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Uncertain AoI in Stochastic Optimal control of Networked and Constrained LTI Systems

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arXiv 2021年

作者： Hahn, Jannik Stursberg, Olaf Control and System Theory Dept. of Electrical Engineering and Computer Science University of Kassel Kassel34121 Germany

— This paper addresses finite-time horizon optimal control of single-loop networked control systems with stochastically modeled communication channel and disturbances. To cope with the uncertainties, an optimization-based control scheme is proposed which uses a disturbance feedback and the age of information as central aspects. The disturbance feedback is an extension of the control law used for balanced stochastic optimal control previously proposed for control systems without network. Balanced optimality is understood as a compromise between minimizing of expected deviations from the reference and minimization of the uncertainty of future states. Time-varying state constraints as well as time-invariant input constraints are considered, and the controllers are synthesized by semi-definite programs. © 2021, CC BY.

关键词： Networked control systems

LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle

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arXiv 2022年

作者： de Lange, Matthis H. Verhoek, Chris Preda, Valentin Tóth, Roland Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven5600MB Netherlands ESTEC European Space Agency Noordwijk2200AG Netherlands Systems and Control Lab Institute for Computer Science and Control Budapest1111 Hungary

Obtaining models that can be used for control is of utmost importance to ensure the guidance and navigation of spacecraft, like a Generic Parafoil Return Vehicle (GPRV). In this paper, we convert a nonlinear model of the atmospheric flight dynamics of an GPRV to a Linear Parameter-Varying (LPV) description, such that the LPV model is suitable for navigation control design. Automated conversion methods for nonlinear models can result in complex LPV representation, which are not suitable for controller synthesis. We apply several state-of-the-art techniques, including learning based approaches, to optimize the complexity and conservatism of the LPV embedding for an GPRV. The results show that we can obtain an LPV embedding that approximates the complex nonlinear dynamics sufficiently well, where the balance between complexity, conservatism and model performance is optimal. © 2022, CC BY-NC-SA.

关键词： Principal component analysis

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

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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

Assessment of Integrating Evacuated Tube-Based Domestic Electric/Solar Water Heater with an Aluminum Reflector for Managing Electrical Consumption

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SSRN

SSRN 2023年

作者： Sabry, Ahmad H. Al-Dujaili, Ayad Q. Oleiwi, Bushra Kadhim Alkubaisi, Yaser Mahmood Humaidi, Amjad J. Electrical Engineering Technical College Middle Technical University Baghdad Iraq Department of Control and Systems Engineering University of Technology-Iraq Baghdad Iraq Department of Computer Engineering Al-Nahrain University Al Jadriyah Bridge Baghdad64074 Iraq Department of Sustainability Management Dubai Academic Health Corporation Dubai4545 United Arab Emirates Control and Systems Eng. Dept. University of Technology Baghdad Iraq

Most places in the world have seen rapid increases in the need for power. It is essential to look for alternatives and energy-saving technologies because traditional fossil fuel power plants emit pollution in addition to the electricity they produce. This work considers a multi-generational heating system with the hybrid Electric/Solar Water Heaters (ESWHs) to replace electric water heaters (EWH)by combining the ESWH with an Aluminum reflector. This work experimental discusses the performance of the ESWH and the influence of placing a reflector plate underneath the evacuated tubes in Baghdad-Iraq. A test procedure to heat the water to a specified heating temperature over the whole day period was conducted to analyze the daily power consumption profile of the system. Four operational scenarios have been conducted including the mode;the ESWH as a solar water heater, ESWH as an electric water heater, ESWH without a reflector, and ESWH with a reflector plate. The experimental analysis confirmed that the presented multi-generational heating configuration could contribute to energy sustainably. The developed multi-generational heating system achieved an improvement in annual energy consumption of 15.4% over the hybrid electric/solar water heater and 46.2% over the traditional electrical water heater. © 2023, The Authors. All rights reserved.

关键词： Energy conservation

Beyond bandit feedback in online multiclass classification 21

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Beyond bandit feedback in online multiclass classification

Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Dirk van der Hoeven Federico Fusco Nicolò Cesa-Bianchi Dept. of Computer Science Università degli Studi di Milano Italy Dept. of Computer Control and Management Engineering Sapienza Università di Roma Italy DSRC & Dept. of Computer Science Università degli Studi di Milano Italy

ISBN: (纸本)9781713845393

We study the problem of online multiclass classification in a setting where the learner's feedback is determined by an arbitrary directed graph. While including bandit feedback as a special case, feedback graphs allow a much richer set of applications, including filtering and label efficient classification. We introduce GAP-PLETRON, the first online multiclass algorithm that works with arbitrary feedback graphs. For this new algorithm, we prove surrogate regret bounds that hold, both in expectation and with high probability, for a large class of surrogate losses. Our bounds are of order $B\sqrt{\rho KT}$, where B is the diameter of the prediction space, K is the number of classes, T is the time horizon, and ρ is the domination number (a graph-theoretic parameter affecting the amount of exploration). In the full information case, we show that GAPPLETRON achieves a constant surrogate regret of order B2K. We also prove a general lower bound of order max {B2K, √T} showing that our upper bounds are not significantly improvable. Experiments on synthetic data show that for various feedback graphs our algorithm is competitive against known baselines.

关键词：

Beyond bandit feedback in online multiclass classification

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arXiv 2021年

作者： van der Hoeven, Dirk Fusco, Federico Cesa-Bianchi, Nicolò Dept. of Computer Science Università degli Studi di Milano Italy Dept. of Computer Control and Management Engineering Sapienza Università di Roma Italy DSRC Dept. of Computer Science Università degli Studi di Milano Italy

We study the problem of online multiclass classification in a setting where the learner’s feedback is determined by an arbitrary directed graph. While including bandit feedback as a special case, feedback graphs allow a much richer set of applications, including filtering and label efficient classification. We introduce Gappletron, the first online multiclass algorithm that works with arbitrary feedback graphs. For this new algorithm, we prove surrogate regret bounds that hold, both in expectation and with high probability, for a large class of surrogate losses. Our bounds are of order B√ρKT, where B is the diameter of the prediction space, K is the number of classes, T is the time horizon, and ρ is the domination number (a graph-theoretic parameter affecting the amount of exploration). In the full information case, we show that Gappletron achieves a constant surrogate regret of order B2K. We also prove a general lower bound of order max {B2K, √T} showing that our upper bounds are not significantly improvable. Experiments on synthetic data show that for various feedback graphs our algorithm is competitive against known baselines. Copyright © 2021, The Authors. All rights reserved.

关键词： Directed graphs

Decentralized Federated Learning for Nonintrusive Load Monitoring in Smart Energy Communities

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Decentralized Federated Learning for Nonintrusive Load Monit...

Mediterranean Conference on control and Automation (MED)

作者： Alessandro Giuseppi Sabato Manfredi Danilo Menegatti Antonio Pietrabissa Cecilia Poli Dept. of Computer Control and Management Engineering (DIAG) University of Rome "La Sapienza" Rome Italy Dept. of Electrical Engineering and Information Technology University of Naples "Federico II" Naples Italy National Centre for Innovative Technologies in Public Health (TISP) Istituto Superiore di Sanit&#x00E0 Rome Italy

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Federated Learning is a distributed learning solution for machine learning problems without the need of collecting the available data in a single centralized data centre. With the standard FL approaches, model training is performed locally and a centralized server collects and elaborates the trainable parameters of the local models: even if data are not shared, the presence of the centralized server still rises trust and security issues. In this work, we introduce the Decentralized Federated Learning (DECFEDAVG) algorithm, which aims at achieving complete decentralization by the lack of a coordination server, and compare its performance against the original federated learning algorithm Federated Averaging (FEDAVG) over the Nonintrusive Load Monitoring problem.

关键词： Training Load monitoring Distance learning Distributed databases Machine learning Collaborative work Data models

Exploiting Prediction Error Inconsistencies through LSTM-based Classifiers to Detect Deepfake Videos 20

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Exploiting Prediction Error Inconsistencies through LSTM-bas...

8th ACM Workshop on Information Hiding and Multimedia Security, IH and MMSec 2020

作者： Amerini, Irene Caldelli, Roberto Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy Parma Italy Mercatorum University Rome Italy

ISBN: (纸本)9781450370509

The ability of artificial intelligence techniques to build synthesized brand new videos or to alter the facial expression of already existing ones has been efficiently demonstrated in the literature. The identification of such new threat generally known as Deepfake, but consisting of different techniques, is fundamental in multimedia forensics. In fact this kind of manipulated information could undermine and easily distort the public opinion on a certain person or about a specific event. Thus, in this paper, a new technique able to distinguish synthetic generated portrait videos from natural ones is introduced by exploiting inconsistencies due to the prediction error in the re-encoding phase. In particular, features based on inter-frame prediction error have been investigated jointly with a Long Short-Term Memory (LSTM) model network able to learn the temporal correlation among consecutive frames. Preliminary results have demonstrated that such sequence-based approach, used to distinguish between original and manipulated videos, highlights promising performances. © 2020 ACM.

关键词： Long short-term memory

Enabling Smart Agriculture in Nigeria: Application of Digital-Twin Technology 1

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Enabling Smart Agriculture in Nigeria: Application of Digita...

1st International Conference on Multidisciplinary engineering and Applied Science, ICMEAS 2021

作者： Elijah, Olakunle Rahim, Sharul Kamal A. Emmanuel, Abioye Abiodun Salihu, Yahaya Otuoze Usman, Zubair Gatugel Jimoh, Adinoyi Momoh Wireless Communication Center School of Electrical Engineering University of Technology Malaysia Johor Bahru Malaysia School of Electrical Engineering University of Technology Malaysia Control and Mechatronics Engr. Dept. Johor Bahru Malaysia Kaduna Polytechnic Computer Engineering Department Kaduna Nigeria Saudi Telecommunication Company Riyadh Saudi Arabia

ISBN: (纸本)9781665434935

The digital twin (DT) is an emerging technology and one of the industry 4.0 technologies. The research in the application of DT in agriculture is currently attracting research interest. In this paper, we provide an overview of DT and discuss possible areas in which it can enable smart agriculture in Nigeria. The benefits and challenges of deploying the DT are also presented. Finally, methods that can be adopted in the application of DT towards solving the problems facing Nigeria's agriculture sector are proposed. © 2021 IEEE.

关键词： Agriculture