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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Jakub Sawulski Maciej Ławryńczuk Institute of Control and Computation Engineering Warsaw University of Technology Warsaw Poland

This work discusses tuning of Model Predictive control (MPC) algorithms by means of some global optimisation methods. For test purposes the Dynamic Matrix control (DMC) algorithm applied to a Multiple-Input Multiple-Output (MIMO) process with 4 manipulated and 3 controlled variables is considered. The tuned parameters include prediction and control horizons as well as the weights of the minimised MPC cost-function. Four global optimisation methods are considered: the Particle Swarm Optimisation method, the Firefly Algorithm, the Grey Wolf Optimiser and the Jaya algorithm. They are compared in terms of the ability to find the best solution and convergence. The obtained results show that global optimisation methods can be successfully used in this type of tasks.

关键词： Process control Prediction algorithms Monte Carlo methods Heuristic algorithms Tuning Optimization methods

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Constrained Observer Based Iterative Learning control Design in the Repetitive Process Setting

Constrained Observer Based Iterative Learning Control Design...

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IEEE Annual Conference on Decision and control

作者： Julia Emelianova Pavel Pakshin Krzysztof Galkowski Eric Rogers Arzamas Polytechnic Institute of R.E. Alekseev Nizhny Novgorod State Technical University Arzamas Institute of Control and Computation Engineering University of Zielona Gora Zielona Gora Poland School of Electronics and Computer Science University of Southampton Southampton UK

ISBN: (数字)9781728113982

ISBN: (纸本)9781728113999

Iterative learning control is a well established method applicable to systems that repeat the same finite duration task over and over again. The mechanism is to use information from the previous repetition to update the control input for the next repetition and thereby sequentially improve performance. Given that it directly regulates the control input, there may be cases where the levels of control action breach the safe operating range of the actuators used. This paper develops a new design for the case when a limit is placed on the control action allowed. The analysis represents the dynamics in a 2D systems setting and uses the stability theory for the particular case of repetitive processes as a basis for analysis and design.

关键词： Two dimensional displays Robots Stability analysis Observers Feedback control Iterative learning control Task analysis

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Improved PI based indirect-type ILC for batch processes with time-varying uncertainties: A new perspective

Improved PI based indirect-type ILC for batch processes with...

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第37届中国控制会议

作者： Shoulin Hao Tao Liu Wojciech Paszke Krzysztof Ga?kowski Institute of Advanced Control Technology Dalian University of Technology Institute of Control and Computation Engineering University of Zielona Góra

This paper proposes an improved PI based indirect-type iterative learning control for batch processes with timevarying uncertainties from a new perspective. The proposed ILC law can be designed independent of the initial PI tuning which primarily aims at holding the robust stability of the closed-loop system due to that only the setpoint command is adjusted by ILC. The feedforward control used in the recently developed indirect-type ILC methods is replaced by retuning the closed-loop PI controller, while the redundant learning information from the current and previous cycles could be reduced compared with the existing literature. Correspondingly, the control structure is significantly simplified with less design parameters, while achieving the similar convergence performance along the batch direction. A robust ILC updating law along with retuned PI parameters are derived by solving a linear matrix inequality and results in the robust stability of the closed-loop system. An illustrative example from the literature is adopted to demonstrate the effectiveness of the proposed method.

关键词： Batch process Time-varying uncertainties PI controller Iterative learning control PI retuning

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Robust and Asymmetric Assessment of the Benefits from Improved control – Industrial Validation

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IFAC-PapersOnLine 2018年第18期51卷 815-820页

作者： Domaski, Pawel D. Golonka, Sebastian Marusak, Piotr M. Moszowski, Bartosz Institute of Control and Computation Engineering Warsaw University of Technology ul. Nowowiejska 15/19 Warsaw00-665 Poland Grupa Azoty Zaklady Azotowe Kdzierzyn S.A. ul. Mostowa 30 A skr. poczt. 163 Kdzierzyn - Kole47-220 Poland

Quality of the control system significantly contributes to the overall process technological and financial results. Plant throughput, environmental footprint and energy consumption push plants towards their technological limitations requiring better operation closer to constraints. Any improvement initiative should be predated with the estimation of the potential benefits associated with the rehabilitation project. The same applies to the control improvements. The assessment is always based on the performance indicators. Classical the same limit method is based on the Gaussian approach. However, investigation of industrial data frequently is not compliant with normal assumption about the properties of the variables. This paper extends the Gaussian approach with the use of robust (Huber) statistics and asymmetric Pearson type IV probability density function. © 2018

关键词： Quality control Energy utilization Probability density function Environmental footprints Pearson distribution Performance assessment Performance indicators Rehabilitation projects Robust statistics same limit method Technological limitations

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Remote control of robotic manipulator under delays in communication channel

Remote control of robotic manipulator under delays in commun...

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International Conference on Methods and Models in Automation and Robotics

作者： Kamil Klimkowicz Maciej Patan University of Zielona Góra Institute of Control and Computation Engineering Zielona Gora Poland

This work is intended as an attempt to design an effective remote control scheme for robotic manipulators under the presence of delays in the communication channel. Two control strategies in the trajectory tracking problem for robotic manipulator are investigated. First, the classical resolved rate motion control technique is adopted and enhanced with experimental delay prediction. As an alternative approach to circumvent the inherent uncertainty of the controlled system is to adopt the iterative learning scheme with error estimation based on the observations from previous trials of robot movements. Both methods are successfully implemented on within the cascade control scheme within the framework of client-server TCP/IP communication architecture. Then, their performance is compared experimentally on the real platform of KUKA Agilus KR6 industrial manipulator with KR C4 compact controller.

关键词： Delays Robot kinematics Manipulators Service robots Servers

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Post-mortem iris decomposition and its dynamics in morgue conditions

arXiv

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arXiv 2019年

作者： Trokielewicz, Mateusz Czajka, Adam Maciejewicz, Piotr Warsaw University of Technology Institute of Control and Computation Engineering Nowowiejska 15/19 Warsaw00665 Poland Department of Computer Science and Engineering University of Notre Dame Notre DameIN46556 United States Department of Ophthalmology Medical University of Warsaw Lindleya 4 Warsaw02005 Poland

With increasing interest in employing iris biometrics as a forensic tool for identification by investigation authorities, there is a need for a thorough examination and understanding of post-mortem decomposition processes that take place within the human eyeball, especially the iris. This can prove useful for fast and accurate matching of ante-mortem with post-mortem data acquired at crime scenes or mass casualties, as well as for ensuring correct dispatching of bodies from the incident scene to a mortuary or funeral homes. Following these needs of forensic community, this paper offers an analysis of the coarse effects of eyeball decay done from a perspective of automatic iris recognition point of view. Therefore, we analyze postmortem iris images acquired in both visible light as well as in near-infrared light (860 nm), as the latter wavelength is used in commercial iris recognition systems. Conclusions and suggestions are provided that may aid forensic examiners in successfully utilizing iris patterns in post-mortem identification of deceased subjects. Initial guide-lines regarding the imaging process, types of illumination, resolution are also given, together with expectations with respect to the iris features decomposition rates. Copyright © 2019, The Authors. All rights reserved.

关键词： Biometrics

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Least Squares and Instrumental Variables Identification of Polynomial Wiener Systems

Least Squares and Instrumental Variables Identification of P...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Andrzej Janczak Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland

The paper is addressed to a combined least squares (LS) and instrumental variables (IV) approach to identification of polynomial Wiener systems. It is assumed that the inverse nonlinear element can be described by a polynomial. It is shown that LS parameter estimates of polynomial Wiener system are inconsistent and to circumvent the consistency problem, the well-known IV method is used. To avoid the parameter redundancy, three different methods of calculation inverse nonlinear function parameters from the transformed model parameters are proposed. The simulation results are included that confirm the practical feasibility of the proposed approach. The highest parameter estimation accuracy has been obtained via solving an overdetermined system of equations, only insignificantly lower accuracy has been obtained using the mean value approach, and the lowest one calculating the inverse nonlinear function parameters from the sums of transformed Wiener model parameters.

关键词： Mathematical model Instruments Parameter estimation Nonlinear dynamical systems Predictive control Predictive models

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Iterative Learning control for a Sub-Class of Uncertain 2D Systems

Iterative Learning Control for a Sub-Class of Uncertain 2D S...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Bartłomiej Sulikowski Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland

In this paper the Iterative Learning control (ILC) scheme is presented for a subclass of 2D systems, i.e. spatially interconnected systems. Also, the existence of norm bounded uncertainty in the model is considered. Firstly, the 2D model is transformed into the uncertain 1D equivalent model using the lifting procedure. Next, in order to lead the model output to the prescribed reference signal, the ILC scheme is implemented. It attains the structure of the controlled differential linear repetitive process (LRP), hence assuring the stability along the trial of the underlying LRP automatically leads to the feasible ILC and the convergence to zero of the tracking error for the originally considered model. In what follows, the LMI based approach towards robust ILC controller design is given as a main result of this paper. To present the applicability of the proposed approach is applied for a subclass of spatially interconnected systems, i.e. the particular case of ladder RLC circuits.

关键词： Uncertainty Two dimensional displays Interconnected systems Mathematical model Integrated circuit modeling Convergence RLC circuits

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Design of diagnostic estimators for an automated guided vehicle

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IFAC-PapersOnLine 2018年第24期51卷 1004-1009页

作者： Witczak, Marcin Stetter, Ralf Buciakowski, Mariusz Theilliol, Didier Kukurowski, Norbert Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra65-246 Poland Faculty of Mechanical Engineering University of Applied Sciences Ravensburg-Weingarten Doggenriedstraße Weingarten Germany Centre de Recherche en Automatique de Nancy CRAN-UMR 7039 Nancy-Universite CNRS Vandoeuvre-les-Nancy CedexF-54506 France

The paper deals with the design of virtual sensors of longitudinal forces as well as torques acting onto an automated guided vehicle. These virtual sensors are based on a measurement set associated with accelerations, velocities and yaw rate sensors mounted on the robot. Subsequently, the estimated forces and torques are used for diagnostic purposes concerning their appropriate distributions. The main novelty of the proposed approach is that it eliminates the necessity of incorporating wheel models, which usually impairs the performance of the approaches proposed in the literature. The final part of the paper presents experimental results, which clearly exhibit the performance of the proposed approach. © 2018

关键词： Automatic guided vehicles Automation Failure analysis Fault detection Mobile robots Automated guided vehicles Force balances Forces and torques Input estimation Longitudinal force observers Virtual sensor Yaw rate sensors

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FULLY TRANSFORMER-BASED BIOMARKER PREDICTION FROM COLORECTAL CANCER HISTOLOGY: A LARGE-SCALE MULTICENTRIC STUDY

arXiv

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arXiv 2023年

作者： Wagner, Sophia J. Reisenbüchler, Daniel West, Nicholas P. Niehues, Jan Moritz Veldhuizen, Gregory Patrick Quirke, Philip Grabsch, Heike I. van den Brandt, Piet A. Hutchins, Gordon G.A. Richman, Susan D. Yuan, Tanwei Langer, Rupert Jenniskens, Josien Christina Anna Offermans, Kelly Mueller, Wolfram Gray, Richard Gruber, Stephen B. Greenson, Joel K. Rennert, Gad Bonner, Joseph D. Schmolze, Daniel James, Jacqueline A. Loughrey, Maurice B. Salto-Tellez, Manuel Brenner, Hermann Hoffmeister, Michael Truhn, Daniel Schnabel, Julia A. Boxberg, Melanie Peng, Tingying Kather, Jakob Nikolas Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Technical University Dresden Dresden Germany Division of Pathology and Data Analytics Leeds Institute of Medical Research at St James’s University of Leeds Leeds United Kingdom Department of Pathology GROW School for Oncology and Developmental Biology Maastricht University Medical Center+ Maastricht Netherlands Department of Epidemiology Maastricht University Medical Center+ Maastricht Netherlands Heidelberg Germany Institute of Pathology und Molecular Pathology Johannes Kepler University Hospital Linz Linz Austria Gemeinschaftspraxis Pathologie Starnberg Germany Nuffield Department of Population Health University of Oxford Oxford United Kingdom Center for Precision Medicine Department of Medical Oncology City of Hope National Medical Center DuarteCA United States Department of Pathology City of Hope Comprehensive Cancer Center DuarteCA United States Department of Community Medicine & Epidemiology Lady Davis Carmel Medical Center Ruth & Bruce Rappaport Faculty of Medicine Technion-Israel Institute of Technology Israel Steve and Cindy Rasmussen Institute for Genomic Medicine Lady Davis Carmel Medical Center Technion Faculty of Medicine Clalit National Cancer Control Center Haifa Israel Precision Medicine Centre of Excellence Health Sciences Building The Patrick G Johnston Centre for Cancer Research Queen’s University Belfast Belfast United Kingdom Regional Molecular Diagnostic Service Belfast Health and Social Care Trust Belfast United Kingdom The Patrick G Johnston Centre for Cancer Research Queen’s University Belfast Belfast United Kingdom Department of Cellular Pathology Belfast Health and Social Care Trust Belfast United Kingdom Centre for Public Health Queen’s University Belfast Belfast United Kingdom Integrated Pathology Unit Institute for Cancer Research Royal Mars

Background: Deep learning (DL) can extract predictive and prognostic biomarkers from routine pathology slides in colorectal cancer. For example, a DL test for the diagnosis of microsatellite instability (MSI) in CRC has been approved in 2022. Current approaches rely on convolutional neural networks (CNNs). Transformer networks are outperforming CNNs and are replacing them in many applications, but have not been used for biomarker prediction in cancer at a large scale. In addition, most DL approaches have been trained on small patient cohorts, which limits their clinical utility. Methods: In this study, we developed a new fully transformer-based pipeline for end-to-end biomarker prediction from pathology slides. We combine a pre-trained transformer encoder and a transformer network for patch aggregation, capable of yielding single and multi-target prediction at patient level. We train our pipeline on over 9,000 patients from 10 colorectal cancer cohorts. Results: A fully transformer-based approach massively improves the performance, generalizability, data efficiency, and interpretability as compared with current state-of-the-art algorithms. After training on a large multicenter cohort, we achieve a sensitivity of 0.97 with a negative predictive value of 0.99 for MSI prediction on surgical resection specimens. We demonstrate for the first time that resection specimen-only training reaches clinical-grade performance on endoscopic biopsy tissue, solving a long-standing diagnostic problem. Interpretation: A fully transformer-based end-to-end pipeline trained on thousands of pathology slides yields clinical-grade performance for biomarker prediction on surgical resections and biopsies. Our new methods are freely available under an open source license. © 2023, CC BY.

关键词： Forecasting

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