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arXiv 2023年

作者： van Goor, Pieter Mahony, Robert Schaller, Manuel Worthmann, Karl Systems Theory and Robotics Group School of Engineering Australian National University Canberra Australia Optimization-based Control Group Institute of Mathematics Technische Universität Ilmenau Germany

Extended Dynamic Mode Decomposition (eDMD) is a powerful tool to generate data-driven surrogate models for the prediction and control of nonlinear dynamical systems in the Koopman framework. In eDMD a compression of the lifted system dynamics on the space spanned by finitely many observables is computed, in which the original space is embedded as a low-dimensional manifold. While this manifold is invariant for the infinite-dimensional Koopman operator, this invariance is typically not preserved for its eDMD-based approximation. Hence, an additional (re-)projection step is often tacitly incorporated to improve the prediction capability. We propose a novel framework for consistent reprojectors respecting the underlying manifold structure. Further, we present a new geometric reprojector based on maximum-likelihood arguments, which significantly enhances the approximation accuracy and preserves known finite-data error bounds. © 2023, CC BY-NC-ND.

关键词： Dynamic mode decomposition

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Sequential fusion state estimation algorithm for multi-sensor nonlinear systems with cross-correlated noise

Sequential fusion state estimation algorithm for multi-senso...

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Chinese Automation Congress (CAC)

作者： Guo-Rui Cheng Kai Zhao Ting-Rui Liu Shen-Min Song Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin China Beijing Institute of Astronautical Systems Engineering Beijing China

This paper is concerned the problem of multi-sensor state estimation with cross-correlated noise, this paper adopts sequential fusion to estimate the state. The statistical characteristics of measurement noise of different sensors in the multi-sensor system is related, and also related to the system noise in one step. Firstly, based on the estimation of observation noise, a global optimal fusion filter based on sequential fusion and Cubature Kalman filter is proposed for the first time. Secondly, the algorithm proposed in this paper is simulated by numerical method. The method of numerical realization is Cubature Kalman filter based on deterministic sampling. Finally, the effectiveness of the proposed algorithm is demonstrated by a simulation example.

关键词： Automation Sensor phenomena and characterization Filtering algorithms Sensor systems Numerical models Kalman filters Noise measurement

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Trajectory Tracking control for a Class of 2×2 Hyperbolic PDE-ODE systems

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IFAC-PapersOnLine 2021年第9期54卷 416-421页

作者： A. Irscheid N. Gehring J. Rudolph Chair of Systems Theory and Control Engineering Saarland University 66123 Saarbrücken Germany Institute of Automatic Control and Control Systems Technology Johannes Kepler University 4040 Linz Austria

This paper introduces an approach to trajectory tracking control of linear 2×2 hyperbolic partial differential equations (PDEs) actuated at one boundary and coupled with ordinary differential equations (ODEs) at the other one. Well-known state feedback methods that benefit from so-called backstepping coordinates are limited to the case of boundary conditions consisting of linear time-invariant ODEs. In a first step, the new approach is introduced for this restricted class of linear dynamic boundary conditions. It is shown that tracking control is immediately achieved by a surprisingly simple, yet elegant, modification of a stabilizing state feedback. Then, a direct extension for solving trajectory tracking problems for a class of nonlinear ODEs at the unactuated boundary is discussed. Numerical studies underline the performance of the proposed method.

关键词： tracking control PDE-ODE systems boundary control predictor feedback distributed parameter systems hyperbolic systems

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Simulation of High-Definition Pixel-Headlights 15th

Simulation of High-Definition Pixel-Headlights

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15th International Symposium on Visual Computing, ISVC 2020

作者： Waldner, Mirko Bertram, Torsten TU Dortmund University Institute of Control Theory and Systems Engineering Dortmund Germany

ISBN: (纸本)9783030645557

This contribution presents a novel algorithm for real-time simulation of adaptive matrix- and pixel-headlights for motor vehicles. The simulation can generate the light distribution of a pair of pixel-headlamps with a resolution of more than one and a half million matrix-elements per light module in real-time. This performance is achieved by dividing the superposition process of the matrix-light-sources into an offline and an online part. The offline part creates a light database that is used by the online component to generate the illumination in memory access efficient way. For an ideal pixel-headlight the run-time of the approach is nearly constant by increasing the number of matrix-lights. This contribution evaluates the visual quality of the simulation. It also presents the changes in the run-time for different pixel-headlamp resolutions and solid angle discretizations. © 2020, Springer Nature Switzerland AG.

关键词： Headlights

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Accelerating soft-constrained MPC for linear systems through online constraint removal

arXiv

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arXiv 2024年

作者： Nouwens, S.A.N. Paulides, M.M. Heemels, W.P.M.H. Control Systems Technology Section Department of Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands Department of Radiotherapy Erasmus University Medical Center Cancer Institute Rotterdam Netherlands

Optimization-based controllers, such as Model Predictive control (MPC), have attracted significant research interest due to their intuitive concept, constraint handling capabilities, and natural application to multi-input multi-output systems. However, the computational complexity of solving a receding horizon problem at each time step remains a challenge for the deployment of MPC. This is particularly the case for systems constrained by many inequalities. Recently, we introduced the concept of constraint-adaptive MPC (ca-MPC) to address this challenge for linear systems with hard constraints. In ca-MPC, at each time step, a subset of the constraints is removed from the optimization problem, thereby accelerating the optimization procedure, while resulting in identical closed-loop behavior. The present paper extends this framework to soft-constrained MPC by detecting and removing constraints based on sub-optimal predicted input sequences, which is rather easy for soft-constrained MPC due to the receding horizon principle and the inclusion of slack variables. We will translate these new ideas explicitly to an offset-free output tracking problem. The effectiveness of these ideas is demonstrated on a two-dimensional thermal transport model, showing a three order of magnitude improvement in online computational time of the MPC scheme. © 2024, CC BY-NC-ND.

关键词： Predictive control systems

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Design and Stability Analysis of control System in Multiport Autonomous Reconfigurable Solar Power Plants (MARS)

Design and Stability Analysis of Control System in Multiport...

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IEEE Workshop on Computers in Power Electronics

作者： Qianxue Xia Suman Debnath Phani R V Marthi Maryam Saeedifard Energy Systems Integration & Control Section Oak Ridge National Lab Knoxville United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States

The Multiport Autonomous Reconfigurable Solar Power Plant (MARS) is an integrated photovoltaic (PV) power generation and energy storage system (ESS), that is designed to connect to both alternating current (AC) transmission grids and high-voltage direct current (HVDC) links. It is a three-phase plant consisting of numerous components with a complex hardware and hierarchical control architecture. This paper presents an approach to decouple the multivariable system of MARS using a recursive reduced-order and boundary layer system methodology. This approach enables efficient computation of the control parameters for the Ll, L2, and L3 controllers. To validate the effectiveness of the proposed control strategy, cyclic tests in accordance with pre-defined performance criteria using controller Hardware-in-the-Loop (cHIL) experiments are conducted. The results demonstrate that the MARS system operates consistently under steady-state conditions. Furthermore, the dynamic response of the MARS system to various grid events is analyzed, underlining the resilience of MARS in presence of faults or loss of generation within the connected WECC system.

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Training robust and generalizable quantum models

arXiv

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arXiv 2023年

作者： Berberich, Julian Fink, Daniel Pranjić, Daniel Tutschku, Christian Holm, Christian University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70569 Germany University of Stuttgart Institute for Computational Physics Stuttgart70569 Germany Fraunhofer IAO Fraunhofer Institute for Industrial Engineering Stuttgart70569 Germany

Adversarial robustness and generalization are both crucial properties of reliable machine learning models. In this paper, we study these properties in the context of quantum machine learning based on Lipschitz bounds. We derive parameter-dependent Lipschitz bounds for quantum models with trainable encoding, showing that the norm of the data encoding has a crucial impact on the robustness against data perturbations. Further, we derive a bound on the generalization error which explicitly involves the parameters of the data encoding. Our theoretical findings give rise to a practical strategy for training robust and generalizable quantum models by regularizing the Lipschitz bound in the cost. Further, we show that, for fixed and non-trainable encodings, as those frequently employed in quantum machine learning, the Lipschitz bound cannot be influenced by tuning the parameters. Thus, trainable encodings are crucial for systematically adapting robustness and generalization during training. The practical implications of our theoretical findings are illustrated with numerical results. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Soft sensor for monitoring dynamic changes in cell composition

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IFAC-PapersOnLine 2022年第23期55卷 98-103页

作者： Sebastián Espinel-Ríos Bruno Morabito Katja Bettenbrock Steffen Klamt Rolf Findeisen Laboratory for System Theory and Automatic Control Otto von Guericke University Magdeburg Germany Analysis and Redesign of Biological Networks Max Planck Institute for Dynamics of Complex Technical Systems Germany Bioprocess Engineering Max Planck Institute for Dynamics of Complex Technical Systems Germany Control and Cyber-Physical Systems Laboratory TU Darmstadt Germany

Precise and accurate online monitoring methods are needed to enable smart biomanufacturing and automation. Most of the sensors available focus on process parameters such as metabolite and dissolved gas concentrations, cell density or viability, among other variables like pH, temperature, etc. In this work, we develop a soft sensor algorithm to estimate the cell composition online, a very important aspect often overlooked in the bioprocess monitoring literature. Our strategy is based on full information estimation, an optimization-based estimator that takes into account the dynamics of the cell metabolism and considers all the available measurements from the beginning of the process, thus it has a memory effect. Being able to track dynamic changes in cell composition can open the door to promising applications, e.g., predictive control and automation of biosystems. As a case study, we consider the Escherichia co li’ s metabolism growing on glycerol under different levels of oxygen supply. We compare the performance of our soft sensor method against resource balance analysis, a previously proposed estimator based on steady-state assumptions. Overall, the presented full information estimator was able to track the dynamic changes in cell composition significantly more accurately. We also discuss how our estimation strategy can be transformed into a moving horizon estimation, where only the available measurements in a fixed and moving window are considered, thereby reducing possible computational burdens.

关键词： Cell composition soft sensor full information estimation resource balance analysis dynamic enzyme-cost flux balance analysis

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Performance Optimization of Angle-Based Network Localization

Performance Optimization of Angle-Based Network Localization

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IEEE Conference on Decision and control

作者： Chenyang Liang Liangming Chen Yibei Li Jie Mei Lihua Xie Center for Control Science and Technology and Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology Shenzhen China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen China

Recent advances in sensor network localization have enabled sensor nodes to localize themselves by using the measurements of inter-node angles. According to our earlier work, the proposed angle-based localization algorithms' performance, particularly, the convergence rate, is relatively poor, which, however, has not been adequately addressed in the existing literature. Motivated by this, this paper aims to improve the performance of angle-based localization algorithms, specifically, the stability margin, convergence rate and robustness against measurement noises. Firstly, we show that the stability margin, convergence rate and robustness of angle-based localization algorithms are commonly determined by one parameter, namely, the minimum eigenvalue of the network's localization matrix. Secondly, we formulate the performance optimization problem as an eigenvalue optimization problem, and show the non-differentiability of the eigenvalue optimization problem. By carefully choosing the decision variable, we utilize interior-point methods to obtain an optimal solution to the eigenvalue optimization problem. Finally, simulation examples validate the improvement of the algorithms' performance.

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Parametric S-Parameter Prediction Using Deep Learning

Parametric S-Parameter Prediction Using Deep Learning

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IEEE Topical Meeting on Electrical Performance of Electronic Packaging (EPEPS)

作者： Vinayak Bansal Lihong Feng Valentin de la Rubia Peter Benner Department of Chemical Engineering Indian Institute of Technology Delhi India Computational Methods in Systems and Control Theory Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Departamento de Mateádtica Aplicada a las TIC Universidad Politécnica de Madrid Madrid Spain

We construct a neural network model of S-parameters, from which the S-parameters can be quickly pre-dicted. Numerical tests on a filter model show that the proposed method accurately predicts the S-parameters with mul... 详细信息

ISBN: (数字)9798350351231

ISBN: (纸本)9798350351248

关键词： Deep learning Accuracy Transfer functions Predictive models Stability analysis Scattering parameters Numerical models Feedforward neural networks Numerical stability Electronics packaging

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