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IEEE Conference on Decision and control

作者： William P. Heath Joaquin Carrasco School of Computer Science and Engineering Bangor University Dean St LL57 1UT UK Department of Electrical and Electronic Engineering Control Systems and Robotics Group Nancy Rothwell Building University of Manchester M13 9PL UK

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Multipliers can be used to guarantee both the Lyapunov stability and input-output stability of Lurye systems with time-invariant memoryless slope-restricted nonlinearities. If a dynamic multiplier is used there is no guarantee the closedloop system has finite incremental gain. It has been suggested in the literature that without this guarantee such a system may be critically sensitive to time-varying exogenous signals including noise. We show that multipliers guarantee the power gain of the system to be bounded and quantifiable. Furthermore power may be measured about an appropriate steady state bias term, provided the multiplier does not require the nonlinearity to be odd. Hence dynamic multipliers can be used to guarantee Lurye systems have low sensitivity to noise, provided other exogenous systems have constant steady state. We illustrate the analysis with an example where the exogenous signal is a power signal with non-zero mean.

关键词： Sensitivity Power measurement Noise Stability analysis Steady-state Time-varying systems Lyapunov methods

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Parks-McClellan Algorithm-Based Active Damping Strategy Applied to Grid-Connected Inverters

Parks-McClellan Algorithm-Based Active Damping Strategy Appl...

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IEEE Annual Southern Power Electronics Conference (SPEC)

作者： José de Arimatéia O. Filho Gabriel Lucas Bressanini Tiago Davi Curi Busarello Luis De Oro Arenas Paulo Fernando Silva Helmo Kelis Morales Paredes Group of Automation and Integratd Systems (GASI) São Paulo State University (Unesp) Sorocaba Brazil Department of Control Automation and Computing Engineering Federal University of Santa Catarina (UFSC) Blumenau Brazil

In recent years, grid-connected inverters have gained wide prominence, primarily attributed to the surge in distributed energy resources. To ensure the reliable operation of these inverters, it is crucial to adequately damp the resonance peak of the LCL filter at the inverter output. By achieving this, the grid-connected inverter guarantees the system's stability while minimizing the harmonic content in the current injected into the distribution grid. To address this issue, this paper proposes the utilization of digital differentiators in conjunction with optimized wideband differentiators, which are designed using the Parks-McClellan (PM) algorithm. These differentiators are applied specifically to the LCL filter capacitor voltage to dampen the resonance peak effectively. Simulation results univocally demonstrate the effectiveness of the proposed approach.

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Unifying Model-Based and Neural Network Feedforward: Physics-Guided Neural Networks with Linear Autoregressive Dynamics

Unifying Model-Based and Neural Network Feedforward: Physics...

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IEEE Conference on Decision and control

作者： Johan Kon Dennis Bruijnen Jeroen van de Wijdeven Marcel Heertjes Tom Oomen Departement of Mechanical Engineering Control Systems Technology Group Eindhoven University of Technology The Netherlands Philips Engineering Solutions The Netherlands ASML The Netherlands Delft University of Technology The Netherlands

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Unknown nonlinear dynamics often limit the tracking performance of feedforward control. The aim of this paper is to develop a feedforward control framework that can compensate these unknown nonlinear dynamics using universal function approximators. The feedforward controller is parametrized as a parallel combination of a physics-based model and a neural network, where both share the same linear autoregressive (AR) dynamics. This parametrization allows for efficient output-error optimization through Sanathanan-Koerner (SK) iterations. Within each SK-iteration, the output of the neural network is penalized in the subspace of the physicsbased model through orthogonal projection-based regularization, such that the neural network captures only the unmodelled dynamics, resulting in interpretable models.

关键词： Friction Feedforward neural networks Nonlinear dynamical systems Feedforward systems Task analysis Optimization

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Frequency-Domain Data-Driven controller Synthesis for Unstable LPV systems ⁎

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IFAC-PapersOnLine 2021年第8期54卷 109-115页

作者： Tom Bloemers Roland Tóth Tom Oomen Control Systems Group Department of Electrical Engineering Eindhoven University of Technology 5612 AE Eindhoven The Netherlands Control Systems Technology Department of Mechanical Engineering Eindhoven University of Technology 5612 AE Eindhoven The Netherlands

Synthesizing controllers directly from frequency-domain measurement data is a powerful tool in the linear time-invariant framework. Ever-increasing performance requirements necessitate extending these approaches to account for plant variations. The aim of this paper is to develop frequency-domain analysis and synthesis conditions for local internal stability and H ∞ -performance of single-input single-output linear parameter-varying systems. The developed synthesis procedure only requires frequency-domain measurement data of the system and does not need a parametric model of the plant. The capabilities of the synthesis procedure are demonstrated on an unstable nonlinear system.

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Minimal realizations of input-output behaviors by LPV state-space representations with affine dependency

arXiv

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arXiv 2023年

作者： Petreczky, Mihály Tóth, Roland Mercère, Guillaume Univ. Lille CNRS Centrale Lille UMR 9189 CRIStAL LilleF-59000 France The University of Poitiers Laboratoire d’Informatique et d’Automatique pour les Systèmes France The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands The Systems and Control Laboratory Institute for Computer Science and Control Budapest Hungary

The paper makes the first steps towards a behavioral theory of LPV state-space representations with an affine dependency on scheduling, by characterizing minimality of such state-space representations. It is shown that minimality is equivalent to observability, and that minimal realizations of the same behavior are isomorphic. Finally, we establish a formal relationship between minimality of LPV state-space representations with an affine dependence on scheduling and minimality of LPV state-space representations with a dynamic and meromorphic dependence on scheduling. © 2023, CC BY.

关键词： State space methods

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Direct data-driven LPV control of nonlinear systems: An experimental result

arXiv

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arXiv 2022年

作者： Verhoek, Chris Abbas, Hossam S. Tóth, Roland Control Systems Group Dept. of Electrical Engineering Eindhoven University of Technology Eindhoven5600MB Netherlands Institute for Electrical Engineering in Medicine Universität zu Lübeck Lübeck23558 Germany Systems and Control Lab Institute for Computer Science and Control Budapest1111 Hungary

We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points. © 2022, CC BY-NC-SA.

关键词： Nonlinear systems

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Parameter Filter-based Event-triggered Learning

arXiv

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arXiv 2022年

作者： Schlor, Sebastian Solowjow, Friedrich Trimpe, Sebastian University of Stuttgart Institute for Systems Theory and Automatic Control Stuttgart70550 Germany Institute for Data Science in Mechanical Engineering RWTH Aachen University Aachen52068 Germany Intelligent Control Systems Group Max Planck Institute for Intelligent Systems Stuttgart70569 Germany

Model-based algorithms are deeply rooted in modern control and systems theory. However, they usually come with a critical assumption – access to an accurate model of the system. In practice, models are far from perfect. Even precisely tuned estimates of unknown parameters will deteriorate over time. Therefore, it is essential to detect the change to avoid suboptimal or even dangerous behavior of a control system. We propose to combine statistical tests with dedicated parameter filters that track unknown system parameters from state data. These filters yield point estimates of the unknown parameters and, further, an inherent notion of uncertainty. When the point estimate leaves the confidence region, we trigger active learning experiments. We update models only after enforcing a sufficiently small uncertainty in the filter. Thus, models are only updated when necessary and statistically significant while ensuring guaranteed improvement, which we call event-triggered learning. We validate the proposed method in numerical simulations of a DC motor in combination with model predictive control. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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Performance comparison of discrete-time robust adaptive controllers for grid-tied power converters under unbalanced grids

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e-Prime - Advances in Electrical engineering, Electronics and Energy 2023年 4卷

作者： de Oliveira Evald, Paulo Jefferson Dias Hollweg, Guilherme Vieira da Silveira, Wagner Barreto Milbradt, Deise Maria Cirollini Tambara, Rodrigo Varella Gründling, Hilton Abílio Intelligent Systems and Control Group Federal University of Pelotas Pelotas - RS Brazil Department of Electrical and Computer Engineering University of Michigan Deaborn MI United States Power Electronics and Control Group Federal University of Santa Maria Brazil - RS Santa Maria - RS Brazil

Renewable energy generation systems largely use grid-tied power converters with LCL filters. A well-known issue in this kind of system is the resonance of the LCL filter, whose frequency is straightforwardly affected by grid condition. Variations on grid impedance tends to make the controller performance poor and until turn the closed-loop unstable. Therefore, some controllers are designed for specific operation ranges to ensure performance and global stability. However, a deep study of Model Reference Adaptive-based controllers for this kind of issue was not conducted yet. The aim of this paper is to fit this research gap. Thus, this paper presents a performance comparison of feasible discrete-time robust adaptive controllers, based on reduced order reference model, for grid-tied power converters with LCL filter under unbalanced grid conditions, providing an assessment of the benefits and drawbacks of each considered control strategy. The evaluated control laws are: Robust Model Reference Adaptive controller, Robust Model Reference-based Adaptive Super-Twisting Sliding Mode controller, Robust Adaptive One Sample Ahead Preview controller, and Robust Adaptive Proportional-Integral controller. Through performance comparison, it can be defined what controller is more adequate in face of grid condition, taking into consideration grid-injected currents quality and design complexity of controller. Experimental results of grid-injected current control of a grid-tied 5.8 kW Voltage-Source Inverter with LCL filter considering relevant grid voltage unbalance are presented to corroborate the controllers’ performance and discuss their reference tracking response, robustness to the parametric variation, exogenous disturbance rejection, parameters adaptability, and global stability. In evaluated scenarios, Robust Adaptive Proportional-Integral controller presented slower regulation dynamics between compared adaptive structures, but reduced tracking error, as well as lower total h

关键词： Adaptive PI controller Grid-tied converters LCL Filter One sample ahead preview controller Robust model reference adaptive control Super-Twisting sliding mode

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Visual-Inertial and Leg Odometry Fusion for Dynamic Locomotion

Visual-Inertial and Leg Odometry Fusion for Dynamic Locomoti...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Victor Dhédin Haolong Li Shahram Khorshidi Lukas Mack Adithya Kumar Chinnakkonda Ravi Avadesh Meduri Paarth Shah Felix Grimminger Ludovic Righetti Majid Khadiv Joerg Stueckler Embodied Vision Group Max Planck Institute for Intelligent Systems Tuebingen Germany Movement Generation and Control Group Max Planck Institute for Intelligent Systems Tuebingen Germany Tandon School of Engineering New York University New York USA Oxford Robotics Institute University of Oxford England Autonomous Motion Department Max Planck Institute for Intelligent Systems Tuebingen Germany

Implementing dynamic locomotion behaviors on legged robots requires a high-quality state estimation module. Especially when the motion includes flight phases, state-of-the-art approaches fail to produce reliable estimation of the robot posture, in particular base height. In this paper, we propose a novel approach for combining visual-inertial odometry (VIO) with leg odometry in an extended Kalman filter (EKF) based state estimator. The VIO module uses a stereo camera and IMU to yield low-drift 3D position and yaw orientation and drift-free pitch and roll orientation of the robot base link in the inertial frame. However, these values have a considerable amount of latency due to image processing and optimization, while the rate of update is quite low which is not suitable for low-level control. To reduce the latency, we predict the VIO state estimate at the rate of the IMU measurements of the VIO sensor. The EKF module uses the base pose and linear velocity predicted by VIO, fuses them further with a second high-rate IMU and leg odometry measurements, and produces robot state estimates with a high frequency and small latency suitable for control. We integrate this lightweight estimation framework with a nonlinear model predictive controller and show successful implementation of a set of agile locomotion behaviors, including trotting and jumping at varying horizontal speeds, on a torque-controlled quadruped robot.

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Learning Stable and Robust Linear Parameter-Varying State-Space Models

Learning Stable and Robust Linear Parameter-Varying State-Sp...

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IEEE Conference on Decision and control

作者： Chris Verhoek Ruigang Wang Roland Tóth Control Systems Group Eindhoven University of Technology The Netherlands Australian Centre for Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Australia Vehicle Industry Research Center Szechenyi Istvan University Hungary

This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models. The model parametrizations guarantee a priori that for all parameter values during training, the allowed models are stable in the contraction sense or have their Lipschitz constant bounded by a user-defined value $\gamma$ . Furthermore, since the parametrizations are direct, the models can be trained using unconstrained optimization. The fact that the trained models are of the LPV-SS class makes them useful for, e.g., further convex analysis or controller design. The effectiveness of the approach is demonstrated on an LPV identification problem.

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