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Practical implementation of a model inverse optimal iterative learning controller

IFAC Proceedings Volumes 2004年第12期37卷 687-692页

作者： J.D. Ratcliffe T.J. Harte J.J. Hätönen P.L. Lewin E. Rogers D.H. Owens School of Electronics and Computer Science University of Southampton University Road Southampton SO17 1BJ United Kingdom Fax: +44 (0)23 8059 3709 Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street Sheffield S1 3JD United Kingdom Systems Engineering Laboratory The University of Oulu P.O. BOX 4300 FIN-90014 University of Oulu Finland

In this paper, a new model inverse optimal iterative learning control algorithm is practically implemented on an industrial gantry robot. The algorithm has only one tuning parameter which can be adjusted to provide a balance between convergence speed and robustness. Results show that the algorithm is capable of learning the required trajectory in very few iterations. However at this convergence rate the lack of robustness is a major issue. Appropriate use of the tuning parameter is shown to greatly increase the algorithm robustness as demonstrated by tests which successfully reach 600 iterations.

关键词： Learning control Iterative methods Optimal control

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Active Collision Avoidance Maneuver Design Under Constant Thrust ⋆

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IFAC Proceedings Volumes 2011年第1期44卷 9887-9892页

作者： Yongqiang Qi Yingmin Jia Junping Du Fashan Yu The Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics (LMIB) Ministry of Education SMSS Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

Abstract In this paper, the relative position parameters of the target spacecraft are obtained by using the vision measurement and the target maneuver positions are calculated through the isochronous interpolation method. Furthermore, new switch control laws under constant thrust are designed for active collision avoidance maneuver of the chaser along a specified trajectory. The switch control laws are obtained based on the acceleration sequences and the working times of thrusters in three axes which can be respectively computed by the time series analysis method. The perturbations and fuel consumptions are addressed during the computation of the working times. The simulation results show that the constant thrust maneuver can effectively guarantee the chaser maneuvering along the specified trajectory.

关键词： Active collision avoidance maneuver Collision possibility Constant thrust Switch control laws

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Improved Multidisciplinary Multiobjective Collaborative Optimization Technique Based on NSGA-II Algorithm 1

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IFAC Proceedings Volumes 2010年第25期43卷 139-144页

作者： H. Li Y. Jing S. Zhang V. Ojleska T. Kolemisevska G.M. Dimirovski Key Laboratory of Integrated Automation of Process Industry Ministry of Education Northeastern University Shenyang 110004 P.R. China Northeastern University School of Information Science & Engineering Shenyang 110004 P.R. China SS Cyril and Methodius University School FEIT Institute of ASE Karpos II BB MK-1000 Skopje R. Macedonia Dogus University Computer Eng. and Control Eng. Departments TR-34722 Istanbul Turkey

The multiobjective collaborative optimization (MCO) has been widely adopted in concurrent engineering design as a good multiobjective optimization approach provided the problem of optimization results converging often to a local extremum has been taken care of. It has been demonstrated to yield useful solutions to a wide range of optimization problems under concurrent condition both technical and non-technical ones. A solution strategy for MCO that overcomes this drawback is proposed here on the grounds of genetic NSGA-II algorithm. It is an adaptive heuristic search technique that controls the distances between the individual and system's feasible region during genetic evolution. In the evolving NSGA-II based non-dominated sorting, the individual threshold of infeasibility degree is reduced gradually via genetic evolution and the individual's feasibility capacity is determined by its infeasibility degree and the threshold. It has been applied to a benchmark example of speed reducer design, and the obtained results demonstrate the proposed technique is comparably more efficient and may offer substantial advantages over the gradient-based optimization methods. Copyright © 2010 IFAC

关键词： Collaborative optimization dynamic threshold genetic algorithms multidisciplinary optimization multiobjective optimization

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Enhanced valley zeeman splitting in fe-doped monolayer mos2

arXiv

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arXiv 2020年

作者： Li, Qi Zhao, Xiaoxu Deng, Longjiang Shi, Zhongtai Liu, Sheng Wei, Qilin Zhang, Linbo Cheng, Yingchun Zhang, Li Lu, Haipeng Gao, Weibo Huang, Wei Qiu, Cheng-Wei Xiang, Gang Pennycook, Stephen John Xiong, Qihua Loh, Kian Ping Peng, Bo National Engineering Research Center of Electromagnetic Radiation Control Materials School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Materials Science and Engineering National University of Singapore 9 Engineering Drive 1 117575 Singapore Department of Chemistry Centre for Advanced 2D Materials Graphene Research Centre National University of Singapore 117549 Singapore Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University 637371 Singapore Key Laboratory of Flexible Electronics Institute of Advanced Materials Nanjing Tech University Nanjing211816 China College of Physical Science and Technology Sichuan University Chengdu610064 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

The "Zeeman effect" offers unique opportunities for magnetic manipulation of the spin degree of freedom (DOF). Recently, valley Zeeman splitting, referring to the lifting of valley degeneracy, has been demonstrated in two-dimensional transition metal dichalcogenides (TMDs) at liquid helium temperature. However, to realize the practical applications of valley pseudospins, the valley DOF must be controllable by a magnetic field at room temperature, which remains a significant challenge. Magnetic doping in TMDs can enhance the Zeeman splitting, however, to achieve this experimentally is not easy. Here, we report unambiguous magnetic manipulation of valley Zeeman splitting at 300 K (geff = -6.4) and 10 K (geff = -11) in a CVD-grown Fe-doped MoS2 monolayer;the effective Landé geff factor can be tuned to -20.7 by increasing the Fe dopant concentration, which represents an approximately fivefold enhancement as compared to undoped MoS2. Our measurements and calculations reveal that the enhanced splitting and geff factors are due to the Heisenberg exchange interaction of the localized magnetic moments (Fe 3d electrons) with MoS2 through the d-orbital hybridization. Copyright © 2020, The Authors. All rights reserved.

关键词： Landforms

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A study on the prediction model of dam seepage volume based on isolated forest-multiple stepwise linear regression

A study on the prediction model of dam seepage volume based ...

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International Conference on computer engineering and Applications (ICCEA)

作者： Guanyu Chen Suli Zhang Chenlin Zhang XiaoChun Qian Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Jilin Province Changbai Mountain Ecological Culture and Virtual Reality Simulation Engineering Research Center Changchun University of Technology School of Computer Science & Engineering Changchun China Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Jilin Province Changbai Mountain Ecological Culture and Virtual Reality Simulation Engineering Research Center Changchun Institute of Technology School of Computer Technology & Engineering Changchun China Jilin Provincial Key Laboratory of Changbai Historical Culture and VR Reconstruction Technology Jilin Province Changbai Mountain Ecological Culture and Virtual Reality Simulation Engineering Research Center Jilin Institute of Chemical Technology School of Information and Control Engineering Changchun China

In the study of hydraulic seepage prediction, it is often necessary to target multiple feature dimensions. In this paper, a variety of traditional machine learning algorithms based on stepwise regression filtering features are used to train a dam seepage prediction model based on isolated forest processing of data outliers. The results show that the algorithm can be applied to predict the seepage data of a reservoir more accurately, and further suggest improvements to the dam's seepage control function and observation facilities. The experimental results show that the correlation coefficient of the linear regression model is significantly improved when the training sample ratio is 0.8 after the isolated forest treatment of outliers and stepwise regression screening features.

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High-Gain Like Observer Design for Nonlinear Systems Applied to Vehicle Motion Estimation

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IFAC-PapersOnLine 2023年第2期56卷 5673-5678页

作者： H. Bessafa C. Delattre Z. Belkhatir A. Zemouche R. Rajamani Université de Lorraine CRAN CNRS UMR 7039 54400 Cosnes et Romain France School of Electronics and Computer Science University of Southampton University Road Southampton SO17 1BJ UK Faculty of Computing Engineering and Media De Montfort University The Gateway Leicester LE1 9BH UK Laboratory for Innovations in Sensing Estimation and Control Department of Mechanical Engineering University of Minnesota Minneapolis USA

This work investigates the problem of state estimation for nonlinear triangular systems having additional output measurements. The main objective of this paper is to propose observer design strategies that account for the available additional information, which is difficult to consider using the standard high-gain observer methodology. To deal with this challenge, we propose a novel observer design method to handle the additional output measurements. This method can be thought of as an extension of the standard high-gain observer by introducing a weighting matrix as a tuning parameter, which uses both the high-gain methodology and the LPV/LMI technique. The proposed approach was applied to a nonlinear longitudinal dynamic model that uses extra measurement information, in addition to the measured longitudinal position of the vehicle.

关键词： Observer design High-gain observer LPV/LMI observer Autonomous Vehicles Longitudinal dynamic model

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Risk-Sensitive Cubature Filtering for Jump Markov Nonlinear Systems and Its Application to Land Vehicle Positioning ⋆

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IFAC Proceedings Volumes 2011年第1期44卷 20-25页

作者： Wenling Li Yingmin Jia Junping Du Fashan Yu The Seventh Research Division and the Department of Systems and Control Beihang University (BUAA) Beijing 100191 China Key Laboratory of Mathematics Informatics and Behavioral Semantics (LMIB) Ministry of Education SMSS Beihang University (BUAA) Beijing 100191 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia School of Computer Science and Technology Beijing University of Posts and Telecommunications Beijing 100876 China School of Electrical Engineering and Automation Henan Polytechnic University Jiaozuo 454000 Henan China

Abstract In this paper, a risk-sensitive filter for a class of jump Markov nonlinear systems is proposed. By using a set of weighted cubature points to approximate the intractable risk-sensitive recursions, the proposed filter could exhibit improved numerical stability with respect to the derivation of the square-root covariance, for which the unscented transform technique might halt the operations due to the presence of non-positive definite covariance. The performance of the proposed filter is illustrated through a simulation study of tracking a land vehicle with maneuvers.

关键词： Markov models Kalman filters estimation theory target tracking

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Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

Fast Intra Mode Prediction Algorithms for SCBS in VVC SCC

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Dayong Wang Yishen Deng Weisheng Li Xin Lu Frederic Dufaux Bo Hang Ce Zhu Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Guangxi Key Laboratory of Machine Vision and Intelligent Control Wuzhou University Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecommunications Faculty of Computing Engineering and Media (CEM) De Montfort University Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et systèmes The Computer School Hubei University of Arts and Science School of Information and Communication Engineering University of Electronic Science and Technology of China

Versatile Video Coding (VVC) now supports Screen Content Coding (SCC) by integrating two efficient coding modes: Intra Block Copy (IBC) and Palette (PLT). However, the numerous modes and the Quad-Tree Plus Multi-Type Tree (QTMT) structure inherent to VVC contribute to a very high coding complexity. To effectively reduce the computational complexity of VVC SCC, we propose a fast Intra mode prediction algorithm for VVC SCC. More specifically, we first use the difference of minimum Sum of Absolute Transformed Differences (SATD) value of four Directional Modes (DMs) of Intra and the SATD value of the IBC-merge mode to determine whether to early skip Intra checking. Subsequently, we use a decision tree to determine whether to early terminate the checking after block differential pulse coded modulation (BDPCM). Finally, we employ a decision tree to determine whether to early skip multiple transform selection (MTS) and low frequency non-separable transform (LFNST) checking. The results demonstrate that our algorithm achieves an average encoding time reduction of 34.34% with a negligible Bjøntegaard delta bitrate increase of 0.46%.

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Comparisons of Different Kernels in Kriging-Assisted Evolutionary Expensive Optimization

Comparisons of Different Kernels in Kriging-Assisted Evoluti...

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IEEE Symposium Series on Computational Intelligence

作者： Jie Tian Ying Tan Chaoli Sun Jianchao Zeng Haibo Yu Yaochu Jin College of Mechanical Engineering Shandong Womens University Jinan China Department of Computer Science and Technology Northeastern University Shenyang China College of Mechanical Engineering Taiyuan University of Science and Technology Taiyuan China Division of Industrial and System Engineering Taiyuan University of Science and Technology Taiyuan China School of Computer Science and Control Engineering North University of China Taiyuan China Department of Computer Science Taiyuan University of Science and Technology Taiyuan China

Surrogate-assisted evolutionary algorithms (SAEAs) have received increasing attention in recent years. Kriging is one of the most popular surrogate models due to its ability to provide approximation uncertainty as well as the approximation value without additional computational cost. The kernel function used in the Kriging model plays a very important role in Kriging, as it has considerable influence on the approximation performance of Kriging models. However, little work has been dedicated to the efficiency of training the Kriging model and the influence of the kernel function on the performance of SAEAs. In this paper, we conduct extensive empirical experiments to examine the performance of Kriging-assisted SAEAs on a widely used test suite of benchmark problems of a dimension of 20 or 30 using different kernel functions. Detailed analyses of the training efficiency and the performance of Kriging-assisted SAEAs with different kernel functions are given.

关键词： Expensive optimization Kriging Kernel function Fitness approximation

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Prediction of endometrial cancer MRI images based on deep learning and improved Bayesian extreme learning machine ensemble learning

Prediction of endometrial cancer MRI images based on deep le...

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Frontiers Technology of Information and computer (ICFTIC), IEEE International Conference on

作者： Ximiao Wang Qinghui Hu Xisheng Dai Peijun Li Yuan Chen Institute of Intelligent Systems and Control Guangxi University of Science and Technology Liuzhou City Guangxi Province China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin City Guangxi Province China Department of Radiology Jiangmen Central Hospital Jiangmen City Guangdong Province China Department of Gynecology Jiangmen Central Hospital Jiangmen City Guangdong Province China

For the prediction of deep myometrial invasion (DMI) in endometrial cancer (EC), this study proposes an ensemble learning method which combines deep learning (DL) and improved Bayesian extreme learning machine (BELM). Firstly, the MRI images of endometrial cancer are preprocessed to meet the requirements. Secondly, the deep features relevant to the task are extracted from the images by using convolutional kernels. Finally, the bootstrap resampling method is employed to repeatedly sample multiple training subsets from the training set. The improved Bayesian extreme learning machine is used as the base classifier to train multiple independent sub-models. An ensemble learning classifier is constructed using an ensemble strategy to improve the prediction accuracy and stability of the model. Experimental results show that the proposed method achieves an AUC of 0.758 on the internal validation set and 0.740 on the external validation set, which demonstrat good generalization ability and stability of the proposed method.

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