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24th ACM International Conference on Hybrid systems Computation and control, HSCC 2021, held as part of the 14th Cyber Physical systems and Internet-of-Things Week, CPS-IoT Week 2021

作者： Lavaei, Abolfazl Nejati, Ameneh Soudjani, Sadegh Zamani, Majid Institute for Dynamic Systems and Control Eth Zurich Switzerland Department of Electrical Engineering Technical University of Munich Germany School of Computing Newcastle University United Kingdom Department of Computer Science University of Colorado Boulder United States

ISBN: (纸本)9781450383394

In this work, we propose a data-driven technique for a formal estimation of infinitesimal generators of continuous-time stochastic systems with unknown dynamics. In the proposed framework, we first approximate the infinitesimal generator of the solution process via a set of data collected from solution processes of unknown systems. We then put some proper assumptions on dynamics of systems and quantify the closeness between the infinitesimal generator and its approximation while providing a priori guaranteed confidence bound. We show that both the time discretization and the number of data play significant roles in providing a reasonable closeness precision. Motivations and Related Works. Infinitesimal generator of a stochastic process (i.e., a partial differential operator that encodes a great deal of information about the process) plays a principal role in many analysis frameworks including: (i) stability analysis of continuous-time stochastic systems via Lyapunov functions [1], (ii) establishing similarity relations between two continuous-time stochastic systems via stochastic simulation functions [2], (iii) constructing finite abstractions of continuous-time stochastic control systems [3], and (iv) safety analysis of continuous-time stochastic systems via barrier certificates [4], to name a few. In the relevant literature, only [5] studies the estimation of infinitesimal generators, whose approach is based on the assumption of knowing the precise model and discretizing both time and state. To the best of our knowledge, our work is the first to propose a method for estimating the infinitesimal generator of stochastic systems with unknown dynamics while providing formal guarantees. The complete version of this work can be found in [6]. © 2021 Owner/Author.

关键词： Stochastic systems

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On the Trade-Offs Between Accuracy, Privacy, and Resilience in Average Consensus Algorithms

On the Trade-Offs Between Accuracy, Privacy, and Resilience ...

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IEEE Conference on Decision and control

作者： Guilherme Ramos André M. H. Teixeira Sérgio Pequito Dept. of Computer Science and Engineering Instituto Superior Técnico University of Lisbon Portugal and Instituto de Telecomunicações Lisbon Portugal Department of Information Technology Division of Systems and Control Uppsala University Sweden

There can be none. In this paper, we address the problem of a set of discrete-time networked agents reaching average consensus privately and resiliently in the presence of a subset of attacked agents. Existing approaches to the problem rely on trade-offs between accuracy, privacy, and resilience, sacrificing one for the others. We show that a separation-like principle for privacy-preserving and resilient discrete-time average consensus is possible. Specifically, we propose a scheme that combines strategies from resilient average consensus and private average consensus, which yields both desired properties. The proposed scheme has polynomial time-complexity on the number of agents and the maximum number of attacked agents. In other words, each agent that is not under attack is able to detect and discard the values of the attacked agents, reaching the average consensus of non-attacked agents while keeping each agent's initial state private. Finally, we demonstrate the effectiveness of the proposed method with numerical results.

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Learning-based Rigid Tube Model Predictive control

arXiv

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arXiv 2023年

作者： Gao, Yulong Yan, Shuhao Zhou, Jian Cannon, Mark Abate, Alessandro Johansson, Karl H. Department of Electrical and Electronic Engineering Imperial College London United Kingdom Department of Mathematics University of Stuttgart Germany Department of Electrical Engineering Linköping University Sweden Department of Engineering Science University of Oxford United Kingdom Department of Computer Science University of Oxford United Kingdom Division of Decision and Control Systems KTH Royal Institute of Technology Sweden

This paper is concerned with model predictive control (MPC) of discrete-time linear systems subject to bounded additive disturbance and mixed constraints on the state and input, whereas the true disturbance set is unknown. Unlike most existing work on robust MPC, we propose an algorithm incorporating online learning that builds on prior knowledge of the disturbance, i.e., a known but conservative disturbance set. We approximate the true disturbance set at each time step with a parameterised set, which is referred to as a quantified disturbance set, using disturbance realisations. A key novelty is that the parameterisation of these quantified disturbance sets enjoys desirable properties such that the quantified disturbance set and its corresponding rigid tube bounding disturbance propagation can be efficiently updated online. We provide statistical gaps between the true and quantified disturbance sets, based on which, probabilistic recursive feasibility of MPC optimisation problems is discussed. Numerical simulations are provided to demonstrate the effectiveness of our proposed algorithm and compare with conventional robust MPC algorithms. © 2023, CC BY.

关键词： Model predictive control

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Adaptive Estimation for Environmental Monitoring Using an Autonomous Underwater Vehicle

Adaptive Estimation for Environmental Monitoring Using an Au...

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IEEE Conference on Decision and control

作者： Ziwen Yang Joana Fonseca Shanying Zhu Cailian Chen Xinping Guan Karl H. Johansson Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures

This paper considers the problem of monitoring and adaptively estimating an environmental field, such as temperature or salinity, using an autonomous underwater vehicle (AUV). The AUV moves in the field and persistently measures environmental scalars and its position in its local coordinate frame. The environmental scalars are approximately linearly distributed over the region of interest, and an adaptive estimator is designed to estimate the gradient. By orthogonal decomposition of the velocity of the AUV, a linear time-varying system is equivalently constructed, and the sufficient conditions on the motion of the AUV are established, under which the global exponential stability of the estimation error system is rigorously proved. Furthermore, an estimate of the exponential convergence rate is given, and a reference trajectory that maximizes the estimate of the convergence rate is obtained for the AUV to track. Numerical examples verify the stability and efficiency of the system.

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Distributed formation control of networked mechanical systems

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IFAC-PapersOnLine 2022年第13期55卷 294-299页

作者： N. Javanmardi P. Borja M.J. Yazdanpanah J.M.A. Scherpen Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran Department of Cognitive Robotics Delft University of Technology Delft The Netherlands Jan C. Wilems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Groningen The Netherlands

This paper investigates a distributed formation tracking control law for large-scale networks of mechanical systems. In particular, the formation network is represented by a directed communication graph with leaders and followers, where each agent is described as a port-Hamiltonian system with a constant mass matrix. Moreover, we adopt a distributed parameter approach to prove the scalable asymptotic stability of the network formation, i.e., the scalability with respect to the network size and the specific formation preservation. A simulation case illustrates the effectiveness of the proposed control approach.

关键词： Autonomous systems Cooperative control Networked control systems Large-scale systems Port-Hamiltonian systems Scalability

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IF: Iterative Fractional Optimization 29

IF: Iterative Fractional Optimization

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29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2021

作者： Chatterjee, Sarthak Das, Subhro Pequito, Sérgio Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY United States MIT-IBM Watson AI Lab IBM Research CambridgeMA United States Delft Center for Systems and Control Delft University of Technology Netherlands

ISBN: (纸本)9782875870827

Most optimization problems lack closed-form solutions of the argument that minimizes a given function, and even if these were available it might be prohibitive to compute it. As such, we rely on iterative numerical algorithms to find an approximate solution. In this paper, we propose to leverage fractional calculus in the context of time series analysis methods to devise a new iterative algorithm. Specifically, we propose to leverage autoregressive fractional-order integrative moving average time series, whose coefficients encode a proxy for local spatial information. We provide evidence that our algorithm is efficient and particularly suitable for cases where the Hessian is ill-conditioned. © 2021 ESANN Intelligence and Machine Learning. All rights reserved.

关键词： Time series analysis

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Assessment and recognition in technical massive open on-line courses with and without on-line laboratories 14

Assessment and recognition in technical massive open on-line...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Martin, Sergio Castro, Manuel Sancristobal, Elio Diaz, Gabriel Garcia-Loro, Felix Perez, Clara Quintana, Blanca Gil, Rosario Plaza, Pedro Lopez, Africa Carro, German Robles, Antonio Tobarra, Llanos Rodriguez-Artacho, Miguel Ruiperez-Valiente, Jose A. Universidad Nacional de Educación A Distancia Electrical and Computer Engineering Department Madrid Spain Universidad Nacional de Educación A Distancia Dep. Sistemas de Comunicación y Control Madrid Spain Universidad Nacional de Educación A Distancia Dep. Lenguajes y Sistemas Informáticos Madrid Spain Masschusetts Institute of Technology Teaching Systems Lab United States

ISBN: (纸本)9798350399431

This paper describes the organization and results of several MOOCs delivered about technical topics (i.e., digital electronics, VHDL design on FPGAs, open education and OERs repositories and the use of STEAM technologies to encourage diversity and inclusion), where online laboratories have been used in some of them. An analysis of the enrollment, students participating on tasks or quizzes, drop-out rate and certifications requests have been done. © 2023 IEEE.

关键词： Drops

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computer Vision-control-Based CNN-PID for Mobile Robot

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computers, Materials & Continua 2021年第7期68卷 1065-1079页

作者： Rihem Farkh Mohammad Tabrez Quasim Khaled Al jaloud Saad Alhuwaimel Shams Tabrez Siddiqui College of Engineering Muzahimiyah BranchKing Saud UniversityP.O.Box 2454Riyadh11451Saudi Arabia College of Computing and Information Technology University of BishaBisha67714Saudi Arabia King Abdulaziz City for Science and Technology Saudi Arabia Department of Computer Science College of Computer Science and Information TechnologyJazan UniversityJazanSaudi Arabia Department of Electrical Engineering Laboratory for AnalysisConception and Control of SystemsLR-11-ES20National Engineering School of TunisTunis El Manar UniversityTunisTunisia

With the development of artificial intelligence technology,various sectors of industry have *** them,the autonomous vehicle industry has developed considerably,and research on self-driving control systems using artificial intelligence has been extensively *** on the use of image-based deep learning to monitor autonomous driving systems have recently been *** this paper,we propose an advanced control for a serving robot.A serving robot acts as an autonomous line-follower vehicle that can detect and follow the line drawn on the floor and move in specified *** robot should be able to follow the trajectory with speed *** controllers were used simultaneously to achieve *** neural networks(CNNs)are used for target tracking and trajectory prediction,and a proportional-integral-derivative controller is designed for automatic steering and speed *** study makes use of a Raspberry PI,which is responsible for controlling the robot car and performing inference using CNN,based on its current image input.

关键词： Autonomous car pid control deep learning convolutional neural network differential drive system raspberry pi

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A Backstepping Integral Sliding Mode controller Design for Partial Feedback Linearized Models of Synchronous Generators in Simple Power systems to Improve the Transient Stability 2

A Backstepping Integral Sliding Mode Controller Design for P...

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2nd IEEE International Conference on Smart Technologies for Power, Energy and control, STPEC 2021

作者： Pramanik, M.A.H. Roy, T.K. Anower, M.S. Ghosh, S.K. Mahmud, M.A. Rajshahi University of Engineering Technology Department of Electrical Computer Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering Technology Department of Electronics Telecommunication Engineering Rajshahi Bangladesh Rajshahi University of Engineering Technology Department of Electrical Electronic Engineering Rajshahi6204 Bangladesh Northumbria University Electrical Power Control Systems Research Group Faculty of Engineering and Environment Newcastle United Kingdom

ISBN: (纸本)9781665443197

This paper proposes a backstepping integral sliding mode controller (BISMC) based on the partial feedback linearization (PFBL) of the detailed model of synchronous generators (SGs) in simple power networks. The proposed scheme is designed to obtain the excitation control input using the detailed model of SGs in a simple power system where the PFBL is used to simplify the model. The BISMC is employed on this simplified model to ensure the transient stability during large disturbances. The inherent property of the BISMC tackles the chattering and overcomes the parameter sensitivity problem of the PFBL scheme. Simulation studies are conducted under different operating situations to justify the effectiveness of the proposed PFBL-BISMC over a partial feedback linearizing controller (PFBLC). © 2021 IEEE.

关键词： Synchronous generators

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Optimizing accuracy and diversity: a multi-task approach to forecast combinations

arXiv

引用

arXiv 2023年

作者： Felici, Giovanni Sudoso, Antonio M. Institute for Systems Analysis and Computer Science Antonio Ruberti National Research Council Via dei Taurini 19 Rome00185 Italy Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Via Ariosto 25 Rome00185 Italy

Forecast combination involves using multiple forecasts to create a single, more accurate prediction. Recently, feature-based forecasting has been employed to either select the most appropriate forecasting models or to optimize the weights of their combination. In this paper, we present a multi-task optimization paradigm that focuses on solving both problems simultaneously and enriches current operational research approaches to forecasting. In essence, it incorporates an additional learning and optimization task into the standard feature-based forecasting approach, focusing on the identification of an optimal set of forecasting methods. During the training phase, an optimization model with linear constraints and quadratic objective function is employed to identify accurate and diverse methods for each time series. Moreover, within the training phase, a neural network is used to learn the behavior of that optimization model. Once training is completed the candidate set of methods is identified using the network. The proposed approach elicits the essential role of diversity in feature-based forecasting and highlights the interplay between model combination and model selection when optimizing forecasting ensembles. Experimental results on a large set of series from the M4 competition dataset show that our proposal enhances point forecast accuracy compared to state-of-the-art methods. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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