On Frequency Response Function Identification for Advanced Motion Control

A key step in control of precision mechatronic systems is Frequency Response Function (FRF) identification. The aim of this paper is to illustrate relevant developments and solutions for FRF identification for advanced motion control. Specifically dealing with transient and/or closed-loop conditions that can normally lead to inaccurate estimation results. This yields essential insights for FRF identification for advanced motion control that are illustrated through a simulation study and validated on an experimental setup.Comment: 6 pages, IEEE 16th International Workshop on Advanced Motion Control, 202

Identifying Position-Dependent Mechanical Systems: A Modal Approach Applied to a Flexible Wafer Stage

Increasingly stringent performance requirements for motion control necessitate the use of increasingly detailed models of the system behavior. Motion systems inherently move, therefore, spatio-temporal models of the flexible dynamics are essential. In this paper, a two-step approach for the identification of the spatio-temporal behavior of mechanical systems is developed and applied to a lightweight prototype industrial wafer stage. The proposed approach exploits a modal modeling framework and combines recently developed powerful linear time invariant (LTI) identification tools with a spline-based mode-shape interpolation approach to estimate the spatial system behavior. The experimental results for the wafer stage application confirm the suitability of the proposed approach for the identification of complex position-dependent mechanical systems, and its potential for motion control performance improvements

Numerically reliable identification of fast sampled systems: a novel δ-domain data-dependent orthonormal polynomial approach

The practical utility of system identification algorithms is often limited by the reliability of their implementation in finite precision arithmetic. The aim of this paper is to develop a method for the numerically reliable identification of fast sampled systems. In this paper, a data-dependent orthonormal polynomial approach is developed for systems parametrized in the δ -domain. This effectively addresses both the numerical conditioning issues encountered in frequency-domain system identification and the inherent numerical round-off problems of fast-sampled systems in the common Z-domain description. Superiority of the proposed approach is shown in an example

Data-dependent orthogonal polynomials on generalized circles: A unified approach applied to δ-domain identification

The performance of algorithms in system identification and control, depends on their implementation in finite-precision arithmetic. The aim of this paper is to develop a unified approach for numerically reliable system identification that combines the numerical advantages of data-dependent orthogonal polynomials and the discrete-time δ-domain parametrization. In this paper, earlier results for discrete orthogonal polynomials on the real-line and the unit-circle are generalized to obtain an approach for the construction of orthogonal polynomials on generalized circles in the complex plane. This enables the formulation of a unified framework for the numerically reliable identification of systems expressed in the δ-domain, as well as in the traditional Laplace and Z-domains. An example is presented which shows the significant numerical advantages of the δ-domain approach for the identification of fast-sampled systems

On numerically reliable frequency-domain system identification: new connections and a comparison of methods

Abstract: Frequency domain identification of complex systems imposes important challenges with respect to numerically reliable algorithms. This is evidenced by the use of different rational and data-dependent basis functions in the literature. The aim of this paper is to compare these different methods and to establish new connections. This leads to two new identification algorithms. The conditioning and convergence properties of the considered methods are investigated on simulated and experimental data. The results reveal interesting convergence differences between (nonlinear) least squares and instrumental variable methods. In addition, the results shed light on the conditioning associated with so-called frequency localising basis functions, vector fitting algorithms, and (bi)-orthonormal basis functions

Identification for control of high-tech motion systems

abstract onl

Numerically reliable identification of fast sampled systems:a novel δ-domain data-dependent orthonormal polynomial approach

\u3cp\u3eThe practical utility of system identification algorithms is often limited by the reliability of their implementation in finite precision arithmetic. The aim of this paper is to develop a method for the numerically reliable identification of fast sampled systems. In this paper, a data-dependent orthonormal polynomial approach is developed for systems parametrized in the δ -domain. This effectively addresses both the numerical conditioning issues encountered in frequency-domain system identification and the inherent numerical round-off problems of fast-sampled systems in the common Z-domain description. Superiority of the proposed approach is shown in an example.\u3c/p\u3

Numerically reliable identification of fast sampled systems:a novel delta-domain data-dependent orthonormal polynomial approach

Abstract— The practical utility of system identification algorithms is often limited by the reliability of their implementation in finite precision arithmetic. The aim of this paper is to develop a method for the numerically reliable identification of fast sampled systems. In this paper, a data-dependent orthonormal polynomial approach is developed for systems parametrized in\u3cbr/\u3ethe δ-domain. This effectively addresses both the numerical conditioning issues encountered in frequency-domain system identification and the inherent numerical round-off problems of fast-sampled systems in the common Z-domain description. Superiority of the proposed approach is shown in an example

Global feedforward control of spatio-temporal mechanical systems: with application to a prototype wafer stage

High throughput requirements on high-precision manufacturing systems lead to a situation where the flexible dynamics hamper the performance at the positions of interest. Since these points are typically not measured directly, high performance local control of measured positions may lead to deteriorated performance due to internal deformations. A possible solution is to employ a control strategy which ensures that the desired rigid body motion is achieved, without exciting the parasitic flexible dynamics. In this paper, a feedforward controller design procedure is developed that achieves this type of global performance, which in turn leads to increased performance at the unmeasured positions of interest. The proposed method is applied to an experimental wafer stage showing that the proposed approach indeed leads to superior results with respect to the traditional local control approach