2020 |
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3. | R. Rozario; A. J. Fleming; T. Oomen Finite-Time Learning Control Using Frequency Response Data with Application to a Nanopositioning Stage Journal Article In: IEEE/ASME Transactions on Mechatronics, vol. 24, no. 5, pp. 2085-2096, 2020, ISSN: 10834435. Abstract | Links | BibTeX | Tags: Nanopositioning, Tracking Control @article{J20a, Learning control enables significant performance improvement for systems that perform repeating tasks. Achieving high tracking performance by utilizing past error data typically requires noncausal learning that is based on a parametric model of the process. Such model-based approaches impose significant requirements on modeling and filter design. This paper aims to reduce these requirements by developing a learning control framework that enables performance improvement through noncausal learning without relying on a parametric model. This is achieved by explicitly using the discrete Fourier transform to enable learning by using a nonparametric frequency response function model of the process. The effectiveness of the developed method is illustrated by application to a nanopositioning stage | |
2019 |
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2. | M. Omidbeike; A. A. Eielsen; Y. K. Yong; A. J. Fleming Multivariable Model-less Feedforward Control of a Monolithic Nanopositioning Stage With FIR Filter Inversion Proceedings Article In: International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), Helsinki, Finland, 2019, ISSN: 978-1-7281-0948-0. Abstract | Links | BibTeX | Tags: Nanopositioning, Tracking Control, Vibration Control @inproceedings{C19d, A model-less approach for inversion of the dynamics of multivariable systems using FIR filters is described. Inversion-based feedforward techniques have been widely used in the literature to achieve high-performance output tracking. The foremost difficulties associated with plant inversions are model uncertainties and non-minimum phase zeros. Various model-based methods have been proposed to exclude nonminimum phase zeros when inverting both single-input and single-output (SISO), and multiple-input and multiple-output (MIMO) systems. However, these methods increase the model uncertainty as they are no longer exact. To overcome these difficulties a model-less approach using FIR filters is presented. The results when applying the feedforward FIR filter to a multivariable nanopositioning system is presented, and they demonstrate the effectiveness of the feedforward technique in reducing the cross-coupling and achieving significantly improved output tracking. | |
1. | M. Omidbeike; Y. K. Yong; S. I. Moore; A. J. Fleming A Five-Axis Monolithic Nanopositioning Stage Constructed from a Bimorph Piezoelectric Sheet Proceedings Article In: International Conference on Manipulation, Automation and Robotics at Small Scales , Helsinki, Finland, 2019, ISSN: 978-1-7281-0948-0. Abstract | Links | BibTeX | Tags: Nanopositioning, Smart Structures, Tracking Control @inproceedings{omidbeike2019axis}, The paper describes design, modeling and control of a five-axis monolithic nanopositioning stage constructed from a bimorph piezoelectric sheet. In this design, actuators are created by removing parts of the sheet using ultrasonic machining. The constructed nanopositioner is ultra-compact with a thickness of 1 mm. It has a X and Y travel range of 15.5 µm and 13.2 µm respectively; a Z travel range of 26 µm; and a rotational motion about the X-and Y-axis of 600 µrad and 884 µrad respectively. The first resonance frequency occurs at 883 Hz in the Z-axis, and the second and third resonance frequency appears at 1850 Hz, rotating about the X-and Y-axis. A decentralized control strategy is implemented to track Z, θx and θy motions. The controller provides good tracking and significantly reduces cross-coupling motions among the three degrees-of-freedom. |
2020 |
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3. | Finite-Time Learning Control Using Frequency Response Data with Application to a Nanopositioning Stage Journal Article In: IEEE/ASME Transactions on Mechatronics, vol. 24, no. 5, pp. 2085-2096, 2020, ISSN: 10834435. | |
2019 |
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2. | Multivariable Model-less Feedforward Control of a Monolithic Nanopositioning Stage With FIR Filter Inversion Proceedings Article In: International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), Helsinki, Finland, 2019, ISSN: 978-1-7281-0948-0. | |
1. | A Five-Axis Monolithic Nanopositioning Stage Constructed from a Bimorph Piezoelectric Sheet Proceedings Article In: International Conference on Manipulation, Automation and Robotics at Small Scales , Helsinki, Finland, 2019, ISSN: 978-1-7281-0948-0. |