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European control Conference (ECC)

作者： Pawel Dabkowski Krzysztof Galkowski Eric Rogers Michael Sebek Faculty of Physics Nicolaus Copernicus University Torun Poland Institute of Control and Computation Engineering University of Zielona Gora Zielona Gora Poland Electronics and Computer Science University of Southampton Southampton UK Department of Control Engineering Czech Technical University in Prague Praha 6 Czech Republic

Repetitive processes are a class of 2D systems that can be used to model physical systems and also there are applications, such as iterative learning control, where using a repetitive processes setting for design has advantages over alternatives. In most cases, it is discrete dynamics that are basis for design and often there will be a need to deal with uncertainty in the process model and the effects of disturbances. This paper develops new algorithms for these tasks that are computed using linear matrix inequalities when the uncertainty is modeled using two standard representations and disturbance attenuation is measured by an norm. The extension of these algorithms to control law design is also developed.

关键词： Asymptotic stability Process control Attenuation Stability criteria Numerical stability Two dimensional displays

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Predictive Fault Tolerant control for LPV systems using model reference

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IFAC-PapersOnLine 2015年第21期48卷 30-35页

作者： Acevedo-Valle, Juan M. Puig, Vicenç Tornil-Sin, Sebastian Witczak, Marcin Rotondo, Damiano Institut de Robótica i Informática Industrial UPC-CSIC Spain Institute of Control and Computation Engineering University of Zielona Gora Poland

The present work proposes a Fault Tolerant control (FTC) methodology for nonlinear discrete-time systems that can be modeled as Linear Parameter Varying (LPV) systems. The proposed approach relies on the modeling of faults as additional scheduling parameters of the LPV model for the controlled system and it uses a triple loop architecture. The inner control loop is designed by means of the standard H2/H∞ control methodologies based on Linear Matrix Inequalities (LMIs). The design takes into account a prespecified set of faults and the ranges of their magnitudes that are wanted to be tolerated and it assumes the availability of on-line fault estimations provided by a Fault Detection and Isolation (FDI) module. The resulting controller tries to compensate the system faults in order to maintain a satisfactory closed-loop dynamic performance, but it does not take into account possible system input and state constraints associated to actuator saturation and other physical limitations. Thus, an intermediate control loop determines the actual compensation feasibility using set invariance theory. And, when it is needed, it applies suitable additive predictive control actions that enlarge the invariant set, trying to assure that the current state remains inside the enlarged invariant set. Finally, an outer loop implements a model REFERENCE control that allows REFERENCE tracking. The use of the proposed FTC methodology is illustrated through its application to the well-known quadruple tank system benchmark. © 2015

关键词： Linear matrix inequalities

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Iterative learning control laws with full dynamics

Iterative learning control laws with full dynamics

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American control Conference

作者： Lukasz Hladowski Yiyang Chen Weronika Nowicka Krzysztof Galkowski Eric Rogers Institute of Control and Computation Engineering University of Zielona Gora ul. Podgorna 50 65-246 Poland Department of Electronics and Computer Science University of Southampton SO17 1BJ UK

ISBN: (纸本)9781467386838

Iterative learning control can be applied to systems that execute the same finite duration task over and over again. The distinguishing feature is the use of information from previous executions to construct the input to the next one in the sequence, including time domain information that would be non-causal in standard control systems. Many algorithms or laws have been developed for an ever increasing range of applications. This paper develops a new law which is fully dynamic, not static, when implemented. Experimental verification results are also given.

关键词： Stability analysis Asymptotic stability Convergence Process control Standards State-space methods Iterative learning control State-space methods asymptotic stability region Process control Stability analysis Iterative learning control Converge

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A tactile-based fabric learning and classification architecture

A tactile-based fabric learning and classification architect...

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International Conference on Information and Automation for Sustainability (ICIAfS)

作者： A.A. Khan M. Khosravi S. Denei P. Maiolino W. Kasprzak F. Mastrogiovanni G. Cannata Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genoa Genoa Italy Computing Department University of London London United Kingdom Institute of Control and Computation Eng. Warsaw University of Technology Warszawa Poland

ISBN: (纸本)9781509061334

This paper proposes an architecture for tactile-based fabric learning and classification. The architecture is based on a number of SVM-based learning units, which we call fabric classification cores, specifically trained to discriminate between two fabrics. Each core is based on a specific subset of the fully available set of features, on the basis of their discriminative value, determined using the p-value. During fabric recognition, each core casts a vote. The architecture collects votes and provides an overall classification result. We tested seventeen different fabrics, and the result showed that classification errors are negligible.

关键词： Fabrics Feature extraction Sensor phenomena and characterization Tactile sensors Frequency modulation Frequency-domain analysis

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Grasp planning taking into account the external wrenches acting on the grasped object 10

Grasp planning taking into account the external wrenches act...

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10th International Workshop on Robot Motion and control, RoMoCo 2015

作者： Seredyński, Dawid Winiarski, Tomasz Banachowicz, Konrad Zieliński, Cezary Institute of Control and Computation Engineering Warsaw University of Technology ul. Nowowiejska 15/19 Warsaw00-665 Poland

ISBN: (纸本)9781479970438

The paper presents an outline of the specification of a robot controller used in manipulation tasks requiring object grasping by a multi-fingered gripper and interaction with the environment. The specification assumes that the robot is represented as an embodied agent composed of real and virtual effectors and receptors and the control subsystem. The actions of those subsystems are defined by finite state machines invoking in each of their states appropriate behaviours. The presentation focuses on grasp planning and execution. © 2015 IEEE.

关键词： Specifications

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Classification of breast cancer cytological specimen using convolutional neural network

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Journal of Physics: Conference Series 2017年第1期783卷

作者： Michał Żejmo Marek Kowal Józef Korbicz Roman Monczak University of Zielona Góra Institute of Control and Computation Engineering 65-516 Szafrana 2 Zielona Góra Poland Department of Pathomorphology Regional Hospital in Zielona Góra Zyty 26 65-046 Zielona Góra Poland

The paper presents a deep learning approach for automatic classification of breast tumors based on fine needle cytology. The main aim of the system is to distinguish benign from malignant cases based on microscopic images. Experiment was carried out on cytological samples derived from 50 patients (25 benign cases + 25 malignant cases) diagnosed in Regional Hospital in Zielona Góra. To classify microscopic images, we used convolutional neural networks (CNN) of two types: GoogLeNet and AlexNet. Due to the very large size of images of cytological specimen (on average 200000 × 100000 pixels), they were divided into smaller patches of size 256 × 256 pixels. Breast cancer classification usually is based on morphometric features of nuclei. Therefore, training and validation patches were selected using Support Vector Machine (SVM) so that suitable amount of cell material was depicted. Neural classifiers were tuned using GPU accelerated implementation of gradient descent algorithm. Training error was defined as a cross-entropy classification loss. Classification accuracy was defined as the percentage ratio of successfully classified validation patches to the total number of validation patches. The best accuracy rate of 83% was obtained by GoogLeNet model. We observed that more misclassified patches belong to malignant cases.

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A neural network-based robust unknown input observer design: Application to wind turbine

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IFAC-PapersOnLine 2015年第21期48卷 263-270页

作者： Witczak, Piotr Patan, Krzysztof Witczak, Marcin Puig, Vincenc Korbicz, Jozef Institute of Control and Computation Engineering University of Zielona Góra ul. Podgórna 50 65–246 Zielona Góra Poland Pau Gargallo 5 Barcelona08028 Spain

The paper deals with the problem of robust unknown input observer design for the neural-network based models of non-linear discrete-time systems. Authors review the recent development in the area of robust observers for non-linear discrete-time systems and proposes less restrictive procedure for design the H∞ observer. The approach guaranties simultaneously the predefined disturbance attenuation level (with respect to state estimation error) and convergence of the observer. The main advantage of the design procedure is its simplicity. The paper presents an unknown input observer design that reduced to a set of linear matrix inequalities. The final part of the paper presents an illustrative example concerning wind turbine. © 2015

关键词： Neural networks

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Predictive fault tolerant control for LPV systems using model reference 9

Predictive fault tolerant control for LPV systems using mode...

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9th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2015

作者： Acevedo-Valle, Juan M. Puig, Vicenç Tornil-Sin, Sebastian Witczak, Marcin Rotondo, Damiano Institut de Robòtica i Informàtica Industrial UPC-CSIC Spain Institute of Control and Computation Engineering University of Zielona Gora Poland

The present work proposes a Fault Tolerant control (FTC) methodology for nonlinear discrete-time systems that can be modeled as Linear Parameter Varying (LPV) systems. The proposed approach relies on the modeling of faults as additional scheduling parameters of the LPV model for the controlled system and it uses a triple loop architecture. The inner control loop is designed by means of the standard H2/H∞ control methodologies based on Linear Matrix Inequalities (LMIs). The design takes into account a prespecified set of faults and the ranges of their magnitudes that are wanted to be tolerated and it assumes the availability of on-line fault estimations provided by a Fault Detection and Isolation (FDI) module. The resulting controller tries to compensate the system faults in order to maintain a satisfactory closed-loop dynamic performance, but it does not take into account possible system input and state constraints associated to actuator saturation and other physical limitations. Thus, an intermediate control loop determines the actual compensation feasibility using set invariance theory. And, when it is needed, it applies suitable additive predictive control actions that enlarge the invariant set, trying to assure that the current state remains inside the enlarged invariant set. Finally, an outer loop implements a model reference control that allows reference tracking. The use of the proposed FTC methodology is illustrated through its application to the well-known quadruple tank system benchmark. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Linear matrix inequalities

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Reconfigurable agent architecture for robots utilising cloud computing

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2015年 351卷 253-264页

作者： Szlenk, Marcin Zieliński, Cezary Figat, Maksym Kornuta, Tomasz Warsaw University of Technology Institute of Control and Computation Engineering Warsaw Poland

The paper presents the general architecture of the control system of a companion robot. As companion robots have to perform diverse and complex tasks, while computational capabilities of the local robot control computer are limited, the control system is split between the robot and the cloud. Moreover, the system is composed of agents, that are arranged into an application on demand of the user. Some of those agents are created on the robot and some in the cloud. As the requirements change the composition of the system changes too. © Springer International Publishing Switzerland 2015.

关键词： Machine design

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Simulation-based evaluation of robot-assisted wireless sensors positioning

Advances in Intelligent Systems and Computing

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Advances in Intelligent Systems and Computing 2015年 351卷 181-190页

作者： Niewiadomska-Szynkiewicz, Ewa Sikora, Andrzej Institute of Control and Computation Engineering Warsaw University of Technology Warsaw Poland Warsaw Poland

Wireless sensor networks (WSNs) can significantly enhance the capability to monitor and control working environments. However, to fulfill the issued sensing tasks the network topology with desired properties (e.g. coverage, connectivity, lifetime, etc.) has to be created. The adequate deployment of sensor devices in the sensing area is needed for acquiring and managing data. In many applications it is necessary to use mobile devices that can move the sensors to the desirable positions in a given workspace. In this paper, we investigate the problem of mobile sensors deployment to get high coverage of a sensing area and ensure network connectivity. We discuss and compare two approaches to the positioning of sensors - pre-defined and self-configuring deployments. Finally, we propose a two-phase approach to WSN formation process, in which created preliminarily regular network topology is adapted to a given application scenario. We assess the quality of deployment strategies based on the results of simulations. © 2015, Springer International Publishing Switzerland.

关键词： Wireless sensor networks

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