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European control Conference (ECC)

作者： Janine Matschek Tim Gonschorek Magnus Hanses Norbert Elkmann Frank Ortmeier Rolf Findeisen Laboratory for Systems Theory and Automatic Control Otto-von-Guericke Univ. Magdeburg Chair of Software Engineering Otto-von-Guericke Univ. Magdeburg Fraunhofer-Institute for Factory Operation and Automation Magdeburg

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

One of the key benefits of model predictive control is the capability of controlling a system proactively in the sense of taking the future system evolution into account. However, often external disturbances or references are not a priori known, which renders the predictive controllers shortsighted or uninformed. Adaptive and learning based prediction models can provide suitable predictions to the controller and therefor can be applied to overcome this issue. We propose to learn references for model predictive controllers via Gaussian processes. To illustrate the approach, we consider robot assisted surgery, where a robotic manipulator must follow a learned reference position based on optical tracking measurements.

关键词： Predictive models Gaussian processes Robots Predictive control Task analysis control systems Tracking

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Construction, modeling, and control of a three-beam prototype using interactive fiber rubber composites

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PAMM 2023年第4期23卷

作者： Aline Iobana Acevedo-Velazquez Najmeh Keshtkar Johannes Mersch Konrad Katzer Chokri Cherif Martina Zimmermann Gerald Gerlach Klaus Röbenack Institute of Control Theory Faculty of Electrical and Computer Engineering TUD Dresden University of Technology Dresden Germany Correspondece Institute of Solid-State Electronics Faculty of Electrical and Computer Engineering TUD Dresden University of Technology Dresden Germany Fraunhofer Institute for Material and Beam Technology IWS Dresden Germany Institute for Material Science Faculty of Mechanical Engineering TUD Dresden University of Technology Dresden Germany Institute of Textile Machinery and High Performance Material Technology Faculty of Mechanical Engineering TUD Dresden University of Technology Dresden Germany

In this contribution, we discuss the construction, modeling, and control of a three-beam prototype that utilizes interactive fiber rubber composites integrated with shape memory alloys (SMAs) as actuators. These actuators are integrated into a textile layer and covered with an elastomer layer, providing flexibility and protection to the prototype. A mathematical model was developed to describe the behavior of the prototype, employing system identification techniques. However, precise position control of systems actuated by SMAs is challenging due to their inherent nonlinearities and hysteretic behavior. To tackle this challenge, a proportional-integral (PI) controller was designed based on robust stability conditions. The effectiveness of the designed PI controller was validated through both numerical simulations and real experiments.

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Incorporating Human Driving Data into Simulations and Trajectory Predictions 1

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4. Internationale ATZ-Fachtagung Automatisiertes Fahren, 2018

作者： Schmidt, Manuel Manna, Carlo Nattermann, Till Glander, Karl-Heinz Bertram, Torsten Institute of Control Theory and Systems Engineering TU Dortmund University Dortmund Germany ZF Group – TRW Automotive GmbH Automated Driving and Integral Cognitive Safety Düsseldorf Germany

ISBN: (数字)9783658237516

ISBN: (纸本)9783658237509

The development of algorithms for automated driving is a very challenging task. Recent progress in machine learning suggests that many algorithms will have a hybrid structure composed of deterministic or optimization and learning based elements. To train and validate such algorithms, realistic simulations are required. They need to be interaction based, incorporate intelligent surrounding traffic and the other traffic participants behavior has to be probabilistic. Current simulation environments for automotive systems often focus on vehicle dynamics. There are also microscopic traffic simulations that on the other hand don’t take vehicle dynamics into account. The few simulation software products that combine both elements still have at least one major problem. That is because lane change trajectories disregard human driving dynamics during such maneuvers. Consequently, machine learning algorithms developed and trained in simulations hardly generalize to non-synthetic data and therefore to real-world applications. © Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature 2019.

关键词： Adversarial machine learning

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Compositional Synthesis of Symbolic Models for Infinite Networks ⁎

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IFAC-PapersOnLine 2020年第2期53卷 1868-1873页

作者： Abdalla Swikir Navid Noroozi Majid Zamani Department of Electrical and Computer Engineering Technical University of Munich Germany Laboratory for Systems Theory and Automatic Control Otto-von-Guericke University Magdeburg Germany Computer Science Department University of Colorado BoulderUSA. Institute of Informatics Ludwig Maximilian University Munich Germany

In this paper, we provide a compositional method for the construction of symbolic models (a.k.a. finite abstractions) for infinite networks of discrete-time control systems. The concrete infinite network and its symbolic model are related by a so-called alternating simulation function which allows one to quantify the mismatch between the output behavior of the infinite interconnection of concrete subsystems and that of their symbolic models. We show that such an alternating simulation function can be obtained compositionally by assuming some small-gain type conditions and composing so-called local alternating simulation functions constructed for subsystems. Assuming certain stability property of concrete subsystems, we also provide a technique to synthesize their symbolic models together with their corresponding local alternating simulation functions. Finally, we apply our results to a traffic network divided into infinitely many cells.

关键词： Compositionality interconnected systems small-gain condition infinite networks symbolic models

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Gaussian Recursive Filtering Algorithm for Multi-Rate Sensor Systems with Delayed and Infrequent Primary Measurements

Gaussian Recursive Filtering Algorithm for Multi-Rate Sensor...

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第三十八届中国控制会议

作者： Kai Tang Xing-jun Yuan Xin Zhao Kai Zhao Shen-min Song System Engineering Research Institute Beijing Center for Control Theory and Guidance Technology Harbin Institute of Technology

In order to reduce the computational burden of multi-rate multi-sensor systems with delayed and infrequent measurements, in this paper, we propose two Gaussian recursive filters. First of all, by augmenting the delayed and infrequent measurements to original one and introducing a judgment factor, the centralized filter is designed. Then, based on the rule of CI fusion and the same factor in centralized fusion filter, the distributed fusion filter is designed. Finally, to verify the effectiveness of proposed centralized and distributed filter in this paper, using a discrete-time linear model, the two algorithms proposed in this paper are compared with the standard Kalman filter without infrequent measurements and the method of re-computation.

关键词： centralized fusion filter distributed fusion filter judgment factor

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Nonlinearity Measures for Distributed Parameter and Descriptor Systems

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IFAC-PapersOnLine 2020年第2期53卷 7545-7550页

作者： Pedro Reyero-Santiago Carlos Ocampo-Martinez Rolf Findeisen Richard D. Braatz Automatic Control Department Universitat Politècnica de Catalunya Institut de Robòtica i Informàtica Industrial (CSIC-UPC) 08028 Barcelona Spain Institute for Automation Engineering (IFAT) Laboratory for Systems Theory and Automatic Control Otto-von-Guericke Universität Magdeburg 39106 Magdeburg Germany Massachusetts Institute of Technology Room E19-551 77 Massachusetts Avenue Cambridge MA 02139 USA

control design and state estimation are usually more straightforward for linear than for nonlinear dynamical systems, which has motivated the development of methods for quantifying the extent of nonlinearity in dynamical systems. Although many well-defined methods have been proposed for systems described by ordinary differential equations, such methods are not as well explored for dynamical systems described by PDEs and descriptor systems that represent most chemical processes. This paper reviews, discusses, and compares methods for the definition and computation of nonlinearity measures. The measures are categorized in terms of open- vs. closed-loop control topologies, theoretical vs. numerically computed, state transformation dependency, input scaling dependency, linearization vs. optimized linear modeling vs. average linear modeling, applicability to unstable dynamical systems, and applicability to the right-hand side of the state equation or to input-output relationships. Then extensions of the nonlinearity measures are discussed for hybrid systems and those described by coupled differential, integral, and algebraic equations, often referred to as descriptor/singular systems.

关键词： nonlinear systems process control nonlinear measures descriptor systems distributed parameter systems

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A convolutional neural network-based diagnostic method using resting-state electroencephalograph signals for major depressive and bipolar disorders

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Biomedical Signal Processing and control 2022年第PartB期72卷 103370-103370页

作者： Lei, Yu Belkacem, Abdelkader Nasreddine Wang, Xiaotian Sha, Sha Wang, Changming Chen, Chao Beijing Institute of Basic Medical Sciences Beijing China Department of Computer and Network Engineering College of Information Technology United Arab Emirates University Al Ain United Arab Emirates School of Artificial Intelligence Xidian University Xian China National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders Beijing Anding Hospital Capital Medical University Beijing China Brain-inspired Intelligence and Clinical Translational Research Center/Department of Neurosurgery Xuanwu Hospital Capitap Medical University Beijing China Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology China Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China

Background: Early and accurate diagnosis of bipolar and major depressive disorders is important in clinical practice. However, no diagnostic biomarkers can discriminate bipolar from major depressive disorder with high accuracy at present. Methods: We propose a novel convolutional neural network architecture using multichannel raw resting-state electroencephalograph signals to differentiate bipolar disorder from major depressive disorder. This method has great potential in diagnosing mental disorders. In total, 101 patients with major depressive disorder, 82 patients with bipolar disorder, and 81 healthy controls were assessed. Clinical diagnosis was performed by psychiatrists based on the Diagnostic and Statistical Manual of Mental Disorders, fifth edition. Participants were instructed to fix their eyes on a cross on the monitor during the collection of resting-state electroencephalograph signals. Results: A classification mean accuracy of 96.88% was achieved using the resting-state electroencephalograph signals with the 10-fold cross-validation method. The results indicate that deep neural networks could learn efficient feature patterns automatically without manual feature selections. Both statistical analysis and feature visualization on different brain regions showed that the classification performance and the learned features of the proposed model were consistent with that obtained in neurobiological studies on bipolar and major depressive disorders. Conclusions: The combined use of deep neural networks and electroencephalograph signals is an effective approach for the computer-aided diagnosis of bipolar and major depressive disorders. © 2021 Elsevier Ltd

关键词： Deep neural networks

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Prediction Model of User Purchase Behavior Based on Machine Learning

Prediction Model of User Purchase Behavior Based on Machine ...

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IEEE International Conference on Mechatronics and Automation

作者： Xiang Zhai Peng Shi Liang Xu Yalong Wang Xi Chen Tianjin Key Laboratory for Control Theory & Applications in Complicated Systems and School of Electrical and Electronic Engineering Tianjin University of Technology Binshui Xidao 391 Tianjin China AVIC Luoyang Institute of Electro-Optical Equipment Luoyang China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

In recent years, with the continuous popularity of the Internet, the number of online shopping users in my country has reached 639 million, which contains huge commercial value. In order to maintain the prosperity, diversity and order of merchants, and fully meet consumers' one-stop shopping needs, it is necessary to analyze and predict user purchase behaviors more accurately. This article uses the interaction between users and products within a certain period of time provided by *** to predict whether users will place orders in designated categories and shops in the next week. First of all, we aim at the problem that the original features are not strongly related, and use feature fusion to construct strong features. For high-dimensional systems that are slow to calculate and prone to overfitting, we use random forests to filter features, reducing the complexity of the model. Finally, a fusion algorithm based on XGBoost and LightGBM is proposed to predict the purchase behavior of users, and the prediction effects of random forest and GBDT algorithm are compared at the same time. The results show that the fusion model has the best prediction effect, and can help merchants more accurately conduct marketing activities to potential customers based on the prediction results.

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Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network 23rd

Cerebrovascular Segmentation in MRA via Reverse Edge Attenti...

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23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Hao Xia, Likun Song, Ran Yang, Jianlong Hao, Huaying Liu, Jiang Zhao, Yitian College of Information Engineering Capital Normal University Beijing China Cixi Institute of Biomedical Engineering Ningbo Institute of Industrial Technology Chinese Academy of Sciences Ningbo China School of Control Science and Engineering Shandong University Jinan China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China International Science and Technology Cooperation Base of Electronic System Reliability and Mathematical Interdisciplinary Capital Normal University Beijing China Laboratory of Neural Computing and Intelligent Perception Capital Normal University Beijing China Beijing Advanced Innovation Center for Imaging Theory and Technology Capital Normal University Beijing China

ISBN: (纸本)9783030597245

Automated extraction of cerebrovascular is of great importance in understanding the mechanism, diagnosis, and treatment of many cerebrovascular pathologies. However, segmentation of cerebrovascular networks from magnetic resonance angiography (MRA) imagery continues to be challenging because of relatively poor contrast and inhomogeneous backgrounds, and the anatomical variations, complex geometry and topology of the networks themselves. In this paper, we present a novel cerebrovascular segmentation framework that consists of image enhancement and segmentation phases. We aim to remove redundant features, while retaining edge information in shallow features when combining these with deep features. We first employ a Retinex model, which is able to model noise explicitly to aid removal of imaging noise, as well as reducing redundancy within an image and emphasizing the vessel regions, thereby simplifying the subsequent segmentation problem. Subsequently, a reverse edge attention module is employed to discover edge information by paying particular attention to the regions that are not salient in high-level semantic features. The experimental results show that the proposed framework enables the reverse edge attention network to deliver a reliable cerebrovascular segmentation. © 2020, Springer Nature Switzerland AG.

关键词： Image enhancement

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Tracking control of Wheeled Mobile Robots with Communication Delay and Data Loss

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Journal of Systems Science & Complexity 2018年第4期31卷 927-945页

作者： ZHANG Tian-Yong LIU Guo-Ping Center For Control Theory and Guidance Technology Harbin Institute of Technology Harbin 150001 China School of Engineering University of South Wales Pontypridd CF37 1DL UK

This paper considers the tracking control problem of a wheeled mobile robot under situation of communication delay and consecutive data packet dropouts in the feedback channel. A tracking controller in discrete-time domain for the case of ideal network condition is first derived, and then the networked predictive controller as well as two algorithms for dealing with communication delay and consecutive data packet dropouts are proposed. Simulation and experimental results verify the realizability and effectiveness of the proposed algorithms.

关键词： Communication delay consecutive data loss networked predictive control trajectory tracking wheeled mobile robot

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