Identification of the mechanical system of a drive in the frequency domain

Pacas, Mario; Villwock, S.; Eutebach, T.

doi:10.1109/iecon.2004.1431740

Cited by 15 publications

(5 citation statements)

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“…For comparison, the inertia, current loop time constant, and friction parameter values of the test bench are obtained through accurate measurement methods. First, measure the frequency response of the X -axis by the sweep frequency test, 24,31 and fit the measured curve with the following model, and then the accurate inertia value and the equivalent time constant value can be obtained…”

Section: Verification Of the Identification Methodsmentioning

confidence: 99%

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Accurate inertia identification method of machine tool feed drives by considering the influence of current loop dynamics and friction

Zhan

Yang

Lyu

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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Accurate identification of the moment of inertia is the basis of modeling, simulation, and controller design of machine tool feed drives. However, insufficient consideration of colored noise factors (such as current loop dynamics, Coulomb’s friction, Stribeck’s friction, and nonlinear damping) will lead to inaccurate inertial identification results. This article proposes a new accurate offline identification method to accurately identify the equivalent inertia of machine tool feed drives. It is based on the least squares method and the instrumental variable method, and the equivalent time constant of the current loop, Coulomb’s friction parameters, Stribeck’s friction parameters, and the nonlinear damping parameter can be identified simultaneously with the inertia. First, a discrete transfer function of feed drives that considers the first-order dynamics of the current loop, Coulomb’s friction, Stribeck’s friction, and nonlinear damping is established. Then, inertia and the equivalent time constant of the current loop, Coulomb’s friction parameters, Stribeck’s friction parameters, and the nonlinear damping parameter are identified simultaneously based on the least squares method. Third, the instrumental variable method is used to correct the parameters identified by the least squares method. Finally, the inertia identification experiments are carried out on a ball screw feed drive system under different types and amplitudes of input signals. The experimental results show that the proposed inertia identification method can effectively improve the accuracy of inertia identification and its robustness to the input signal amplitude.

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Section: Verification Of the Identification Methodsmentioning

confidence: 99%

“…Figure 3 shows the measured feed drive frequency response and the estimated frequency response curve based on the inertia model shown in equation (31). The amplitude-frequency curve between 10 and 200 Hz is used for fitting to avoid the influence of nonlinear friction and structural resonance modes.…”

Section: Test Benchmentioning

confidence: 99%

Accurate inertia identification method of machine tool feed drives by considering the influence of current loop dynamics and friction

Zhan

Yang

Lyu

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part I:

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show abstract

“…An additional advantage of the Luenberger-Observer is the effective filtering of quantisation noise for low resolution position encoders. cx (12) …”

Section: X/b Axismentioning

confidence: 99%

“…Although the conservative approach presented here is robust against parameter variations in the range of 10 to 20 per cent, it is necessary to check the parameters after modifications like tool or workpiece changes. To ensure the robustness against inprocess parameter variations online parameter identification is required [12]. Modern control systems [13] measure the frequency response of the plant for commissioning purposes as shown in Figure 11 for the example in section 6.…”

Section: X/b Axismentioning

confidence: 99%

State Control of Servo Drives with Flexible Structural Components

Zirn¹,

Batzies²,

Weikert³

et al. 2006

Conference Record of the 2006 IEEE Industry Applications Conference Forty-First IAS Annual Meeting

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This paper is concerned with the state control of servo axes for robots and machine tools. Special consideration is given to the additional damping of elastic structural components. Their effect on the feedback-controlled system is described by a simplified but suitable dynamic model that allows the state observation as well as online parameter identification for all relevant cases of structural flexibility. This leads to an extension of standard servo drives by a robust state space controller. The practical benefit of this method and its implementation on standard control hardware is illustrated for a newly developed machine tool axis.

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“…Although the real mechanical system should be regarded as a complex multimass system, the simplified two-mass approximation has been successfully used in control design, for example, in the case of rolling-mill drives, conveyor drives, and other production processes [7]. As the two-mass-system-based modeling has become popular, numerous advanced control methods have been proposed, for example, in [8]- [12], in order to obtain high dynamics of the drive system.…”

mentioning

confidence: 99%

Online Estimation of Linear Tooth Belt Drive System Parameters

Nevaranta

Parkkinen

Lindh

et al. 2015

IEEE Trans. Ind. Electron.

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Many control schemes rely on an analytical model of the servomechanism to be controlled, and hence, accurate knowledge about the position-and timedependent parameter variability becomes crucial in many contexts, such as robust control methods. Although a properly designed robust controller can cope with a large parameter variation, real-time identification of the system parameter behavior could lead to several advantages by means of monitoring the varying dynamics, e.g., the predetermined uncertainty region around the nominal value. This paper addresses issues in online parameter estimation of a linear tooth belt drive with a limited stroke. Particular attention is paid to detecting the position-dependent changes in the system dynamics by using recursive least squares algorithm and exciting the system in different cart positions in order to identify the varying dynamics. The algorithm used is based on an indirect output-error identification scheme. The experimentally estimated parameters are compared with the corresponding two-mass model parameters. The results show an acceptable agreement and demonstrate the feasibility of the estimation method to estimate the parameters of a closed-loop controlled servomechanism with a limited stroke and time-varying parameters.Index Terms-Closed-loop identification, estimation, linear tooth belt drive, online, variable structure systems. I. INTRODUCTIONL INEAR movement is often needed with different industrial applications. Linear tooth belt drives offer low-cost solution for linear movements with a wide operating range and capability for high-speed use, and they are traditionally used in different conveyor applications and pick-and-place solutions. The main difficulties with linear tooth belt systems come from the elastic structure of the tooth belt itself, which sets the system's natural frequency low and limits the control bandwidth. Although the linear tooth belt drive has many drawbacks in [1], tooth belt drives are used in a high-precision laser-cutting application, which shows that, with proper control design, a linear tooth belt system can be applied to demanding linear applications as well. High performance of linear tooth belt drives requires that the system disturbances, parameter Manuscript

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Identification of the mechanical system of a drive in the frequency domain

Cited by 15 publications

References 1 publication

Accurate inertia identification method of machine tool feed drives by considering the influence of current loop dynamics and friction

Accurate inertia identification method of machine tool feed drives by considering the influence of current loop dynamics and friction

State Control of Servo Drives with Flexible Structural Components

Online Estimation of Linear Tooth Belt Drive System Parameters

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