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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Surface Defect Classification of Steels Based on Ensemble of Extreme Learning Machines

Surface Defect Classification of Steels Based on Ensemble of...

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WRC Symposium on Advanced Robotics and Automation (WRC SARA)

作者： Yanan Liu Ying Jin Hongbin Ma Beijing Institute of Technology Beijing P. R. China State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing P. R. China

In recent years, iron and steel industry of China has developed rapidly, and steel surface defects recognition has attracted wide attention in the field of industrial inspection. Aiming at the problems of poor precision and low speed of traditional surface defect detection methods, we propose to use a fully learnable ensemble of Extreme Learning Machines (ELMs), which is ELM-IN-ELM, for defect classification. The Local Binary Pattern is adopted as the basic feature extraction method. The ELM-IN-ELM determines the final classification decision by automatically learning the output of M independent ELM sub-models. To further illustrate the superiority of the ELM-IN-ELM algorithm for classification, the Northeastern University (NEU) surface defect database is used to evaluate its classification effect. The experimental results demonstrate that this method works remarkably well for surface defects classification. Compared with other methods, the proposed method can identify the types of defects more accurately, which is of practical significance to steel surface defect detection.

关键词： Steel Neural networks Training Inspection Surface treatment Feature extraction Binary codes

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Distribution system state estimation: an overview of recent developments

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Frontiers of Information Technology & Electronic Engineering 2019年第1期20卷 4-17页

作者： Wang Gang Giannakis Georgios B Chen Jie Sun Jian Department of Electrical and Computer Engineering and Digital Technology Center University of Minnesota USA Minneapolis MN 55455 School of Automation Beijing Institute of Technology Beijing 100081 Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing 100081

In the envisioned smart grid, high penetration of uncertain renewables, unpredictable participation of (industrial) customers, and purposeful manipulation of smart meter readings, all highlight the need for accurate, fast, and robust power system state estimation (PSSE). Nonetheless, most real-time data available in the current and upcoming transmission/distribution systems are nonlinear in power system states (i.e., nodal voltage phasors). Scalable approaches to dealing with PSSE tasks undergo a paradigm shift toward addressing the unique modeling and computational challenges associated with those nonlinear measurements. In this study, we provide a contemporary overview of PSSE and describe the current state of the art in the nonlinear weighted least-squares and least-absolutevalue PSSE. To benchmark the performance of unbiased estimators, the Cramer-Rao lower bound is developed. Accounting for cyber attacks, new corruption models are introduced, and robust PSSE approaches are outlined as well. Finally, distribution system state estimation is discussed along with its current challenges. Simulation tests corroborate the effectiveness of the developed algorithms as well as the practical merits of the theory.

关键词： state estimation Cramer-Rao bound Feasible point pursuit Semidefinite relaxation Proximal linear algorithm Composite optimization Cyber attack Bad data detection

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Multi-criteria consensus sorting model with flexible linguistic preferences based on fuzzy information granulation from the perspective of preference disaggregation

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Engineering Applications of Artificial Intelligence 2025年 154卷

作者： Zhang, Shiji Zhang, Shitao Tian, Hao Deveci, Muhammet Liu, Xiaodi School of Microelectronics and Data Science Anhui University of Technology Ma'anshan243032 China Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes Anhui University of Technology Ma'anshan243002 China Anhui Provincial Joint Key Laboratory of Disciplines for Industrial Big Data Analysis and Intelligent Decision Ma'anshan243032 China Department of Industrial Engineering Turkish Naval Academy National Defence University Tuzla Istanbul34942 Turkey The Bartlett School of Sustainable Construction University College London LondonWC1E 6BT United Kingdom Department of Information Technologies Western Caspian University Baku1001 Azerbaijan

Multi-criteria group sorting (MCGS) that considers linguistic preferences involves multiple individuals evaluating alternatives and assigning them to pre-determined ordered categories based on specific criteria. Nevertheless, due to the limited availability of class information and the constrained cognitive capacity of decision-makers (DMs), it becomes challenging for DMs to furnish explicit preference information to reach consensus. Besides, decision parameters such as consensus threshold and class thresholds are also too hard to be assumed in advance. Therefore, this paper studies the preference disaggregation problem in MCGS, in which DMs are allowed to provide their pairwise comparisons in flexible linguistic expressions (FLEs) as preference information. To more fully utilize the preferences, semantic granulation is introduced. First, an individual consistency recognition model is proposed to identify inconsistent preferences and provide modification directions to the corresponding individuals. Next, semantic granulation and maximum entropy are combined into a granulation-driven information transformation model to convert the preference information based on FLEs into triangular fuzzy numbers (TFNs). Subsequently, in the consensus-driven preference disaggregation model, decision parameters and sorting results can be obtained at the premise of consensus by adjusting weights. Ultimately, to substantiate the effectiveness of the proposal, two numerical applications concerning the sorting of government venture capitals and information system suppliers are presented, along with comparative analysis, sensitivity analysis, and simulation analysis. © 2025 The Authors

关键词： Information granules

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Distributed computation for solving the sylvester equation based on optimization

arXiv

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arXiv 2019年

作者： Deng, Wen Zeng, Xianlin Hong, Yiguang School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China

This paper solves the Sylvester equation in the form of AX + XB = C in a distributed way, and proposes three distributed continuous-time algorithms for three cases. We start with the basic algorithm for solving a least squares solution of the equation, and then give a simplified algorithm for the case when there is an exact solution to the equation, followed by an algorithm with regularization case. Based on local information and appropriate communication among neighbor agents, we solve the distributed computation problem of the Sylvester equation from the optimization viewpoint, and we prove the convergence of proposed algorithms to an optimal solution in three different cases, with help of the convex optimization and semi-stability. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

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Optimize Student Learning via Random Forest-Based Adaptive Narrative Game

Optimize Student Learning via Random Forest-Based Adaptive N...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： Ryan Hare Ying Tang Wei Cui Joleen Liang Department of Electrical and Computer Engineering Rowan University Glassboro USA State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Institute of Smart Education Qingdao Academy of Intelligent Industries Qingdao China Squirrel AI Learning Shanghai China

ISBN: (数字)9781728169040

ISBN: (纸本)9781728169057

This paper presents an adaptive narrative game system that focuses on sequential logic design. The system adapts a random forest machine learning model to estimate a student's current level of domain knowledge relative to the problem presented to him through his game-playing behavior data, such as time taken to find solutions, errors in solutions, and emotional indicators. Hints, prompts, and/or individualized lessons are then offered to the player to guide their learning in a positive and productive direction. Our preliminary pilot study demonstrates that the model can make accurate classifications, from which proper assistance can then be provided to individual students as they play.

关键词： Games Databases Forestry Education Problem-solving Presses Artificial intelligence

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A hybrid differential evolution and estimation of distribution algorithm for the multi-point dynamic aggregation problem 18

A hybrid differential evolution and estimation of distributi...

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2018 Genetic and Evolutionary Computation Conference, GECCO 2018

作者： Hao, Rong Zhang, Jia Xin, Bin Chen, Chen Dou, Lihua School of Automation Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China School of Automation Beijing Institute of Technology State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing China

ISBN: (纸本)9781450357647

The multi-point dynamic aggregation (MPDA) is a typical task planning problem. In order to solve the MPDA problem efficiently, a hybrid differential evolution (DE) and estimation of distribution algorithm (EDA) called DE-EDA is proposed in this paper, which combines the merits of DE and EDA. The DE-EDA has been applied to multiple MPDA instances of different scales, and compared with EDA and two versions of DE in convergence speed and solution quality separately. The results demonstrate the DE-EDA can solve the MPDA problem effectively. © 2018 Copyright held by the owner/author(s).

关键词： Evolutionary algorithms

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Spacecraft High Accuracy Attitude control by Quaternion-Based Nonlinear Dynamic Inversion

Spacecraft High Accuracy Attitude Control by Quaternion-Base...

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第三十八届中国控制会议

作者： Jinhao Liu Jianying Yang Science and Technology on Aerospace Intelligent Control Laboratory The State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University

Attitude control of spacecraft is an important operation that is general to every spacecraft. However, it is a challenging task due to the nonlinearity present in the spacecraft rotational equations when a certain requirement is demanded from a mission. Linearization can make system simple. But it is accurate only in small range. InfraRed Astronomy Satellite Swarm Interferometer(IRASSI) is a mission which demands an attitude accuracy better than 0.4 arcsec. Thus, in this paper, we check the feasibility of nonlinear dynamic inversion control method for single IRASSI satellite. Quaternion is chosen as the parameter for attitude description due to its singularity free property and avoidance of gimbal-lock phenomenon. In order to provide the shortest distance maneuver to save time and energy, the unwinding problem is also considered due to quaternion's 2:1 mapping.

关键词： Attitude control nonlinear dynamic inversion quaternion high accuracy

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Distributed optimization for continuous time multiple agents systems with unknown parameters

Distributed optimization for continuous time multiple agents...

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第三十八届中国控制会议

作者： Zilun Hu Jianying Yang Science and Technology on Aerospace Intelligent Control Laboratory The State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Peking University

This paper addresses a distributed convex optimization problem with objective functions for arbitrary order continuous time multiply agents systems with unknown parameters. In this paper, the control objective is not only to drive the multiply agents to reach a consensus, but also to cooperatively minimize the team objective function with consensus requirement. Different from the traditional optimization problems, in this paper, the proposed method is distributed, which means that the agent can only acquire the information of its neighbors' objective function through the network connection. Theoretical studies indicate that the proposed algorithms can minimize the team objective function and all agents will reach a consensus. Some simulation examples are presented to show the validity of the theoretical results.

关键词： Distributed optimization Adaptive control Multiply agents

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3D Face Reconstruction System From A Single Photo Based On Regression Neural Network

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IFAC-PapersOnLine 2020年第5期53卷 71-76页

作者： Yatai Ji Keqiang Li Huaiyu Wu Gang Xiong Zhen Shen Xiuqin Shang Bangwen Xi The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Department of Automation Tsinghua University Beijing 100084 China University of Chinese Academy of Sciences Beijing 100049 China The Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Donggguan 523808 China The Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing 100190 China

3D human face modeling is one of the most popular research directions in the field of computer stereo vision. On the one hand, the complex physiological structure of human face as well as various expressions and attitude changes bring great challenges to three-dimensional modeling, which makes the modeling method of human face have high research value and reference significance. On the other hand, 3D face reconstruction has a broad application prospect, and the application demand attracts more researchers to invest in it. Based on the 3DMM and 3DDFA method, this paper utilizes regression neural network to realize end-to-end face reconstruction. After that, we build a visualization program with the training model to realize face modeling based on a single face image of any pose or expression. It’s a 3D face reconstruction system which has functions such as face alignment, face rotation and so on.

关键词： face reconstruct 3DMM 3DDFA regression network

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