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Estimation and compensation of periodic disturbance using internal-model-based equivalent-input-disturbance approach

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Science China(Information Sciences) 2022年第8期65卷 150-163页

作者： Qicheng MEI Jinhua SHE Zhentao LIU Min WU School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

This paper presents an improved equivalent-input-disturbance(EID) approach to deal with periodic disturbances. The approach has two degrees of freedom. One is an improved EID compensator, in which a repetitive controller is inserted in this study. The other is a conventional servo system for a reference *** improved EID compensator estimates and compensates for periodic disturbances without steady-state error, and the servo system ensures a satisfactory tracking performance. The improved EID compensator is designed using the linear-matrix-inequality(LMI) method. Three parameters in an LMI are selected using the particle-swarm-optimization(PSO) algorithm. The state-feedback gain of the conventional servo system is designed using the linear-quadratic-regulator(LQR) method. Simulation results of a rotational control system demonstrate the validity of the approach and its advantage over others.

关键词： equivalent input disturbance (EID) linear matrix inequality (LMI) linear quadratic regulator (LQR) particle swarm optimization (PSO) periodic disturbance repetitive control

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Exchange Strategies for Multi-Colony Ant Algorithms in Dynamic Environments 13

Exchange Strategies for Multi-Colony Ant Algorithms in Dynam...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Mavrovouniotis, Michalis Li, Changhe Yazdani, Danial Handjimitsis, Diofantos Eratosthenes Centre of Excellence Limassol Cyprus Cyprus University of Technology Department of Civil Engineering and Geomatics Limassol Cyprus School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China University of Technology Sydney Faculty of Engineering & Information Technology Sydney Australia

ISBN: (纸本)9798350308365

In dynamic optimization problems where optimal solutions change over time, traditional ant colony optimization (ACO) algorithms face limitations. This study explores the adaptation of multi-colony ACO algorithms, known for their enhanced search capabilities in stationary problems, to tackle optimization problems in dynamic environments. Various strategies for exchanging information between colonies, which is a critical factor influencing algorithm performance, are investigated. Using the dynamic traveling salesman problem as a foundation, we generate test cases to reflect real-world complexities. Our results on a set of problem instances reveal that the choice of communication strategy between colonies significantly impacts the adaptability and efficiency of multi-colony ACO algorithms in tracking moving optimum. © 2024 IEEE.

关键词： Ant colony optimization

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Photovoltaic Data Cleaning Method Based on DBSCAN Clustering, Quartile Algorithm and Pearson Correlation Coefficient Interpolation Method 6

Photovoltaic Data Cleaning Method Based on DBSCAN Clustering...

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6th International Conference on Energy, Electrical and Power Engineering, CEEPE 2023

作者： Wang, Biyuan Deng, Xingye Chen, Taowei Li, Yidong Huizhou Power Supply Bureau Guangdong Power Grid Co Guangdong China School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9798350348279

The proportion of photovoltaic power generation in the global energy structure is increasing year by year. Because of its volatility and periodicity, grid-connected photovoltaic challenges the safe and stable operation of power grid. One of the ways to solve this problem is data mining. Data mining needs high-quality data support, and data cleaning is one of the important means to improve data quality. Aiming at the problem of poor quality of original PV data, this paper analyzes two typical outliers of PV data and proposes a PV output data cleaning method based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm, quartile algorithm and Pearson correlation coefficient interpolation method. Comparison of DBSCAN clustering algorithm combined with the quartile method of outlier identification with the quartile method alone outlier identification, the effectiveness of the method proposed in this paper is verified. Then using the method proposed in this paper for outlier identification based on Pearson correlation coefficient interpolation and cubic spline interpolation to verify the data filling effect, the results show that Pearson correlation coefficient interpolation is superior to cubic spline interpolation. Finally, two sets of data with different data cleaning methods are substituted into the Long short-term memory (LSTM) Model to verify accuracy of the cleaning method proposed in this paper. © 2023 IEEE.

关键词： Data mining

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Adaptive Backstepping Sliding Mode control for Underwater Glider Trajectory Tracking Based on Nonlinear Disturbance Observer

Adaptive Backstepping Sliding Mode Control for Underwater Gl...

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2023 China Automation Congress, CAC 2023

作者： Li, Zhenjia Cao, Weihua Gan, Chao Wen, Mingming School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Guangzhou Marine Geological Survey Guangzhou510000 China

ISBN: (纸本)9798350303759

An adaptive backstepping dynamic surface sliding mode controller based on nonlinear disturbance observer (NDOABSMC) is designed to track zigzag motion of underwater glider (UG). Firstly, a nonlinear disturbance observer is devised to observe ocean current. Then, the backstepping sliding mode control is used to devise motion attitude controller of UG to ensure that UG can track the target trajectory quickly. Additionally, the stability of UG's control system is analyzed. In the end, the proposed control strategy is compared with other approaches. © 2023 IEEE.

关键词： Backstepping

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Dense Color Constraints based 6D object pose estimation from RGB-D images 41

Dense Color Constraints based 6D object pose estimation from...

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第41届中国控制会议

作者： Zilun Wang Yi Liu Chi Xu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems CRRC Zhuzhou Locomotive Co. Ltd. National Innovation Center of Advanced Rail Transit Equipment

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

A key problem for 6 D pose estimation based on RGB-D image input is how to make full use of these two different data *** previous work simply took the depth map as the input of the fourth channel of CNN,or carried out the fusion of features extracted from these two data sources with different *** their fusion did not impose the right constraints and lost some valuable *** this work,we propose that DCC(Dense Color Constraints).6 D pose estimation performance can be improved effectively by using dense corresponding color *** show the most advanced end-to-end performance in LineMod datasets.

关键词： Computer Vision Deep Learning Pose Estimation

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Bayesian Exponential Regularized Tensor Based Approach for Sparse Geomagnetic Data Completion

Bayesian Exponential Regularized Tensor Based Approach for S...

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2023 International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2023

作者： Li, Guoyu Bin, Junchi Liu, Huan School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Engineering University of British Columbia OkanaganBCV1V 1V7 Canada

ISBN: (纸本)9798350318012

Geomagnetic data is vital for predicting earthquakes and magnetic storms. In this regard, a new Bayesian exponential regularized tensor completion framework for sparse geomagnetic data, i.e. BERTC, is proposed to address this problem in the study. First, the spatiotemporal geomagnetic data is reshaped into a 3D tensor with days and hours that features random missing elements. Second, a Gibbs sampling algorithm is developed to achieve probabilistic inference on matrices' factors and corresponding parameters in this model. Thus, the sparse tensor can be gradually optimized to fill the missing entries during iterations. Third, an exponential regularizer is proposed to reduce oscillations before and after iterations to enhance imputation quality further. Finally, the derived factor matrices are aggregated from Gibbs sampling to complete the sparse tensor. Numerical geomagnetic datasets from 13 cities are employed, and extensive comparison experiments are conducted to evaluate the imputation performance of the BERTC. The results show the superiority of the proposed BERTC compared to the state-of-the-art methods in terms of imputation accuracy, with an approximate improvement of the imputation accuracy as no less than 20%. © 2023 IEEE.

关键词： Matrix algebra

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Optimized Hybrid control for Attitude Adjustment of Downhole Directional Drilling Tools

Optimized Hybrid Control for Attitude Adjustment of Downhole...

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Annual Conference of Industrial Electronics Society

作者： Jing Xiao She Wang Zhen Cai School of Information Engineering Wuhan City Polytechnic University Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This study delves into the critical role of attitude control in downhole directional drilling tools, focusing on the complex coupling between inclination and azimuth in the motion model, particularly the time-varying nature of azimuth adjustments. By implementing separate control strategies for inclination and azimuth, the research integrates a comprehensive hybrid control system. It rigorously examines the stability of the closed-loop systems and validates the effectiveness and applicability of the hybrid control method through extensive simulations. The system demonstrates significant robustness and adaptability, enhancing drilling precision and efficiency.

关键词： Drilling Couplings Industrial electronics Azimuth Attitude control Simulation Focusing Stability analysis Robustness Linear matrix inequalities

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MC-NeRF: Multi-Camera Neural Radiance Fields for Multi-Camera Image Acquisition systems

arXiv

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arXiv 2023年

作者： Gao, Yu Su, Lutong Liang, Hao Yue, Yufeng Yang, Yi Fu, Mengyin School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China

Neural Radiance Fields (NeRF) use multi-view images for 3D scene representation, demonstrating remarkable performance. As one of the primary sources of multi-view images, multi-camera systems encounter challenges such as varying intrinsic parameters and frequent pose changes. Most previous NeRF-based methods assume a unique camera and rarely consider multi-camera scenarios. Besides, some NeRF methods that can optimize intrinsic and extrinsic parameters still remain susceptible to suboptimal solutions when these parameters are poor initialized. In this paper, we propose MC-NeRF, a method that enables joint optimization of both intrinsic and extrinsic parameters alongside NeRF. The method also supports each image corresponding to independent camera parameters. First, we tackle coupling issue and the degenerate case that arise from the joint optimization between intrinsic and extrinsic parameters. Second, based on the proposed solutions, we introduce an efficient calibration image acquisition scheme for multi-camera systems, including the design of calibration object. Finally, we present an end-to-end network with training sequence that enables the estimation of intrinsic and extrinsic parameters, along with the rendering network. Furthermore, recognizing that most existing datasets are designed for a unique camera, we construct a real multi-camera image acquisition system and create a corresponding new dataset, which includes both simulated data and real-world captured images. Experiments confirm the effectiveness of our method when each image corresponds to different camera parameters. Specifically, we use multi-cameras, each with different intrinsic and extrinsic parameters in real-world system, to achieve 3D scene representation without providing initial poses. The Code and supplementary materials are available at https://***/MC-NeRF Copyright © 2023, The Authors. All rights reserved.

关键词： Parameter estimation

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Group consensus of multi-agent systems with additive noises

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Science China(Information Sciences) 2022年第10期65卷 242-255页

作者： Chuanjian LI Xiaofeng ZONG School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

This paper is concerned with group consensus of multi-agent systems(MASs) that consist of two groups in additive noise environments. First, a control protocol is proposed based on the state information of each agent's neighbors corrupted by additive noises. Second, some sufficient conditions and necessary conditions are obtained for the following two types of group consensus behaviors.(1) Pure group consensus:agents in both groups have the same behavior(weak consensus or strong consensus);(2) hybrid group consensus: agents in different groups achieve different consensus behaviors. It is revealed that the influence between the two groups should be attenuated such that the MASs can achieve group consensus in additive noise environments, i.e., the affected group must fight against the influence that comes from another ***, some simulation examples are given to illustrate the feasibility of the theoretical results.

关键词： group consensus multi-agent systems additive noises pure group consensus hybrid group consensus

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DR-WLC: Dimensionality Reduction cognition for object detection and pose estimation by Watching, Learning and Checking

arXiv

引用

arXiv 2023年

作者： Gao, Yu Xu, Xi Jiang, Tianji Chen, Siyuan Yang, Yi Yue, Yufeng Fu, Mengyin School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China

Object detection and pose estimation are difficult tasks in robotics and autonomous driving. Existing object detection and pose estimation methods mostly adopt the same-dimensional data for training. For example, 2D object detection usually requires a large amount of 2D annotation data with high cost. Using high-dimensional information to supervise lower-dimensional tasks is a feasible way to reduce datasets size. In this work, the DR-WLC, a dimensionality reduction cognitive model, which can perform both object detection and pose estimation tasks at the same time is proposed. The model only requires 3D model of objects and unlabeled environment images (with or without objects) to finish the training. In addition, a bounding boxes generation strategy is also proposed to build the relationship between 3D model and 2D object detection task. Experiments show that our method can qualify the work without any manual annotations and it is easy to deploy for practical applications. Source code is at https: //***/IN2-ViAUn/DR-WLC. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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