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arXiv 2021年

作者： Markolf, Lukas Stursberg, Olaf Control and System Theory Group Dept. of Electrical Engineering and Computer Science University of Kassel Germany

This work considers artificial feed-forward neural networks as parametric approximators in optimal control of discrete-time systems. Two different approaches are introduced to take polytopic input constraints into account. The first approach determines (sub-)optimal inputs by the application of gradient methods. Closed-form expressions for the gradient of general neural networks with respect to their inputs are derived. The approach allows to consider state-dependent input constraints, as well as to ensure the satisfaction of state constraints by exploiting recursive reachable set computations. The second approach makes use of neural networks with softmax output units to map states into parameters, which determine (sub-)optimal inputs by a convex combination of the vertices of the input constraint set. The application of both approaches in model predictive control is discussed, and results obtained for a numerical example are used for illustration. Copyright © 2021, The Authors. All rights reserved.

关键词： Discrete time control systems

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Using AoI forecasts in communicating and robust distributed model-predictive control

arXiv

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arXiv 2021年

作者： Hahn, Jannik Schoeffauer, Richard Wunder, Gerhard Stursberg, Olaf Control and System Theory Dept. of Electrical Engineering and Computer Science University of Kassel Germany Heisenberg CIT Group Free University of Berlin Dept. of Mathematics and Computer Science Germany

In order to enhance the performance of cyber-physical systems, this paper proposes the integrated design of distributed controllers for distributed plants and the control of the communication network. Conventional design methods use static interfaces between both entities and therefore rely on worst-case estimations of communication delay, often leading to conservative behavior of the overall system. By contrast, the present approach establishes a robust distributed model-predictive control scheme, in which the local subsystem controllers operate under the assumption of a variable communication schedule that is predicted by a network controller. Using appropriate models for the communication network, the network controller applies a predictive network policy for scheduling the communication among the subsystem controllers across the network. Given the resulting time-varying predictions of the age of information, the paper shows under which conditions the subsystem controllers can robustly stabilize the distributed system. To illustrate the approach, the paper also reports on the application to a vehicle platooning scenario. © 2021, CC BY.

关键词： Networked control systems

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Numerical Evaluation of a Robust Self-Triggered MPC Algorithm

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IFAC-PapersOnLine 2016年第22期49卷 151-156页

作者： Brunner, Florian D. Heemels, W.P.M.H. Allgöwer, Frank Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 Stuttgart70569 Germany Control System Technology Group Department of Mechanical Engineering Eindhoven University of Technology Netherlands

We present numerical examples demonstrating the efficacy of a recently proposed self-triggered model predictive control scheme for disturbed linear discrete-time systems with hard constraints on the input and state. In order to reduce the amount of communication between the controller and the actuator, the control input is not re-computed at each point in time but only at certain sampling instances. These instances are determined in a self-triggered fashion in the sense that at every sampling instant the next sampling instant is computed as a function of the current system state. A compact set in the state space, whose size is a design parameter in the control scheme, is stabilized. © 2016

关键词： Discrete time control systems Digital control systems Model predictive control Predictive control systems Robustness (control systems) control inputs control schemes Design parameters Hard constraints Linear discrete time systems Predictive control Sampling instants Self triggered controls

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Robust Event-Triggered MPC for Constrained Linear Discrete-Time systems with Guaranteed Average Sampling Rate

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IFAC-PapersOnLine 2015年第23期48卷 117-122页

We propose a robust event-triggered model predictive control (MPC) scheme for linear time-invariant discrete-time systems subject to bounded additive stochastic disturbances and hard constraints on the input and state. For given probability distributions of the disturbances acting on the system, we design event conditions such that the average frequency of communication between the controller and the actuator in the closed-loop system attains a given value. We employ Tube MPC methods to guarantee robust constraint satisfaction and a robust asymptotic bound on the system state. Moreover, we show that based on a given periodically updated Tube MPC scheme, an appropriate event-triggered MPC scheme can be designed, with the same guarantees on constraints and region of attraction, but with a reduced number of average communications. © 2015, IFAC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Discrete time control systems Closed loop systems Digital control systems Linear control systems Model predictive control Predictive control systems Probability distributions Robustness (control systems) Stochastic models Stochastic systems Constraint Satisfaction Discrete time systems Event triggered controls Linear discrete time systems Linear time invariant Predictive control Region of attraction Stochastic disturbances

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Communication scheduling in robust self-triggered MPC for linear discrete-time systems 5

Communication scheduling in robust self-triggered MPC for li...

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5th IFAC Workshop on Distributed Estimation and control in Networked systems, NecSys 2015

作者： Brunner, Florian D. Gommans, T.M.P. Heemels, W.P.M.H. Allgöwer, Prank Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 Stuttgart70569 Germany Control System Technology Group Department of Mechanical Engineering Eindhoven University of Technology Netherlands

We consider a networked control system consisting of a physical plant, an actuator, a sensor, and a controller that is connected to the actuator and sensor via a communication network. The plant is described by a linear discrete-time system subject to additive disturbances. In order to reduce the required number of communications in the system, we propose a robust self-triggered model predictive controller based on rollout techniques that robustly asymptotically stabilizes a certain periodic sequence of sets in the state space while guaranteeing robust satisfaction of hard state and input constraints. At periodically occurring scheduling times, the self-triggered model predictive control algorithm determines the times at which the control input and plant measurement are updated in the time span until the next scheduling time. We establish a certain upper bound on the average sampling rate in the closed-loop system. Moreover, we show how increasing the asymptotic bound on the system state, which is a design parameter in the control scheme, can be used to further reduce the average number of communications in the system. © 2015, IF AC (International Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Closed loop systems

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Communication Scheduling in Robust Self-Triggered MPC for Linear Discrete-Time systems ∗

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IFAC-PapersOnLine 2015年第22期48卷 132-137页

作者： Florian D. Brunner T.M.P. Gommans W.P.M.H. Heemels Frank Allgöwer Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 70569 Stuttgart Germany Control System Technology Group Department of Mechanical Engineering Eindhoven University of Technology The Netherlands

关键词： predictive control self-triggered control robustness

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Resource-aware set-valued estimation for discrete-time linear systems

Resource-aware set-valued estimation for discrete-time linea...

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IEEE Annual Conference on Decision and control

作者： F. D. Brunner T. M. P. Gommans W. P. M. H. Heemels F. Allgowert Institute for Systems Theory and Automatic Control (IST) University of Stuttgart 70550 Stuttgart Germany Control System Technology group Department of Mechanical Engineering Eindhoven University of Technology

ISBN: (纸本)9781479978878

In this paper, we propose a self-triggered estimator for discrete-time linear systems subject to unknown but bounded disturbances affecting both the system states and outputs. The proposed self-triggered estimator is a set-valued estimator that employs rollout techniques to reduce the communication between the sensors and the estimator with respect to a periodic sampling strategy. Moreover, at each time instant it provides an estimate of the plant state and a guaranteed bound on the difference between the true plant state and the estimate.

关键词： Linear system discrete-time discrete time systems Unknown Sensors Communication Research

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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VVAF - Worst case & safety analysis tools for autonomous rendezvous system

VVAF - Worst case & safety analysis tools for autonomous ren...

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AIAA Guidance, Navigation, and control Conference

作者： Paulino, Nuno Di Sotto, Emanuele Salehi, Sohrab Kron, Aymeric Hamel, Jean-François Wang, Wenfei Menon, Prathyush P. Bates, Declan G. Papachristodoulou, Antonis Maier, Christoph GMV Tres Cantos Madrid Spain Rhea System at ESA-ESTEC Noordwijk Netherlands NGC Aerospace Mouans Sartoux France NGC Aerospace Sherbrooke Canada Control and Instrumentation Research Group Department of Engineering University of Leicester Leicester United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Institute for Systems Theory and Automatic Control University of Stuttgart Stuttgart Germany

ISBN: (纸本)9781600869624

The traditional verification and validation (V&V) process can produce sufficiently safe and reliable systems in the demanding aerospace field. But this comes at great cost and effort, with the necessary simulation of all possible operational conditions for increasingly complex control systems involving substantial autonomy. It is of interest to go beyond the statistical confidence of traditional Monte Carlo methods. New V&V methods need to cope with a large number of mission parameters and mode transitions. They must identify the worst combinations of perturbations, uncertainties and failures. Two classes of frameworks are investigated in the VVAF project: integrated V&V frameworks (IVVF) supported by simulation based analysis techniques, either integrated or with local analysis techniques to establish the global system properties;and enhanced V&V frameworks (EVVF), based on mathematical proof. Two EVVF techniques are explored: Barrier-construction and Reachability-set analysis. These guarantee fulfillment of globally exact safe operation, by the construction of barrier certificates considering nonlinearities, uncertainties and constraints. In addition, two IVVF techniques are developed: Analytical-based (AIVV) and Optimization-based (OIVV). AIVV consists of integrating and embedding frequency domain LFT-based mu-analysis techniques to exclude regions with guaranteed performance and/or identify the most critical areas, within a Monte Carlo simulation based V&V. With as few runs as possible of the non linear time simulator, local analyses can steer the global search towards the worst case. OIVV uses optimization techniques, also linked to the simulator, to identify and characterize the system worst case. Several heuristic techniques are evaluated for global parameters search, which is then refined using local optimization techniques to locate and characterize the worst case. The AIVV can also aid the OIVV by identifying search regions and promising initialization poi

关键词： Monte Carlo methods

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