Niko Nevaranta

Online Estimation of Linear Tooth Belt Drive System Parameters

Many control schemes rely on an analytical model of the servomechanism to be controlled, and hence, accurate knowledge about the position- and time-dependent 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.

Online Identification of a Mechanical System in Frequency Domain Using Sliding DFT

A proper real-time system identification method is of great importance in order to acquire an analytical model that sufficiently represents the characteristics of the monitored system. While the use of different time-domain online identification techniques has been widely recognized as a powerful approach to system diagnostics, the frequency-domain identification techniques have primarily been considered for offline commissioning purposes. This paper addresses issues in the online frequency-domain identification of a mechanical system with varying dynamics; particular attention is paid to detect the changes in the system dynamics. A closed-loop online identification method is presented that is based on a sliding discrete Fourier transform at a selected set of frequencies. The method is experimentally validated by a closed-loop controlled servomechanism with a limited stroke and time-varying parameters.

Indirect tension control method for an intermittent web transport system

Tension and conveyance in web-handling machines should be controlled accurately to ensure high product quality. Unlike typical web handling applications, the motions in discontinuous web transport process are typically fast, and thus, dynamically more demanding. Conveying the web in a discontinuous way brings new performance requirements for position and tension control. In this paper, a simple control method is proposed for dynamic tension regulation in a discontinuous web transport system. The web tension is controlled indirectly based on feedback information about position difference between two rollers. The tension controller is implemented in parallel with the speed controller. The parallel control structure improves dynamic reaction to changes in the web tension. The performances of the mathematical model and the controller designed are tested with experimental tests. The tension controller is implemented as part of the frequency converter control structure. The effectiveness of the tension control in dynamical web handling is studied with simulations and by experimental tests.

Recursive identification of linear tooth belt-drive system

Tooth belt drives with directly connected permanent magnet servo motors are mechanically very flexible systems, and the resonances of the system change significantly as function of cart position and load. This paper addresses issues in identification of a linear tooth belt-drive with limited stroke. Particular attention is paid to detecting the changes in the system dynamics. This is achieved by using recursive least squares algorithm and exciting the system in different cart positions in order to identify the varying dynamics. Moreover, the direct identification in open-loop as well as under closed-loop conditions is considered. The identification of linear tooth belt drive is evaluated by simulations as well as through experimental tests.

Position controller tuning of an intermittent web transport system using off-line identification

Mathematical modeling as well as design of proper process controllers for web handling systems requires knowledge of the system dynamics or observations from an actual system. The dynamic behaviour of a web handling system is defined by the interactions between a flexible web, rollers and electric drives. In this paper, offline-identification of a web transport subsystem is considered. The identification of the system is carried out by exciting the controlled system with a pseudo-random binary signal (PRBS) as torque reference. A discrete time polynomial model with an Output-Error (OE) structure is used in the time-domain identification process. Moreover, this paper investigates the tension and position controls of an intermittent web transport system. The position controller of the system is designed for the identified and reference system model with H∞-loop shaping. Validation of the controller design and the influence of the model improvement are verified through experimental tests.

Sensorless positioning of a non-salient permanent magnet linear motor by combining open-loop current angle rotation method and back-EMF estimator

A sensorless positioning method for a non-salient permanent magnet linear synchronous motor is presented in this paper. The control method combines an open-loop current angle rotation method to the back-EMF estimator method. The open-loop current angle rotation method is used at low velocities and the back-EMF method at higher velocities. The position accuracy is defined by the open-loop current angle rotation method and improved with current angle compensation. The position accuracy of the sensorless method is verified with position measurements in experimental tests.

Online identification of AMB rotor system dynamics using sliding DFT

This paper presents an online nonparametric frequency response estimation approach for the identification of rotor dynamics of a high-speed machine supported by an active magnetic bearing (AMB) system. The closed-loop identification estimation approaches (direct and indirect) are based on a sliding discrete Fourier transform (SDFT) method that is applied in conjunction with a known multisine excitation signal design. The feasibility of the proposed identification approach is verified with experimental results on an AMB system. According to the results, the SDFT-based identification approach is applicable to online identification of rotor system dynamics in a computationally efficient manner.

Recursive parameter estimation of a mechanical system in frequency domain

Frequency-domain identification and parameter estimation methods are well established and commonly applied for commissioning and diagnostics purposes in electric drives. In this paper, the feasibility of a recursive least squares parameter estimation algorithm from frequency-domain observations is studied. The identification problem is treated from two different perspectives: first, by estimating a discrete autoregressive model with exogenous terms (ARX) from the discrete Fourier transforms (DFTs) of the input-output signals obtained from the identification experiment and second, a nonparametric model that is fitted in terms of least squares regression. Both proposed identification approaches are studied by simulations and experimentally validated by a closed-loop-controlled servomechanism.

Non-parametric frequency response estimation of two-mass-system using Kalman filter

This paper studies an online nonparametric identification method that is based on a time-frequency representation of signals using Kalman filter. The method utilizes swept excitation signal by synchronizing the Kalman filter to the frequency of the excitation signal generator and updating the filter gains on a sample-by-sample basis. Four different closed loop identification configuration are studied and experimentally compared by considering direct and indirect identification approaches. Moreover, this paper studies loop transfer function estimation from the closed loop controlled servomechanism and discusses the possibility to use the result for loop diagnostics. The experimental results illustrate the effectiveness of the studied online identification method to estimate nonparametric frequency response of the closed loop-controlled servomechanism at a selected band of frequencies.