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On the Relation between Discrete and Continuous-time Refined Instrumental Variable Methods

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arXiv 2023年

作者： González, Rodrigo A. Rojas, Cristian R. Pan, Siqi Welsh, James S. The Control Systems Technology Group Eindhoven University of Technology Netherlands The Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden The School of Engineering University of Newcastle Callaghan Australia

The Refined Instrumental Variable method for discrete-time systems (RIV) and its variant for continuous-time systems (RIVC) are popular methods for the identification of linear systems in open-loop. The continuous-time equivalent of the transfer function estimate given by the RIV method is commonly used as an initialization point for the RIVC estimator. In this paper, we prove that these estimators share the same converging points for finite sample size when the continuous-time model has relative degree zero or one. This relation does not hold for higher relative degrees. Then, we propose a modification of the RIV method whose continuous-time equivalent is equal to the RIVC estimator for any non-negative relative degree. The implications of the theoretical results are illustrated via a simulation example. © 2023, CC BY-NC-ND.

关键词： Continuous time systems

Geometric Parameterized Macromodel of Uniaxial Capacitance Accelerometer Based on Modal Superposition 16

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Geometric Parameterized Macromodel of Uniaxial Capacitance A...

16th IEEE International Scientific and Technical Conference Actual Problems of Electronic Instrument engineering, APEIE 2023

作者： Gridchin, Aleksandr V. Koleda, Aleksei N. Barbin, Evgenii S. Nesterenko, Tamara G. Kolchuzhin, Vladimir A. Department of Semiconductor Devices and Microelectronics Novosibirsk State Technical University Novosibirsk Russia Department of Electronic Engineering National Research Tomsk Polytechnic University Tomsk Russia Laboratory of Intelligent Computer Systems Tomsk State University of Control Systems and Radioelectronics Tomsk Russia Advanced Cellular Group Qorvo Munich GmbH Munich Germany

ISBN: (纸本)9798350330885

The paper presents a piecewise geometric parameterization method and a parametric analysis of uniaxial capacitance accelerometer using a reduced model formulated on the modal superposition method. The extraction of parameters for the macromodel such as modal frequencies, modal masses, modal stiffnesses, strain energy, and capacitances are described. The reduced model consists of the two modes: torsion mode, and transversal mode, three electrodes in the electrostatic domain, and eight master nodes in the geometrical domain located at the edges of the plate. The generation of the discrete macromodels is executed using commercial software. The thickness of the plate has been selected as a design parameter. Linear piecewise interpolation is used for macromodel parameterization which have been done inside VHDL-AMS code. The accelerometer's dynamic response simulated using of parametrized macromodel considering the mechanical contact phenomena and the nonlinear electrostatic interaction are presented and discussed. The developed approach can be applied for fast system level simulations in view of the variation of the geometric parameters. © 2023 IEEE.

关键词： Capacitance

Learning Flow Functions from Data with Applications to Nonlinear Oscillators

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arXiv 2023年

作者： Aguiar, Miguel Das, Amritam Johansson, Karl H. Division of Decision and Control Systems and Digital Futures KTH Royal Institute of Technology Stockholm Sweden Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands

We describe a recurrent neural network (RNN) based architecture to learn the flow function of a causal, time-invariant and continuous-time control system from trajectory data. By restricting the class of control inputs to piecewise constant functions, we show that learning the flow function is equivalent to learning the input-to-state map of a discrete-time dynamical system. This motivates the use of an RNN together with encoder and decoder networks which map the state of the system to the hidden state of the RNN and back. We show that the proposed architecture is able to approximate the flow function by exploiting the system’s causality and time-invariance. The output of the learned flow function model can be queried at any time instant. We experimentally validate the proposed method using models of the Van der Pol and FitzHugh-Nagumo oscillators. In both cases, the results demonstrate that the architecture is able to closely reproduce the trajectories of these two systems. For the Van der Pol oscillator, we further show that the trained model generalises to the system’s response with a prolonged prediction time horizon as well as control inputs outside the training distribution. For the FitzHugh-Nagumo oscillator, we show that the model accurately captures the input-dependent phenomena of excitability. © 2023, CC BY-NC-ND.

关键词： Recurrent neural networks

Ego-Motion Estimation and Dynamic Motion Separation from 3D Point Clouds for Accumulating Data and Improving 3D Object Detection

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arXiv 2023年

作者： Palmer, Patrick Krüger, Martin Altendorfer, Richard Bertram, Torsten TU Dortmund Institute of Control Theory and Systems Engineering Dortmund44227 Germany ZF Group Koblenz56070 Germany

New 3+1D high-resolution radar sensors are gaining importance for 3D object detection in the automotive domain due to their relative affordability and improved detection compared to classic low-resolution radar sensors. One limitation of high-resolution radar sensors, compared to lidar sensors, is the sparsity of the generated point cloud. This sparsity could be partially overcome by accumulating radar point clouds of subsequent time steps. This contribution analyzes limitations of accumulating radar point clouds on the View-of-Delft dataset [1]. By employing different ego-motion estimation approaches, the dataset’s inherent constraints, and possible solutions are analyzed. Additionally, a learning-based instance motion estimation approach is deployed to investigate the influence of dynamic motion on the accumulated point cloud for object detection. Experiments document an improved object detection performance by applying an ego-motion estimation and dynamic motion correction approach. © 2023, CC BY.

关键词： Object detection

Novel Disturbance Observer Relevant Parametric System Identification Based on Robust Stability Criterion

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Novel Disturbance Observer Relevant Parametric System Identi...

control Technology and Applications (CCTA),

作者： Hanul Jung Paul Tacx Tom Oomen Sehoon Oh Department of Robotics Engineering DGIST Daegu Korea Department of Mechanical Engineering Control Systems Technology Group Eindhoven University of Technology Eindhoven The Netherlands

In this paper, novel parametric identification is addressed by the disturbance observer (DOB) relevant algorithm. To fully utilize the disturbance observer, a proper nominal model, which is a parametric model, is essential. The control performance of the DOB can be checked based on the robust stability criterion, which is related to the modeling error and the desired bandwidth of the DOB. Consequently, it is beneficial to evaluate the parametric model in the system identification procedure. Therefore, this paper proposes a novel disturbance observer-relevant system identification algorithm based on the robust stability criterion of the disturbance observer. The effectiveness of the proposed method is validated by showing the numerical simulations under specific conditions.

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Distributed Time-Varying Gaussian Regression via Kalman Filtering

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arXiv 2025年

作者： Taddei, Nicola Maggioni, Riccardo Eising, Jaap De Pasquale, Giulia Dörfler, Florian Automatic Control Laboratory Department of Electrical Engineering and Information Technology ETH Zurich Physikstrasse 3 Zurich8092 Switzerland Control Systems Group Department of Electrical Engineering TU Eindhoven Flux Groene Loper 19 Eindhoven5612 AP Netherlands

We consider the problem of learning time-varying functions in a distributed fashion, where agents collect local information to collaboratively achieve a shared estimate. This task is particularly relevant in control applications, whenever real-time and robust estimation of dynamic cost/reward functions in safety critical settings has to be performed. In this paper, we adopt a finite-dimensional approximation of a Gaussian Process, corresponding to a Bayesian linear regression in an appropriate feature space, and propose a new algorithm, DistKP, to track the time-varying coefficients via a distributed Kalman filter. The proposed method works for arbitrary kernels and under weaker assumptions on the time-evolution of the function to learn compared to the literature. We validate our results using a simulation example in which a fleet of Unmanned Aerial Vehicles (UAVs) learns a dynamically changing wind field. Copyright © 2025, The Authors. All rights reserved.

关键词： Gaussian distribution

Safe Zeroth-Order Optimization Using Linear Programs

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arXiv 2023年

作者： Guo, Baiwei Wang, Yang Jiang, Yuning Kamgarpour, Maryam Ferrari-Trecate, Giancarlo The Decode Group Institute of Mechanical Engineering Epfl Switzerland Delft Center for Systems and Control Delft University of Technology Netherlands The Predict Group Institute of Mechanical Engineering Epfl Switzerland The Sycamore Group Institute of Mechanical Engineering Epfl Switzerland

To solve unmodeled optimization problems with hard constraints, this paper proposes a novel zeroth-order approach called Safe Zeroth-order Optimization using Linear Programs (SZO-LP). The SZO-LP method solves a linear program in each iteration to find a descent direction, followed by a step length determination. We prove that, under mild conditions, the iterates of SZO-LP have an accumulation point that is also the primal of a KKT pair. We then apply SZO-LP to solve an Optimal Power Flow (OPF) problem on the IEEE 30-bus system. The results demonstrate that SZO-LP requires less computation time and samples compared to state-of-the-art approaches. © 2023, CC0.

关键词： Iterative methods

Hybrid Autonomous Underwater Glider (HAUG) Obstacle Detection and Avoidance

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Hybrid Autonomous Underwater Glider (HAUG) Obstacle Detectio...

11th IEEE International Conference on System engineering and Technology, ICSET 2021

作者： Putra, A. A. Gde Jenana Trilaksono, Bambang Riyanto Hidayat, Egi Muhammad Idris School of Electrical Engineering and Informatics Institut Teknologi Bandung Control and Computer Systems Research Group Bandung Indonesia

ISBN: (纸本)9781665437660

Hybrid Autonomous Underwater Glider (HAUG) is a vehicle used for underwater missions such as monitoring and finding new underwater resources. HAUG has good endurance and maneuverability compared to conventional Autonomous Underwater Vehicle (AUV) and Autonomous Underwater Glider (AUG). It is because HAUG has two operational modes. They are AUV and AUG's operational mode. When HAUG is in some missions, it may be faced with an obstacle that can be a threat to the HUG's safety. Therefore, HAUG should have the ability to detect and avoid obstacles. Gemini 720 im Imaging Forward Looking Sonar (FLS) is used for obstacle detection in this work. The main issue of underwater obstacle detection is noisy data received by sonar. Therefore, by designing an obstacle detection, it will overcome those issues. Frost filter and local histogram entropy are used in the sonar data processing. The processed sonar data are provided in the local sonar frame then will be used by obstacle avoidance systems. BK-product fuzzy and reactive algorithms are used for obstacle avoidance. In this paper, we added some procedures to those obstacle avoidance algorithms to handle the huge or non-complex u-shaped obstacle. Both of the obstacle detection and avoidance simulations are in ROS (Robot Operating System). The obstacle detection simulation shows that the different sizes of obstacles can be detected with average errors of approximately 0.335 meters. The obstacle avoidance simulations are in AUV's mode with no ocean current applied. The obstacle avoidance simulated in this work is with two cases. Using simulated lidar as a sensor's output and using sonar's plugin provided by Gazebo. The obstacle avoidance using simulated lidar shows that the error's value is approximately 10.12 meters, 103.62 meters, and 354.4 meters respectively. The obstacle avoidance simulation with sonar's plugin shows that the error's value is 6.55 meters. © 2021 IEEE.

关键词： Meters Laser radar Simulation Operating systems Sonar Robot sensing systems systems engineering and theory

Fractional order MRAC control design for a lightning system based on a fractional order second degree model

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IFAC-PapersOnLine 2024年第12期58卷 95-100页

作者： Samir Ladaci Seif Eddine Khelas Amani R Ynineb Dana Copot Clara-Mihaela Ionescu Ecole Nationale Polytechnique Depart. Automatic Control Eng. El Harrach 16200 Algiers Algeria National Polytechnic School of Constantine E.E.A. Department 25100 Constantine Algeria Research Group on Dynamical Systems and Control Department of Electromechanical Systems and Metal Engineering Ghent University B-9052 Ghent Belgium

This paper presents a new adaptive fractional-order MRAC controller design for the class of non-integer second-order systems. The update of the control law gains follows a derivative of fractional order equal to the order of the controlled system model. The stability analysis of the control system has been performed based on Lyapunov theorem. We apply this control law to an experimental multivariable lightning system. The results obtained show the efficiency and performance improvement of the proposed control solution compared to the classical MRAC.

关键词： Fractional order system adaptive control MRAC FOMRAC Lightning system