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IEEE Conference on Decision and control

作者： Chris Verhoek Jaap Eising Florian Dörfler Roland Tóth Control Systems Group Eindhoven University of Technology The Netherlands Institut für Automatik ETH Zürich Switzerland Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Hungary

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

A promising step from linear towards nonlinear data-driven control is via the design of controllers for linear parameter-varying (LPV) systems, which are linear systems whose parameters are varying along a measurable scheduling signal. However, the interplay between uncertainty arising from corrupted data and the parameter-varying nature of these systems impacts the stability analysis and limits the generalization of well-understood data-driven methods available for linear time-invariant systems. In this work, we decouple this interplay using a recently developed variant of the Fundamental Lemma for LPV systems and the concept of data-informativity, in combination with biquadratic Lyapunov forms. Together, these allow us to develop novel linear matrix inequality conditions for the existence of scheduling-dependent Lyapunov functions, incorporating the intrinsic nonlinearity. Appealingly, these results are stated purely in terms of the collected data and bounds on the noise, and they are computationally favorable to check.

关键词： Linear systems Uncertainty Noise Stability analysis Robustness Trajectory Linear matrix inequalities Noise measurement Output feedback Lyapunov methods

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Autonomous Hook-Based Grasping and Transportation with Quadcopters

arXiv

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arXiv 2023年

作者： Antal, Péter Péni, Tamás Tóth, Roland The Systems and Control Lab. HUN-REN Institute for Computer Science and Control Budapest Hungary The Control Systems Group The Eindhoven University of Technology Netherlands

Payload grasping and transportation with quadcopters is an active research area that has rapidly developed over the last decade. To grasp a payload without human interaction, most state-of-the-art approaches apply robotic arms that are attached to the quadcopter body. However, due to the large weight and power consumption of these aerial manipulators, their agility and flight time are limited. This paper proposes a motion control and planning method for transportation with a lightweight, passive manipulator structure that consists of a hook attached to a quadrotor using a 1 DoF revolute joint. To perform payload grasping, transportation, and release, first, time-optimal reference trajectories are designed through specific waypoints to ensure the fast and reliable execution of the tasks. Then, a two-stage motion control approach is developed based on a robust geometric controller for precise and reliable reference tracking and a linear–quadratic payload regulator for rapid setpoint stabilization of the payload swing. Furthermore, stability of the closed-loop system is mathematically proven to give safety guarantee for its operation. The proposed control architecture and design are evaluated in a high-fidelity physical simulator, and also in real flight experiments, using a custom-made quadrotor–hook manipulator platform. © 2023, CC BY-NC-SA.

关键词： Manipulators

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Closed-loop Aspects of Data-Enabled Predictive control

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IFAC-PapersOnLine 2023年第2期56卷 1388-1393页

作者： Rogier Dinkla Sebastiaan P. Mulders Jan-Willem van Wingerden Tom Oomen Delft University of Technology Delft Center for Systems and Control Mekelweg 2 2628 CD Delft The Netherlands Eindhoven University of Technology Control Systems Technology group De Zaale 5600 MB Eindhoven The Netherlands.

In recent years, the amount of data available from systems has drastically increased, motivating the use of direct data-driven control techniques that avoid the need of parametric modeling. The aim of this paper is to analyze closed-loop aspects of these approaches in the presence of noise. To analyze this, a unified formulation of several approaches, including Data-enabled Predictive control (DeePC) and Subspace Predictive control (SPC) is obtained and the influence of noise on closed-loop predictors is analyzed. The analysis reveals potential closed-loop correlation problems, which are closely related to well-known results in closed-loop system identification, and consequent control issues. A case study reveals the hazards of noise in data-driven control.

关键词： Data-driven control subspace predictive control data-enabled predictive control closed loop identification instrumental variables

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Optimal Synthesis of LTI Koopman Models for Nonlinear systems with Inputs

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IFAC-PapersOnLine 2022年第35期55卷 49-54页

作者： Lucian C. Iacob Roland Tóth Maarten Schoukens Control Systems Group Eindhoven University of Technology Eindhoven The Netherlands Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

A popular technique used to obtain linear representations of nonlinear systems is the so-called Koopman approach, where the nonlinear dynamics are lifted to a (possibly infinite dimensional) linear space through nonlinear functions called observables. In the lifted space, the dynamics are linear and represented by a so-called Koopman operator. While the Koopman theory was originally introduced for autonomous systems, it has been widely used to derive linear time-invariant (LTI) models for nonlinear systems with inputs through various approximation schemes such as the extended dynamics mode decomposition (EDMD). However, recent extensions of the Koopman theory show that the lifting process for such systems results in a linear parameter-varying (LPV) model instead of an LTI form. As LTI Koopman model based control has been successfully used in practice and it is generally temping to use such LTI descriptions of nonlinear systems, due to the simplicity of the associated control tool chain, a systematic approach is needed to synthesise optimal LTI approximations of LPV Koopman models compared to the ad-hoc schemes such as EDMD, which is based on least-squares regression. In this work, we introduce optimal LTI Koopman approximations of exact Koopman models of nonlinear systems with inputs by using ℓ 2 -gain and generalized H 2 norm performance measures. We demonstrate the advantages of the proposed Koopman modelling procedure compared to EDMD.

关键词： Nonlinear systems Koopman operator Linear Parameter-Varying systems

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Design of controls for ISS and Integral ISS Stabilization of Multistable State Periodic systems

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IFAC-PapersOnLine 2023年第2期56卷 4472-4477页

作者： Jesus Mendoza-Avila Angel Mercado-Uribe Denis Efimov Johannes Schiffer Inria Univ. Lille CNRS UMR 9189 - CRIStAL F-59000 Lille France Control Systems and Network Control Technology Group Brandenburg University of Technology Cottbus 03046 Germany Fraunhofer Research Institution for Energy Infrastructures and Geothermal Systems (IEG) Cottbus 03046 Germany

This paper is devoted to the construction of continuous and periodic controllers for ISS or iISS stabilization of a class of multistable state periodic systems. For this purpose, the concepts of practical ISS-, ISS- and iISS-control Leonov functions are introduced. Then, it is proven that the existence of these functions is a sufficient condition to ensure that the system under study is ISS- or iISS stabilizable, and explicit feedback control laws can be obtained by means of a mild adaptation of Sontag's universal formula. The proposed method is illustrated by the design of an excitation controller for a synchronous generator ensuring ISS of the closed-loop system.

关键词： Input-to-state stability control Lyapunov functions Multistable systems State periodic systems

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Closed-loop Data-Enabled Predictive control and its equivalence with Closed-loop Subspace Predictive control

arXiv

引用

arXiv 2024年

作者： Dinkla, Rogier Mulders, Sebastiaan P. Oomen, Tom van Wingerden, Jan-Willem Delft Center for Systems and Control Delft University of Technology Mekelweg 2 Delft2628CD Netherlands Control Systems Technology Group Eindhoven University of Technology Eindhoven5600 MB Netherlands

Factors like improved data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive control (DeePC). However, closed-loop identification bias arises in the presence of noise, which reduces the effectiveness of obtained control policies. In this paper we propose Closed-loop Data-enabled Predictive control (CL-DeePC), a framework that unifies different approaches to address this challenge. To this end, CL-DeePC incorporates instrumental variables (IVs) to synthesize and sequentially apply consistent single or multi-step-ahead predictors. Furthermore, a computationally efficient CL-DeePC implementation is developed that reveals an equivalence with Closed-loop Subspace Predictive control (CL-SPC). Compared to DeePC, CL-DeePC simulations demonstrate superior reference tracking, with a sensitivity study finding a 48% lower susceptibility to noise-induced reference tracking performance degradation. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Physics-guided neural networks for inversion-based feedforward control applied to hybrid stepper motors

Physics-guided neural networks for inversion-based feedforwa...

引用

2023 IEEE Conference on control technology and Applications, CCTA 2023

作者： Fan, D. Bolderman, M. Koekebakker, S. Butler, H. Lazar, M. Eindhoven University of Technology Control Systems Group Eindhoven5612 AP Netherlands Canon Production Printing Venlo5900 AE Netherlands Asml Veldhoven5504 DR Netherlands

ISBN: (纸本)9798350335446

Rotary motors, such as hybrid stepper motors (HSMs), are widely used in industries varying from printing applications to robotics. The increasing need for productivity and efficiency without increasing the manufacturing costs calls for innovative control design. Feedforward control is typically used in tracking control problems, where the desired reference is known in advance. In most applications, this is the case for HSMs, which need to track a periodic angular velocity and angular position reference. Performance achieved by feed-forward control is limited by the accuracy of the available model describing the inverse system dynamics. In this work, we develop a physics-guided neural network (PGNN) feedforward controller for HSMs, which can learn the effect of parasitic forces from data and compensate for it, resulting in improved accuracy. Indeed, experimental results on an HSM used in printing industry show that the PGNN outperforms conventional benchmarks in terms of the mean-absolute tracking error. © 2023 IEEE.

关键词： Feedforward control

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On a novel tracking differentiator design based on iterative learning in a moving window

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control Theory and technology 2023年第1期21卷 46-55页

作者： Xiangyang Li Rafal Madonski Zhiqiang Gao Senping Tian Key Laboratory of Autonomous Systems and Network Control Ministry of EducationSchool of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510641China Energy and Electricity Research Center Jinan UniversityZhuhai 519070China Department of Electrical and Computer Science Cleveland State UniversityClevelandOH 44115USA

Differential signals are key in control engineering as they anticipate future behavior of process variables and therefore are critical in formulating control laws such as proportional-integral-derivative(PID).The practical challenge,however,is to extract such signals from noisy measurements and this difficulty is addressed first by *** in the form of linear and nonlinear tracking differentiator(TD).While improvements were made,TD did not completely resolve the conflict between the noise sensitivity and the accuracy and timeliness of the *** two approaches proposed in this paper start with the basic linear TD,but apply iterative learning mechanism to the historical data in a moving window(MW),to form two new iterative learning tracking differentiators(IL-TD):one is a parallel IL-TD using an iterative ladder network structure which is implementable in analog circuits;the other a serial IL-TD which is implementable digitally on any computer *** algorithms are validated in simulations which show that the proposed two IL-TDs have better tracking differentiation and de-noise performance compared to the existing linear TD.

关键词： Tracking differentiator(TD) Iterative learning Iterative learning tracking differentiator(IL-TD) Active disturbance rejection control(ADRC)-Two-dimensional system(2-D system)

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Cloud Edge Collaborative Interaction technology Based On Compzxutility Boards 4

Cloud Edge Collaborative Interaction Technology Based On Com...

引用

4th IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Xie, Yehua Zhang, Chuanyu Chang, Bo Zhou, Jianxin Liu, Hongkun Wang, Xiaojin Nari Group Corporation State Grid Electric Power Research Institute Jiangsu China Nari Technology Development Co. Ltd Jiangsu China NARI-TECH Nanjing Control Systems Ltd Jiangsu China

ISBN: (纸本)9798350363586

The cloud-edge collaborative interaction technology, leveraging computing power boards, comprises cloud application management services, edge application management clients, and computing power boards. This technology and system serve as a bridge for application and data interaction between cloud and edge systems. The application management service is hosted in the cloud, while the application management client resides on the edge, and the computing power board is integrated into the physical server on the edge. The application management service encompasses modules like application management and edge management. The computing power board facilitates the subscription and deployment of lightweight AI large model applications at the edge. This technology enables the publication, subscription, downloading, and deployment of applications, standardizes application management, facilitates seamless upgrades, enables remote operation and maintenance, and enriches the application ecosystem. © 2024 IEEE.

关键词： Mobile edge computing

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State Derivative Normalization for Continuous-Time Deep Neural networks

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IFAC-PapersOnLine 2024年第15期58卷 253-258页

作者： Jonas Weigand Gerben I. Beintema Jonas Ulmen Daniel Görges Roland Tóth Maarten Schoukens Martin Ruskowski Chair of Machine Tools and Control Systems RPTU Kaiserslautern and the German Research Center for Artificial Intelligence Kaiserslautern Germany Control Systems (CS) Group at the Department of Electrical Engineering Eindhoven University of Technology Netherlands. R. Tóth is also affiliated to the Systems and Control Laboratory at the Institute for Computer Science and Control Budapest Hungary Institute for Electromobility RPTU Kaiserslautern Germany

The importance of proper data normalization for deep neural networks is well known. However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods. This results in a reduced model quality. In this contribution, we show that these three normalization tasks are inherently coupled. Due to the existence of this coupling, we propose a solution to all three normalization challenges by introducing a normalization constant at the state derivative level. We show that the appropriate choice of the normalization constant is related to the dynamics of the to-be-identified system and we derive multiple methods of obtaining an effective normalization constant. We compare and discuss all the normalization strategies on a benchmark problem based on experimental data from a cascaded tanks system and compare our results with other methods of the identification literature.

关键词： System Identification Continuous-Time Neural Ordinary Differential Equations Nonlinear State-Space

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