101 research outputs found
Feedforward of sampled-data system for high-precision motion control using basis functions with ZOH differentiator
Get PDFFeedforward control has an important role in high-precision mechatronic systems. The aim of this research is to design a discrete-time feedforward controller to improve on-sample and intersample errors. The developed approach is parameterized using a linear combination of parameters and basis functions, which results in a parameterization that has intuitive physical meaning. The basis functions are designed with a differentiator that considers the sampled-data and zero-order-hold aspects. The performance improvement is demonstrated by comparing the developed approach with a conventional basis function design for a motion system.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Jan-Willem van Wingerde
Beyond Nyquist in Frequency Response Function Identification: Applied to Slow-Sampled Systems
No full textFast-sampled models are essential for control design, e.g., to address intersample behavior. The aim of this letter is to develop a non-parametric identification technique for fast-sampled models of systems that have relevant dynamics and actuation above the Nyquist frequency of the sensor, such as vision-in-the-loop systems. The developed method assumes smoothness of the frequency response function, which allows to disentangle aliased components through local models over multiple frequency bands. The method identifies fast-sampled models of slowly-sampled systems accurately in a single identification experiment. Finally, an experimental example demonstrates the effectiveness of the technique. Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Jan-Willem van Wingerde
Gaussian Processes for Advanced Motion Control
Get PDFMachine learning techniques, including Gaussian processes (GPs), are expected to play a significant role in meeting speed, accuracy, and functionality requirements in future data-intensive mechatronic systems. This paper aims to reveal the potential of GPs for motion control applications. Successful applications of GPs for feedforward and learning control, including the identification and learning for noncausal feedforward, position-dependent snap feedforward, nonlinear feedforward, and GP-based spatial repetitive control, are outlined. Experimental results on various systems, including a desktop printer, wirebonder, and substrate carrier, confirmed that data-based learning using GPs can significantly improve the accuracy of mechatronic systems.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Team Jan-Willem van Wingerde
A Kernel-Based Identification Approach to LPV Feedforward: With Application to Motion Systems
Get PDFThe increasing demands for motion control result in a situation where Linear
Parameter-Varying (LPV) dynamics have to be taken into account. Inverse-model
feedforward control for LPV motion systems is challenging, since the inverse of
an LPV system is often dynamically dependent on the scheduling sequence. The
aim of this paper is to develop an identification approach that directly
identifies dynamically scheduled feedforward controllers for LPV motion systems
from data. In this paper, the feedforward controller is parameterized in basis
functions, similar to, e.g., mass-acceleration feedforward, and is identified
by a kernel-based approach such that the parameter dependency for LPV motion
systems is addressed. The resulting feedforward includes dynamic dependence and
is learned accurately. The developed framework is validated on an example.Comment: Final author versio
Trace gas fluxes from intensively managed rice and soybean fields across three growing seasons in the brazilian amazon.
Get PDFThe emission of gases that may potentially intensify the greenhouse effect has received special attention due to their ability to raise global temperatures and possibly modify conditions for life on earth. The objectives of this study were the quantification of trace gas flux (N2O, CO2 and CH4) in soils of the lower Amazon basin that are planted with rice and soybean, and the relation of this flux to soil physical and chemical parameters and to precipitation. This study was conducted in agricultural fields planted with rice (Oryza Sativa) and soybean (Glycine max), located near the cities of Belterra and Santarém in western Pará State, Brazil, during the production years of 2005-2007. Measurements were done using static chambers in the field, and samples were analyzed by gas chromatography in the laboratory. Statistical analysis was conducted to determine variation ingas flux in the two crops, and the results show that CO2 flux varied between 305 and 227 mg-C m-2 h-1 under rice, and 243 and 156 mg-C m-2 h-1 under soybean. Flux of nitrous oxide (N2O) under rice varied between 4.5 and 20.4 ?g-N m-2 h-1, and under soybean flux variation was between 4.0 and 9.4 ?g- N m-2 h-1. Variation in flux of methane (CH4) under rice was between 5.1 and 14.0 ?g-C m-2 h-1, and under soybean it was 0.4 and 1.2 ?g-C m-2 h-1. These results demonstrate that, during our study period, the rice crop had higher flux for all trace gases than the soybean crop
Frequency Domain Identification of Multirate Systems: A Lifted Local Polynomial Modeling Approach
Get PDFFrequency-domain representations of multirate systems are essential for controller design and performance evaluation of multirate systems and sampled-data control. The aim of this paper is to develop a time-efficient closed-loop identification approach for multirate systems in the frequency-domain. The developed method utilizes local polynomial modeling for lifted representations of LPTV systems, which enables direct identification of closed-loop multirate systems in a single identification experiment. Unlike LTI identification techniques, the developed method does not suffer from bias due to ignored LPTV dynamics. The developed approach is demonstrated on a multirate example, resulting in accurate and fast identification in the frequency domain
Directors' duties and liabilities in the vicinity of insolvency
No full textItem does not contain fulltextThe Max Planck Institute & Radboud University Exchange Seminar, 14 oktober 201
Feedforward with acceleration and snap using sampled-data differentiator for a multi-modal motion system
Get PDFSampled-data control requires both on-sample and intersample performance in high-precision mechatronic systems. The aim is to design a discrete-time linearly parameterized feedforward controller to improve both on-sample and intersample performance in a multi-modal motion system. The continuous-time performance is considered as state compatibility by a multirate zero-order-hold differentiator. The developed approach enables the linearly parameterized feedforward controller design for sampled-data systems with physically intuitive tuning parameters. The performance improvement is validated by comparing the developed approach with a conventional approach using a backward differentiator for a multi-modal motion system
Position-Dependent Snap Feedforward: A Gaussian Process Framework
No full textMechatronic systems have increasingly high performance requirements for
motion control. The low-frequency contribution of the flexible dynamics, i.e.
the compliance, should be compensated for by means of snap feedforward to
achieve high accuracy. Position-dependent compliance, which often occurs in
motion systems, requires the snap feedforward parameter to be modeled as a
function of position. Position-dependent compliance is compensated for by using
a Gaussian process to model the snap feedforward parameter as a continuous
function of position. A simulation of a flexible beam shows that a significant
performance increase is achieved when using the Gaussian process snap
feedforward parameter to compensate for position-dependent compliance.Comment: To appear in: 2022 IEEE American Control Conference. arXiv admin
note: text overlap with arXiv:2201.0751
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