Displacement estimation on a nonlinear system using various observers : application on a printer setup

Current developments in various technological elds show an increasingly important role for robotic systems. The medical world is no exception with clear examples like high-tech surgery- and service robots. To be able to generate an accurate 3D reconstruction, it is critical that the 3-dimensional positions and orientations of the detector and the positions of the X-ray source during a movement are accurately known. In order to measure exact positions of the X-ray source and detector, usually external measurements are required. Using IMU sensors, measurements can be performed on linear accelerations, rotational velocities and even angles using a magnetometer. Estimations of linear position displacements are dicult to obtain using a double integration over time of the acceleration measurements due to systematic errors (drift). Additional lters are required, i.e., Kalman lters. The estimation of linear displacement proves to be the most problematic, which leads to the choice for this particular setup. The printer has a single DOF (linear translation), which is subject to (nonlinear) disturbances in the form of friction. The challenge is to model the system according to the needs for various lters and estimate the position of the printer head using specically observed signals (typically the acceleration of the print head). The position measurement available on the system is only used for verication purposes. To correctly estimate the displacement when only using an acceleration signal an accurate model of the system is required. The system is a motion system with nonlinear friction of which the inuence should be found. If the inuence is signicant the estimation will have to take into account this nonlinearity in order to estimate the displacement

Frequency response function identification of LPV systems:a 2D-LRM approach with application to a medical X-ray system

LPV control has emerged as a systematic approach in the design of gain-scheduled controllers. This requires the identification of LPV models. The aim of this paper is to develope a flexible and accurate 2D-LRM approach, to enable fast and accurate non-parametric system identification of a frequency response function. The scope of this paper is on the identification of open-loop SISO LPV systems. Smoothness between several frozen LTI conditions within the LPV system is exploited to enable accurate pre-testing for parametric LPV modeling. The proposed approach achieves smoother estimations of the LPV behavior without an increase in estimation errors and with reduced variances. Traditional LPM and LRM approaches can be recovered as a special case of the proposed approach. The potential of the approach is shown by virtue of a simulation of a medical X-ray system

Accurate FRF identification of LPV systems:ND-LPM with application to a medical X-ray system

\u3cp\u3eLinear parameter varying (LPV) controller synthesis is a systematic approach for designing gain-scheduled controllers. The advances in LPV controller synthesis have spurred the development of system identification techniques that deliver the required models. The aim of this paper is to present an accurate and fast frequency response function (FRF) identification methodology for LPV systems. A local parametric modeling approach is developed that exploits smoothness over frequencies and scheduling parameters. By exploiting the smoothness over frequency as well as over the scheduling parameters, increased time efficiency in experimentation time and accuracy of the FRF is obtained. Traditional approaches, i.e., the local polynomial method/local rational method, are recovered as a special case of the proposed approach. The application potential is illustrated by a simulation example as well as real-life experiments on a medical X-ray system.\u3c/p\u3

Identifying a C-arc medical X-ray system : a 2D-LRM approach

abstract onl

Model-based geometric calibration for medical x-ray systems

Purpose: \u3cbr/\u3eCurrently applied calibration approaches lead to satisfying 3D roadmapping overlay and 3D reconstruction accuracies; however, the required calibration times are extensive. The aim of this paper is the introduction of a novel model-based approach for geometric system calibrations, leading to a significant reduction of calibration times.\u3cbr/\u3eMethods: \u3cbr/\u3eBy using physical insight into the system, a physical model is derived which can be exploited to predict geometric calibrations parameters. Model-parameters are estimated using a limited set of phantom-based measurement data. Effectively, the calibration procedure is recast to a parameter identification experiment.\u3cbr/\u3eResults: \u3cbr/\u3eThe potential of the proposed approach is illustrated by virtue of a benchmark object, successful reconstruction of a clinical phantom, and comparison to phantom-based accuracies.\u3cbr/\u3eConclusions: \u3cbr/\u3eAccurate models are required to achieve the desired accuracies. Based on the results in this work, the approach seems to be feasible for practical applications; however, to achieve all the desired specifications, future research should focus on enhanced modeling techniques

Image-based estimation and nonparametric modeling:Towards enhanced geometric calibration of an X-ray system

\u3cp\u3eGeometric calibrations of medical imaging systems are crucial to allow for advanced (X-ray) imaging techniques. Developments in medical procedures, lightweight system design and the growing costs of healthcare, leads to the desire for simpler and faster calibration approaches. The aim of this paper is to present a novel approach to enhance system calibrations for a wide range of imaging applications. The method is based on the introduction of small markers within the line of sight of the system, by virtue of a small mechanical adjustment to the system. By detecting markers in the X-ray images, displacements between the systems X-ray source and detector are in-situ measured. Additionally, the approach can be used to obtain nonparametric models of the dynamics of the mechanical system, enabling advanced observer-based estimation approaches. The potential of the method is illustrated by experimental results.\u3c/p\u3