检索结果-内蒙古大学图书馆

Safe Barrier-Constrained control of Uncertain systems via Event-triggered Learning

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Lederer, Armin Begzadić, Azra Hirche, Sandra Cortés, Jorge Herbert, Sylvia The Learning and Adaptive Systems Group Department of Computer Science ETH Zurich Switzerland The Department of Mechanical and Aerospace Engineering University of California San Diego United States The Chair of Information-oriented Control TUM School of Computation Information and Technology Technical University of Munich Germany

While control barrier functions are employed in addressing safety, control synthesis methods based on them generally rely on accurate system dynamics. This is a critical limitation, since the dynamics of complex systems are often not fully known. Supervised machine learning techniques hold great promise for alleviating this weakness by inferring models from data. We propose a novel control barrier function-based framework for safe control through event-triggered learning, which switches between prioritizing control performance and improving model accuracy based on the uncertainty of the learned model. By updating a Gaussian process model with training points gathered online, the approach guarantees the feasibility of control barrier function conditions with high probability, such that safety can be ensured in a data-efficient manner. Furthermore, we establish the absence of Zeno behavior in the triggering scheme, and extend the algorithm to sampled-data realizations by accounting for inter-sampling effects. The effectiveness of the proposed approach and theory is demonstrated in simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Gaussian distribution

Novel convex decomposition of piecewise affine functions

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Schlüter, Nils Darup, Moritz Schulze the Control and Cyberphysical Systems Group Department of Mechanical Engineering TU Dortmund University Germany

In this paper, we present a novel approach to decompose a given piecewise affine (PWA) function into two convex PWA functions. Convex decompositions are useful to speed up or distribute evaluations of PWA functions. Different approaches to construct a convex decomposition have already been published. However, either the two resulting convex functions have very high or very different complexities, which is often undesirable, or the decomposition procedure is inapplicable even for simple cases. Our novel methodology significantly reduces these drawbacks in order to extend the applicability of convex decompositions. © 2021, CC BY-NC-ND.

关键词： Functions

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

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Verhoek, Chris Wang, Ruigang Tóth, Roland Control Systems Group Eindhoven University of Technology Netherlands The Australian Centre for Robotics The School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney Australia The Vehicle Industry Research Center Széchenyi István 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 γ. 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. © 2023, CC BY-NC-SA.

关键词： Optimization

Human-Exoskeleton Interaction Force Estimation Based on Quasi-Direct Drive Actuators

学校读者我要写书评

暂无评论

Human-Exoskeleton Interaction Force Estimation Based on Quas...

IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob)

作者： Julian Seiler Niklas Schêfer Guoping Zhao Bastian Latsch Martin Grimmer Philipp Beckerle Mario Kupnik Measurement and Sensor Technology Group Technical University of Darmstadt Germany School of Mechanical Engineering Southeast University China Lauflabor Locomotion Laboratory Technical University of Darmstadt Germany Control and Cyber-Physical Systems Technical University of Darmstadt Germany Chair of Autonomous Systems and Mechatronics Friedrich-Alexander-Universität Erlangen Nurnberg Germany

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Exoskeletons have emerged as a promising tech-nological solution to provide functional compensation, training support, physical enhancement, and rehabilitation for an aging population. High torque density and control bandwidth are essential for exoskeleton actuation. In addition, monitoring and control of human-exoskeleton interaction forces is essential for the effectiveness, safety, and comfort of exoskeletons. Quasi-direct drives (QDD) have the potential to address the actuation requirements by enabling proprioceptive actuation with low mechanical output impedance. We present a test bench system to identify and validate the system dynamics, including torque constant, inertia, and friction properties of the QDD Cube-Mars AK10-9 V1.1. The actuator is employed in a hip exoskeleton to evaluate open-loop torque tracking and interaction force estimation in an assisted gait scenario with one participant walking on a treadmill. The model estimates interaction forces with a mean absolute error (MAE) of 2.78±0.58N (6.4% of rated force output). Model-based open-loop control improves the torque tracking MAE by 23%. Our analysis indicates that considering the soft coupling between human and exoskeleton has the potential to further improve the torque tracking and interaction force estimation accuracy. Altogether, the findings demonstrate that QDD actuation enables backdrivability, re-liable force estimation and controllable assistance, thereby providing an effective solution for exoskeleton actuation.

关键词： Actuators Torque Accuracy Friction Exoskeletons Force Dynamics Estimation Impedance Hip

DAFNI: A computational platform to support infrastructure systems research

学校读者我要写书评

暂无评论

Proceedings of the Institution of Civil Engineers: Smart Infrastructure and Construction 2023年第3期176卷 108-116页

作者： Matthews, Brian Hall, Jim Batty, Michael Blainey, Simon Cassidy, Nigel Choudhary, Ruchi Coca, Daniel Hallett, Stephen Harou, Julien J James, Phil Lomax, Nik Oliver, Peter Sivakumar, Aruna Tryfonas, Theodoros Varga, Liz Scientific Computing Department Science and Technology Facilities Council Didcot United Kingdom School of Geography and the Environment University of Oxford Oxford United Kingdom Centre for Advanced Spatial Analysis University College London London United Kingdom Transportation Research Group Faculty of Engineering and Physical Sciences University of Southampton Southampton United Kingdom Department of Civil Engineering University of Birmingham Birmingham United Kingdom Department of Engineering University of Cambridge Cambridge United Kingdom Department of Automatic Control and Systems Engineering University of Sheffield Sheffield United Kingdom Centre for Environmental and Agricultural Informatics Cranfield University Cranfield United Kingdom Department of Mechanical Aerospace and Civil Engineering University of Manchester Manchester United Kingdom School of Engineering Newcastle University Newcastle United Kingdom School of Geography University of Leeds Leeds United Kingdom Department of Civil and Environmental Engineering Imperial College London London United Kingdom Department of Civil Engineering University of Bristol Bristol United Kingdom Department of Civil Environmental and Geomatic Engineering University College London London United Kingdom

Research into the engineering of infrastructure systems is increasingly data intensive. Researchers build computational models to explore scenarios such as investigating the merits of infrastructure plans, analysing historical data to inform system operations or assessing the impacts of infrastructure on the environment. Models are more complex, at higher resolution and with larger coverage. Researchers also require a 'multi-systems' approach to explore interactions between systems, such as energy and water with urban development, and across scales, from buildings and streets to regions or nations. Consequently, researchers need enhanced computational resources to support cross-institutional collaboration and sharing at scale. The Data and Analytics Facility for National Infrastructure (DAFNI) is an emerging computational platform for infrastructure systems research. It provides high-throughput compute resources so larger data sets can be used, with a data repository to upload data and share these with collaborators. Users' models can also be uploaded and executed using modern containerisation techniques, giving platform independence, scaling and sharing. Further, models can be combined into workflows, supporting multi-systems modelling and generating visualisations to present results. DAFNI forms a central resource accessible to all infrastructure systems researchers in the UK, supporting collaboration and providing a legacy, keeping data and models available beyond the lifetime of a project. © 2023 Emerald Publishing Limited: All rights reserved.

关键词： Urban growth

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

学校读者我要写书评

暂无评论

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.

关键词：

A moving horizon estimator for aquifer thermal energy storages

学校读者我要写书评

暂无评论

arXiv 2025年

作者： van Randenborgh, Johannes Darup, Moritz Schulze Control and Cyberphysical Systems Group Germany Faculty of Mechanical Engineering TU Dortmund University Germany

Aquifer thermal energy storages (ATES) represent groundwater saturated aquifers that store thermal energy in the form of heated or cooled groundwater. Combining two ATES, one can harness excess thermal energy from summer (heat) and winter (cold) to support the building’s heating, ventilation, and air conditioning (HVAC) technology. In general, a dynamic operation of ATES throughout the year is beneficial to avoid using fossil fuel-based HVAC technology and maximize the "green use" of ATES. Model predictive control (MPC) with an appropriate system model may become a crucial control approach for ATES systems. Consequently, the MPC model should reflect spatial temperature profiles around ATES’ boreholes to predict extracted groundwater temperatures accurately. However, meaningful predictions require the estimation of the current state of the system, as measurements are usually only at the borehole of the ATES. In control, this is often realized by model-based observers. Still, observing the state of an ATES system is non-trivial, since the model is typically hybrid. We show how to exploit the specific structure of the hybrid ATES model and design an easy-to-solve moving horizon estimator based on a quadratic program. © 2025, CC BY-NC-ND.

关键词： Quadratic programming

Parameter Filter-based Event-triggered Learning

学校读者我要写书评

暂无评论

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

Discovery of sparse hysteresis models for piezoelectric materials

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Chandra, Abhishek Daniels, Bram Curti, Mitrofan Tiels, Koen Lomonova, Elena A. Tartakovsky, Daniel M. Electromechanics and Power Electronics Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Control Systems Technology Group Department of Mechanical Engineering Eindhoven University of Technology Eindhoven5600 MB Netherlands Department of Energy Science and Engineering Stanford University 367 Panama Street StanfordCA94305 United States

This article presents an approach for modelling hysteresis in piezoelectric materials, that leverages recent advancements in machine learning, particularly in sparse-regression techniques. While sparse regression has previously been used to model various scientific and engineering phenomena, its application to nonlinear hysteresis modelling in piezoelectric materials has yet to be explored. The study employs the least-squares algorithm with a sequential threshold to model the dynamic system responsible for hysteresis, resulting in a concise model that accurately predicts hysteresis for both simulated and experimental piezoelectric material data. Several numerical experiments are performed, including learning butterfly-shaped hysteresis and modelling real-world hysteresis data for a piezoelectric actuator. The presented approach is compared to traditional regression-based and neural network methods, demonstrating its efficiency and robustness. Source code is available at https://***/chandratue/SmartHysteresis. © 2023, CC BY.

关键词： Hysteresis