to the forgotten part such that forgetting the regressor matrix is along the direction that the new information comes. Furthermore, the adaptive law is designed based on the parameter estimation error and the exponential convergence of the proposed method can be guaranteed under the persistent excitation (PE) condition. Finally, comparative simulation related to the proposed method and the classic APE method driven by the parameter estimation error is provided. Simulation results show that the proposed APE with DF algorithm has a better estimation performance than the classic APE method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Errors

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