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48th IEEE Conference on Decision and control held jointly with 2009 28th Chinese control Conference, CDC/CCC 2009

作者： Zhang, Jinhui Shi, Peng Xia, Yuanqing Fu, Mengyin Key Laboratory of Complex System Intelligent Control and Decision School of Automation Beijing Institute of Technology Beijing 100081 China Faculty of Advanced Technology University of Glamorgan Pontypridd CF37 1DL United Kingdom School of Engineering and Science Victoria University Australia

ISBN: (纸本)9781424438716

This paper is devoted to robust adaptive sliding mode control for a class of nonlinear systems in the Takagi-Sugeno forms with mismatched parametric uncertainties. Sufficient conditions for the existence of linear sliding surfaces are given in terms of linear matrix inequalities, by which the corresponding adaptive reaching motion controller is also designed. Simulation studies show the effectiveness of the control scheme. ©2009 IEEE.

关键词： Linear matrix inequalities

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Smooth controller design for non-linear systems using multiple fixed models

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IET control Theory & Applications 2017年第9期11卷 1467-1473页

作者： Jie Chen Wei Chen Jian Sun The Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing Institute of Technology Beijing People's Republic of China Science and Technology on Electronic Information Control Laboratory Chengdu People's Republic of China

Multiple model adaptive control (MMAC) with second-level adaptation is a recently proposed methodology for dealing with systems where the parametric uncertainty is large. Compared with the multiple model switching scheme, the new scheme can lead to significant improvements in performance. Some research has been conducted using the new scheme, but all of the results concern linear systems with an adaptive identification model set. In this study, MMAC with second-level adaptation scheme is extended to non-linear systems in strict feedback form, and the fixed identification model set is under consideration. This is motivated by the fact that a smooth controller can lead to smooth performance and the fixed identification model set gains potential advantages over the adaptive identification model set, especially for the case that the parameters of the system change over the time. Design details are presented and the stability of MMAC with second-level adaptation using a fixed identification model set for non-linear systems is given, which has not been discussed before. Finally, two simulations are performed to show that this scheme performs much better than conventional schemes, including adaptive control and multiple-model switching schemes, in terms of convergence speed and transient performance.

关键词： nonlinear control systems control system synthesis adaptive control uncertain systems linear systems identification convergence transient performance convergence speed fixed identification model set second-level adaptation scheme adaptive identification model set linear systems multiple model switching scheme parametric uncertainty MMAC multiple model adaptive control multiple fixed models nonlinear systems smooth controller design

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Low-Cost INS/GNSS Integration Algorithm Aided by Multi-Constraints Applied to Vehicle Navigation

Low-Cost INS/GNSS Integration Algorithm Aided by Multi-Const...

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IEEE International Conference on control and Robotics Engineering (ICCRE)

作者： Baoshuang Ge Hai Zhang Wenxing Fu Jieling Chen School of Automation Science and Electrical Engineering Beihang University Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China China Ship Development and Design Center Wuhan China

ISBN: (数字)9781728167916

ISBN: (纸本)9781728167923

The low-cost inertial navigation system/global navigation satellite system (INS/GNSS) integration has been widely used to vehicle navigation. However, the positioning performance often declines due to the weak satellite signals. This paper presents a new approach to improve the accuracy of the vehicle navigation. In contrast to the current methods, a new velocity constraint model is developed to aid the estimation of the navigation errors. The altitude accuracy of GNSS is challenged in comparison with its position in the horizonal plane. Thus, an auxiliary low-cost barometer is used to obtain the height as well. The experimental results demonstrate the benefit of the proposed multi-constraints aiding algorithm.

关键词： Global navigation satellite system Global Positioning system Kalman filters Receivers Sea measurements

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An SIE Formulation With Loop Analysis for Large-Scale Arbitrarily Shaped Interconnects

An SIE Formulation With Loop Analysis for Large-Scale Arbitr...

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Antennas and Propagation Society International Symposium

作者： Zekun Zhu Ningbo Gong Guijie Diao Shunchuan Yang School of Electronic and Information Engineering Beihang University Beijing China Beijing Electro-mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory China

A magneto-quasi-static (MQS) surface integral equation (SIE) formulation with loop analysis is proposed to extract the impedance of interconnects in packages. The loop analysis is developed with an independent and complete set of unknowns according to the graph theory, which can significantly reduce the dimension of the final matrix. With the pre-corrected fast Fourier Transform (pFFT), large-scale interconnects can be efficiently modelled. A practical example in packages is carried out to validate the effectiveness and scalability of the proposed formulation. Results show that the proposed formulation is accurate and flexible to model complex interconnects in packages compared with the industrial solver.

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Flight Parameter Prediction and Fault Propagation based on Machine Learning and Symbolic Directed Graph

Flight Parameter Prediction and Fault Propagation based on M...

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International Symposium on Autonomous systems (ISAS)

作者： Wenzhuo Li Kun Guo Yuehua Cheng Hengsong Hu Cheng Xu Ziquan Yu College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Institute of Mechanical and Electrical Engineering Beijing China

Fault influence and propagation analysis of flight vehicle systems is an important element of flight vehicle health management and also an important problem to be solved. A fault influence model based on data-driven is established, including a prediction model of flight parameters under fault, a dynamic influence path, and an influence degree model. Based on the historical experimental data, a long and short-term memory neural network (LSTM) model is proposed to predict the time-series data of each flight parameter of the flight vehicle under fault; based on the prediction results, a symbolic directed graph (SDG) is used to describe the fault of the flight vehicle system, and then introduce the concept of a compatible path with time-series characteristics to describe the dynamic propagation process of the fault. The case shows that the method proposed in this paper enables qualitative and quantitative analysis of the fault influence, and can reasonably describe the fault propagation path and influence characteristics.

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Effective distributed algorithm for solving linear matrix equations

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science China(Information sciences) 2023年第8期66卷 265-266页

作者： Songsong CHENG Jinlong LEI Xianlin ZENG Yuan FAN Yiguang HONG Anhui Engineering Laboratory of Human-Robot Integration System and Intelligent Equipment School of Electrical Engineering and Automation Anhui University Department of Control Science and Engineering Tongji University Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology

Dear editor,Solving linear matrix equations is a basic and important problem in many fields such as the computation of generalized inverses of matrices and(generalized) Sylvester equations. Also, the linear algebraic ...

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Gated Driver Attention Predictor

Gated Driver Attention Predictor

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International Conference on intelligent Transportation

作者： Tianci Zhao Xue Bai Jianwu Fang Jianru Xue College of Transportation Engineering Chang'an University Xi'an China Science and Technology on Complex System Control and Intelligent Agent Cooperative Laboratory Beijing China College of Artificial Intelligence Xi'an Jiaotong University Xi'an China

Driver attention prediction implies the intention understanding of where the driver intends to go and what object the driver concerned about, which commonly provides a driving task-guided traffic scene understanding. Some recent works explore driver attention prediction in critical or accident scenarios and find a positive role in helping accident prediction, while the promotion ability is constrained by the prediction accuracy of driver attention maps. In this work, we explore the network connection gating mechanism for driver attention prediction (Gate-DAP). Gate-DAP aims to learn the importance of different spatial, temporal, and modality information in driving scenarios with various road types, occasions, and light and weather conditions. The network connection gating in Gate-DAP consists of a spatial encoding network gating, long-short-term memory network gating, and information type gating modules. Each connection gating operation is plug-and-play and can be flexibly assembled, which makes the architecture of Gate-DAP transparent for evaluating different spatial, temporal, and information types for driver attention prediction. Evaluations on DADA-2000 and BDDA datasets verify the superiority of the proposed method with the comparison with state-of-the-art approaches.

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An Approximated Domination Relationship based on Binary Classifiers for Evolutionary Multiobjective Optimization

An Approximated Domination Relationship based on Binary Clas...

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Congress on Evolutionary Computation

作者： Hao Hao Aimin Zhou Hu Zhang Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology East China Normal University Shanghai China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing Electro-mechanical Engineering Institute Beijing China

Preselection is an important strategy to improve evolutionary algorithms’ performance by filtering out unpromising solutions before fitness evaluations. This paper introduces a pre-selection strategy based on an approximated Pareto domination relationship for multiobjective evolutionary optimization. For each objective, a binary relation between each pair of solutions is constructed based on the current population, and a binary classifier is built based on the binary relation pairs. In this way, an approximated Pareto domination relationship can be defined. When new trial solutions are generated, the approximated Pareto domination is used to select promising solutions, which shall be evaluated by the real objective functions. The new preselection is integrated into two algorithms. The experimental results on two benchmark test suites suggest that the algorithms with preselection outperform their original ones.

关键词： Filtering Computational modeling Sociology Training data Evolutionary computation Approximation algorithms Linear programming

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Identity Re recognition technology Based on Improved Lightweight ConvNetx Model

Identity Re recognition Technology Based on Improved Lightwe...

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Chinese control Conference (CCC)

作者： Peng Siting Li Lei Hongbo Zhu Ren Jun Song Zhichao Jiabing Yang The Beijing Electro-Mechanical Engineering Institute Beijing China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Beihang University School of Electronics and Information Engineering Beijing China

In the engineering field, there is a requirement for re recognition, including the identity recognition of cooperative and non-cooperative targets, that is, different sensors shoot targets in the same scene, and the identity recognition of targets from two perspectives is required, also known as target accurate retrieval. This method is widely used in fields such as automatic driving and military strikes. In the actual application scenario, deploying the re-recognition algorithm on the embedded processor needs to ensure the real-time computing speed and accuracy, so the complexity of the deep learning algorithm must be reduced, and pruning and lightweight processing are required. In the engineering field, when the image acquired by the sensor is subject to natural interference such as cloud and fog weather or fire and smoke, the difficulty of re-recognition increases. This paper adopts the lightweight improvement of ConvNeXt network model, and fine-tuned it on the basis of its original feature extraction function, so that it can successfully migrate to infrared image classification and also complete the extraction of infrared image target detail distinguishing features, and at the same time introduce the alignment of similar targets in infrared cross-view recognition. In the original similarity calculation, the average value is directly obtained from the spatial dimension of the output feature layer, which is replaced by the weight of the position in each space for estimation. The feature information of the multi-view marine ship target is extracted from the multi-sensor marine image, and its similarity is calculated, so as to determine the identity, thus reducing the support conditions and reconnaissance costs, and improving the accurate detection and tracking ability under the diverse dynamic reconnaissance conditions.

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Localization of Pallets in Narrow Aisles for Unmanned Forklifts 3

Localization of Pallets in Narrow Aisles for Unmanned Forkli...

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3rd International Symposium on Computer technology and Information science, ISCTIS 2023

作者： Sun, Peng Ding, Wei Lian, Mingchang Chen, Hao Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences Quanzhou362200 China Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China Fujian Fuzhou350108 China Fujian Provincial Key Laboratory of Intelligent Identification and Control of Complex Dynamic System Quanzhou362200 China

ISBN: (纸本)9798350325386

As the application of unmanned forklifts becomes more and more widespread, logistics scenarios are constantly evolving. This paper focuses on a scenario where the pick-up of pallets perpendicular to forklift in narrow aisles is required. The narrow aisles only allow the forklift to complete the turning, performing pallet localization in front of it will be difficult. We proposes a method for localizing pallets in narrow aisles for unmanned forklifts. By installing the camera at an oblique angle above the pallet, the collected images are uploaded to the image processing system. The YOLOv5 is used to detect the pallet and extract the bounding boxes of the pallet class. Based on the bounding box coordinates, the original image is cropped. Convert the cropped image to grayscale for Canny edge detection, and then perform Hough line detection to extract the right edge of the pallet. This right edge will be compared with the reference pallet edge to obtain the relative localization between the pallet and the unmanned forklift. © 2023 IEEE.

关键词： Feature extraction

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