Frequency-domain representations are crucial for the design and performance evaluation of controllers in multirate systems, specifically to address intersample performance. The aim of this paper is to develop an effec...
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Frequency-domain representations are crucial for the design and performance evaluation of controllers in multirate systems, specifically to address intersample performance. The aim of this paper is to develop an effective frequency-domain system identification technique for closed-loop multirate systems using solely slow-rate output measurements. By indirect identification of multivariable time-invariant representations through lifting, in combination with localmodeling techniques, the multirate system is effectively identified. The developed method is capable of accurate identification of closed-loop multirate systems within a single identification experiment, using fast-rate excitation and inputs, and slow-rate outputs. Finally, the developed framework is validated using a benchmark problem consisting of a multivariable dual-stage actuator from a hard disk drive, demonstrating its applicability and accuracy.
Next -generation deformable mirrors are envisaged to exhibit low -frequency flexible dynamics and to contain a large number of spatially distributed actuators due to increasingly stringent performance requirements. Th...
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Next -generation deformable mirrors are envisaged to exhibit low -frequency flexible dynamics and to contain a large number of spatially distributed actuators due to increasingly stringent performance requirements. The increasingly complex system characteristics necessitate identifying the flexible dynamic behavior for design validation and next -generation control. The aim of this paper is to develop a unified approach for the identification of mechanical systems with a large number of spatially distributed actuators and a limited number of sensors. A frequency domain -based approach using localmodeling techniques is developed. The modal modeling framework is employed to analyze the design and create outputs that were not measured. The proposed approach is applied to an experimental deformable mirror case study that illustrates the effectiveness of the proposed approach.
Fast-rate models are essential for control design, specifically to address intersample behavior. The aim of this article is to develop a frequency-domain nonparametric identification technique to estimate fast-rate mo...
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Fast-rate models are essential for control design, specifically to address intersample behavior. The aim of this article is to develop a frequency-domain nonparametric identification technique to estimate fast-rate models of systems that have relevant dynamics and allow for actuation above the Nyquist frequency of a slow-rate output. Examples of such systems include vision-in-the-loop systems. Through local rational models over multiple frequency bands, aliased components are effectively disentangled, particularly for lightly damped systems. The developed technique accurately determines nonparametric fast-rate models of systems with slow-rate outputs, all within a single identification experiment. Finally, the effectiveness of the technique is demonstrated through experiments conducted on a prototype wafer stage used for semiconductor manufacturing.
Frequency response function (FRF) identification is often used as a basis for control systems design and as a starting point for subsequent parametric system identification. The aim of this paper is to develop a multi...
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Frequency response function (FRF) identification is often used as a basis for control systems design and as a starting point for subsequent parametric system identification. The aim of this paper is to develop a multiple-input multiple-output (MIMO) local parametric modeling approach for FRF identification of lightly damped mechanical systems with improved speed and accuracy. The proposed method is based on local rational models, which can efficiently handle the lightly-damped resonant dynamics. A key aspect herein is the freedom in the multivariable rational model parametrizations. Several choices for such multivariable rational model parametrizations are proposed and investigated. For systems with many inputs and outputs the required number of model parameters can rapidly increase, adversely affecting the performance of the localmodeling approach. Therefore, low-order model structures are investigated. The structure of these low-order parametrizations leads to an undesired directionality in the identification problem. To address this, an iterative local rational modeling algorithm is proposed. As a special case recently developed SISO algorithms are recovered. The proposed approach is successfully demonstrated on simulations and on an active vibration isolation system benchmark, confirming good performance of the method using significantly less parameters compared with alternative approaches. (C) 2017 Elsevier Ltd. All rights reserved.
作者：
Paul TacxTom OomenASML
Development and Engineering Mechatronics and Measurement Systems Veldhoven The Netherlands Department of Mechanical Engineering
Eindhoven University of Technology Eindhoven The Netherlands Faculty of Mechanical
Maritime and Materials Engineering Delft University of Technology Delft The Netherlands
Data-driven estimation of system norms is essential for analyzing, verifying, and designing control systems. Existing data-based methods often do not capture the inter-grid and transient behavior of the system, leadin...
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Data-driven estimation of system norms is essential for analyzing, verifying, and designing control systems. Existing data-based methods often do not capture the inter-grid and transient behavior of the system, leading to inaccurate and unreliable system norm estimations. This paper presents a unified approach for accurate and reliable estimation of the H 2 and H ∞ norm with a limited amount of data. The key step is to exploit localparametric models that explicitly incorporate the inter-grid and transient dynamics. The system norm is estimated through the computation of local system norms of the localparametric models within their the local frequency interval. Simulation and experimental results illustrate the effectiveness of the proposed method.
Thermal-induced deformations are becoming increasingly important for the control performance of precision motion systems. The aim of this paper is to identify the underlying thermal dynamics in view of precision motio...
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Thermal-induced deformations are becoming increasingly important for the control performance of precision motion systems. The aim of this paper is to identify the underlying thermal dynamics in view of precision motion control. Identifying thermal systems is challenging due to large transients, large time scales, excitation signal limitations, large environmental disturbances, and non-linear behavior. An approach for non-parametric identification is developed that is particularly suitable for thermal and mechanical aspects in mechatronic systems. In particular, prior knowledge of several domains can be directly specified. Additionally, the non-parametric model is used as a basis for parameter estimation of a grey-box model. The presented methods form a complete framework that facilitates the implementation of advanced control techniques and error compensation strategies by providing high-fidelity models, enabling increased accuracy and throughput in high precision motion control. (C) 2019, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
Thermal-induced deformations are becoming increasingly important for the control performance of precision motion systems. The aim of this paper is to identify the underlying thermal dynamics in view of precision motio...
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Thermal-induced deformations are becoming increasingly important for the control performance of precision motion systems. The aim of this paper is to identify the underlying thermal dynamics in view of precision motion control. Identifying thermal systems is challenging due to large transients, large time scales, excitation signal limitations, large environmental disturbances, and non-linear behavior. An approach for non-parametric identification is developed that is particularly suitable for thermal and mechanical aspects in mechatronic systems. In particular, prior knowledge of several domains can be directly specified. Additionally, the non-parametric model is used as a basis for parameter estimation of a grey-box model. The presented methods form a complete framework that facilitates the implementation of advanced control techniques and error compensation strategies by providing high-fidelity models, enabling increased accuracy and throughput in high precision motion control.
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