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arXiv 2021年

作者： Zardini, Gioele Lanzetti, Nicolas Pavone, Marco Frazzoli, Emilio Institute for Dynamic Systems and Control Department of Mechanical and Process Engineering ETH Zürich Zürich Switzerland Automatic Control Laboratory Department of Information Technology and ElectricalEngineering ETH Zürich Zürich Switzerland Autonomous Systems Lab Department of Aeronautics and Astronautics Stanford University StanfordCA United States

Challenged by urbanization and increasing travel needs, existing transportation systems need new mobility paradigms. In this article, we present the emerging concept of autonomous mobility-on-demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to autonomous mobility-on-demand systems. Specifically, we first identify problem settings for their analysis and control, from both operational and planning perspectives. We then review modeling aspects, including transportation networks, transportation demand, congestion, operational constraints, and interactions with existing infrastructure. Thereafter, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research. © 2021, CC BY-NC-ND.

关键词： Autonomous vehicles

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Geometrical Features based Visual Relocalization for Indoor Service Robot

Geometrical Features based Visual Relocalization for Indoor ...

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作者： Jikai Wang Deyun Dai Peng Wang Hao Zhao Meng Xu Zonghai Chen Department of Automation University of Science and Technology of China Department of Automatic Control and Systems Engineering The University of Sheffield

In traditional SLAM methods, the environment map is the simply assembled points or lines, which makes it difficult to directly perform relocalization using such map. This paper presents a new implementation method for indoor environment representation and visual relocalization using RGB-D *** method is developed for indoor service robots to perform relocalization using the observed point and line features. In this paper, the sparse feature map, line segment map, and dense point cloud map of an environment are learned by a random forest to regress the correspondences between visual features and 3 D world locations, geometrical features and 3 D world locations. Using the learned forest, landmark positions are efficiently predicted and the camera poses are then estimated in a probabilistic framework. The performance of the proposed method is demonstrated under various challenging environments using public benchmark dataset and our own dataset collected in an office environment. These conditions contain ambiguous areas,long corridor, moving people, viewpoint changes, or illumination variation. The proposed method is thoroughly evaluated against several strong state-of-the-art baselines. Experimental results prove the efficacy of our method.

关键词： Camera relocalization regression forest line segment RGB-D sensor

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Performance analysis of genetic algorithms for route computation applied to emergency vehicles in uncertain traffic

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and Computer Science 2020年第1期82卷 167-178页

作者： Constantinescu, Vlad Pătrașcu, Monica Department of Automatic Control and Systems Engineering University POLITEHNICA of Bucharest Romania Institute of Space Science Măgurele Romania

Efficient transportation is an important requirement in today’s world. As modern cities grow in size and complexity, the travel distances for people and goods increase while available time decreases. Routes must be computed dynamically and close to real-time, while taking into consideration various factors such as road congestion and maximum speed allowed. In this paper, we explore the use of evolutionary algorithms to solve this multiple criteria optimization problem. The performance analysis included in this study identifies the best configuration for the genetic routing algorithm which provides the best consistency of correct routes. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： Motion planning

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Power Quality Management of Interconnected Microgrids using Model Predictive control ⁎

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IFAC-PapersOnLine 2020年第2期53卷 12918-12923页

作者： F. Garcia-Torres S. Vazquez C. Bordons I. Moreno-Garcia A. Gil P. Roncero-Sanchez Centro Nacional del Hidrogeno Puertollano (Ciudad Real) Spain Electronic Engineering Department Universidad de Sevilla Sevilla Spain Systems Engineering and Automatic Control Department Universidad de Sevilla Sevilla Spain Department of Electronic and Computer Engineering Universidad de Cordoba Cordoba Spain Systems Engineering and Automatic Control Department University of Castilla-La Mancha Ciudad Real Spain

In this paper, the power quality of interconnected microgrids is managed using a Model Predictive control (MPC) methodology which manipulates the power converters of the microgrids in order to achieve the requirements. The control algorithm is developed for the microgrids working modes: grid-connected, islanded and interconnected. The results and simulations are also applied to the transition between the different working modes. In order to show the potential of the control algorithm a comparison study is carried out with classical Proportional-Integral Pulse Width Modulation (PI-PWM) based controllers. The proposed control algorithm not only improves the transient response in comparison with classical methods but also shows an optimal behavior in all the working modes, minimizing the harmonics content in current and voltage even with the presence of non-balanced and non-harmonic-free three-phase voltage and current systems.

关键词： Interconnected systems Harmonics Predictive control Power System control

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Covariance estimation from compressive data partitions using a projected gradient-based algorithm

arXiv

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arXiv 2021年

作者： Monsalve, Jonathan Ramirez, Juan Esnaola, Iñaki Arguello, Henry Department of Electrical Engineering Universidad Industrial de Santander Bucaramanga680002 Colombia Department of Computer Science Universidad Rey Juan Carlos Madrid28933 Spain Department of Automatic Control and Systems Engineering The University of Sheffield Western Bank Sheffield United Kingdom Department Systems Engineering and Informatics Universidad Industrial de Santander Bucaramanga680002 Colombia

Covariance matrix estimation techniques require high acquisition costs that challenge the sampling systems’ storing and transmission capabilities. For this reason, various acquisition approaches have been developed to simultaneously sense and compress the relevant information of the signal using random projections. However, estimating the covariance matrix from the random projections is an ill-posed problem that requires further information about the data, such as sparsity, low rank, or stationary behavior. Furthermore, this approach fails using high compression ratios. Therefore, this paper proposes an algorithm based on the projected gradient method to recover a low-rank or Toeplitz approximation of the covariance matrix. The proposed algorithm divides the data into subsets projected onto different subspaces, assuming that each subset contains an approximation of the signal statistics, improving the inverse problem’s condition. The error induced by this assumption is analytically derived along with the convergence guarantees of the proposed method. Extensive simulations show that the proposed algorithm can effectively recover the covariance matrix of hyperspectral images with high compression ratios (8 − 15% approx) in noisy scenarios. Additionally, simulations and theoretical results show that filtering the gradient reduces the estimator’s error recovering up to twice the number of eigenvectors. © 2021, CC BY-NC-ND.

关键词： Covariance matrix

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System identification beyond the Nyquist frequency: A kernel-regularized approach

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control engineering Practice 2025年 164卷

作者： Max van Haren Roy S. Smith Tom Oomen Department of Mechanical Engineering Control Systems Technology Section Eindhoven University of Technology Groene Loper 5 Eindhoven 5612 AE The Netherlands Automatic Control Laboratory ETH Zürich Zürich 8092 Switzerland Delft Center for Systems and Control Delft University of Technology Mekelweg 2 Delft 2628 CN The Netherlands

Models that contain intersample behavior are important for control design of systems with slow-rate outputs. The aim of this paper is to develop a system identification technique for fast-rate models of systems where only slow-rate output measurements are available, e.g., vision-in-the-loop systems. In this paper, the intersample response is estimated by identifying fast-rate models through least-squares criteria, and the limitations of these models are determined. In addition, a method is developed that surpasses these limitations and is capable of estimating unique fast-rate models of arbitrary order by regularizing the least-squares estimate. The developed method utilizes fast-rate inputs and slow-rate output measurements and identifies fast-rate models accurately in a single identification experiment. Finally, both simulation and experimental validation on a prototype wafer stage demonstrate the effectiveness of the framework.

关键词： System identification Kernel-regularized estimation Multirate Sampled-data systems Frequency response functions

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Retraction Note: EoT-driven hybrid ambient assisted living framework with naïve Bayes–firefly algorithm

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Neural Computing and Applications 2024年第24期36卷 15215-15215页

作者： Hassan, Mohammed K. Desouky, Ali I. El Badawy, Mahmoud M. Sarhan, Amany M. Elhoseny, Mohamed Gunasekaran, M. Department of Computer Engineering and Systems Faculty of Engineering Mansoura University Mansoura City Egypt Department of Computer Engineering and Automatic Control Faculty of Engineering Tanta University Tanta Egypt Faculty of Computers and Information Mansoura University Mansoura Egypt University of California Davis USA

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Generalization Analysis of Machine Learning Algorithms via the Worst-Case Data-Generating Probability Measure

arXiv

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arXiv 2023年

作者： Zou, Xinying Perlaza, Samir M. Esnaola, Iñaki Altman, Eitan INRIA Centre Inria d'Université Côte d'Azur Sophia Antipolis06902 France Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States GAATI Laboratory Université de la Polynésie Française Faaa98702 French Polynesia Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom Laboratoire d'Informatique d'Avignon Université d'Avignon France

In this paper, the worst-case probability measure over the data is introduced as a tool for characterizing the generalization capabilities of machine learning algorithms. More specifically, the worst-case probability measure is a Gibbs probability measure and the unique solution to the maximization of the expected loss under a relative entropy constraint with respect to a reference probability measure. Fundamental generalization metrics, such as the sensitivity of the expected loss, the sensitivity of the empirical risk, and the generalization gap are shown to have closed-form expressions involving the worst-case data-generating probability measure. Existing results for the Gibbs algorithm, such as characterizing the generalization gap as a sum of mutual information and lautum information, up to a constant factor, are recovered. A novel parallel is established between the worst-case data-generating probability measure and the Gibbs algorithm. Specifically, the Gibbs probability measure is identified as a fundamental commonality of the model space and the data space for machine learning algorithms. © 2023, CC BY.

关键词： Learning algorithms

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Reducing the computational effort of MPC with closed-loop optimal sequences of affine laws

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IFAC-PapersOnLine 2020年第2期53卷 11344-11349页

作者： Martin Mönnigmann Gabriele Pannocchia Automatic Control and Systems Theory Department of Mechanical Engineering Ruhr-Universität Bochum Germany Department of Civil and Industrial Engineering University of Pisa 56122 Pisa Italy

We consider the classical infinite-horizon constrained linear-quadratic regulator (CLQR) problem and its receding-horizon variant used in model predictive control (MPC). If the terminal constraints are inactive for the current initial condition, the optimal input signal sequence that results for the open-loop CLQR problem is equal to the closed-loop optimal sequence that results for MPC. Consequently, the closed-loop optimal solution is available from solving only one CLQR problem instead of the usual infinite number of CLQR problems solved on the receding horizon. In the presence of disturbances or because of plant-model mismatch, the system will eventually leave the predicted optimal trajectory. Consequently, the solution of the single open-loop CLQR problem is no longer optimal, and the receding horizon problem must resume. We show, however, that the open-loop solution is also robust. Robustness essentially is given, because the solution of the CLQR problem not only provides the sequence of nominally optimal input signals, but a sequence of optimal affine laws along with their polytopes of validity. We analyze the degree of robustness by computational experiments. The results indicate the degree of robustness is practically relevant.

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A suboptimal H2 clustering-based model reduction approach for linear network systems

A suboptimal H2 clustering-based model reduction approach fo...

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European control Conference (ECC)

作者： Xiaodong Cheng Ion Necoara Control Systems Group Eindhoven University of Technology Eindhoven The Netherlands Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest Romania

ISBN: (数字)9783907144022

ISBN: (纸本)9781728188133

In this paper we propose a (suboptimal) H 2 model reduction method for a class of linear network systems that describe diffusively coupled networks. To preserve a network structure, we form a reduced-order model by using the characteristic matrix of a graph clustering so that the reduced-order model has less number of vertices. Then, we formulate the model reduction problem as a nonconvex optimization problem with binary variables, aiming for a graph clustering that minimizes the H 2 -norm of the approximation error. Based on the controllability and the observability Gramians of the error system we derive an optimization problem with mixed-binary variables and then we propose a convex relaxation of the binary variables, leading to a smooth optimization formulation. For this new optimization problem we derive an explicit expression for the gradient of the objective function and then we employ a projected gradient algorithm for solving the optimization problem with mathematical guarantees on its convergence.

关键词： Optimization Reduced order systems Laplace equations Modeling Approximation error Network topology Mathematical model

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