Adaptive Sliding Mode Contouring Control Design Based on Reference Adjustment and Uncertainty Compensation for Feed Drive Systems

Industrial feed drive systems, particularly, ball-screw and lead-screw feed drives are among the dominating motion components in production and manufacturing industries. They operate around the clock at high speeds for coping with the rising production demands. Adversely, high-speed motions cause mechanical vibrations, high-energy consumption, and insufficient accuracy. Although there are many control strategies in the literature, such as sliding mode and model predictive controls, further research is necessary for precision enhancement and energy saving. This study focused on design of an adaptive sliding mode contouring control based on reference adjustment and uncertainty compensation for feed drive systems. A combined reference adjustment and uncertainty compensator for precision motion of industrial feed drive systems were designed. For feasibility of the approach, simulation using matlab was conducted, and results are compared with those of an adaptive nonlinear sliding model contouring controller. The addition of uncertainty compensator showed a substantial improvement in performance by reducing the average contour error by 85.71% and the maximum contouring error by 78.64% under low speed compared to the adaptive sliding mode contouring controller with reference adjustment. Under high speed, the addition of uncertainty compensator reduced the average and absolute maximum contour errors by 4.48% and 10.13%, respectively. The experimental verification will be done in future. Keywords:    Machine tools, Feed drive systems, contouring control, Uncertainty dynamics, Sliding mode control

Modeling Surfaces from Volume Data Using Nonparallel Contours

Magnetic resonance imaging: MRI) and computed tomography: CT) scanners have long been used to produce three-dimensional samplings of anatomy elements for use in medical visualization and analysis. From such datasets, physicians often need to construct surfaces representing anatomical shapes in order to conduct treatment, such as irradiating a tumor. Traditionally, this is done through a time-consuming and error-prone process in which an experienced scientist or physician marks a series of parallel contours that outline the structures of interest. Recent advances in surface reconstruction algorithms have led to methods for reconstructing surfaces from nonparallel contours that could greatly reduce the manual component of this process. Despite these technological advances, the segmentation process has remained unchanged. This dissertation takes the first steps toward bridging the gap between the new surface reconstruction technologies and bringing those methods to use in clinical practice. We develop VolumeViewer, a novel interface for modeling surfaces from volume data by allowing the user to sketch contours on arbitrarily oriented cross-sections of the volume. We design the algorithms necessary to support nonparallel contouring, and we evaluate the system with medical professionals using actual patient data. In this way, we begin to understand how nonparallel contouring can aid the segmentation process and expose the challenges associated with a nonparallel contouring system in practice

Reference trajectory modification based on spatial iterative learning for contour control of 2-axis NC systems

Contour error is a main factor that affects the quality of products in numerical control (NC) machining. This paper presents a contour control strategy based on digital curves for high-precision control of computer numerical control (CNC) machines. A contour error estimation algorithm is presented for digital curves based on a geometrical method. The dynamic model of the motion control system is transformed from time domain to space domain because the contour error is dependent on space instead of time. Spatial iterative learning control (sILC) is developed to reduce the contour error, by modifying the reference trajectory in the form of G code. This allows system improvement without interference of low-level controllers so it is applicable to many commercial controllers where interpolators and feed-drive controllers cannot be altered. The effectiveness of this method is verified by experiments on a NC machine, which have shown good performance not only for smooth trajectories but also for large curvature trajectories

Cardiovascular Magnetic Resonance Deformation Imaging By Feature Tracking For Assessment Of Left And Right Ventricular Structure And Function

The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the authorCardiac magnetic resonance (CMR) imaging is the gold standard imaging technique for assessment of ventricular dimensions and function. CMR also allows assessment of ventricular deformation but this requires additional imaging sequences and time consuming post processing which has limited its widespread use. A novel CMR analysis software package, ‘feature tracking’ (Tom Tec, Germany) can measure ventricular deformation directly from cine CMR images. This thesis seeks to further our understanding of the feasibility of feature tracking to assess myocardial deformation and volumetric measures. Chapter 3 validates normal ranges for deformation parameters and compares values against traditional tagging measures. The work identifies global circumferential strain measures as being the most reproducible. In chapters 4 and 5, feature tracking values for left and right ventricular strain are compared with echocardiography derived speckle tracking indices of deformation. For left ventricular (LV) parameters, circumferential and longitudinal strain are most consistent and for the right ventricular (RV) measures, assessment of free wall strain using feature tracking shows promise and with modifications in algorithms is likely to further improve in the future. Chapter 6 assesses the ability of feature tracking to measure diastolic function. The results show that radial diastolic velocities and longitudinal diastolic strain rates can predict diastolic dysfunction (as diagnosed by echocardiography) with acceptable levels of sensitivity and specificity, particularly when used in combination. 11 The use of feature tracking to provide automated measures of ventricular volumes, mass and ejection fraction is assessed in chapter 7. Feature tracking in this context shows acceptable correlation but poor absolute agreement with manual contouring and further adjustments to algorithms is necessary to improve its accuracy. This work offers insights into the use of feature tracking for the assessment of ventricular deformation parameters. It is a technique with advantages over CMR tagging methods and given the speed of post processing has the potential to become the CMR preferred assessment for strain quantification in the future.I am indebted to the Engineering and Physical Sciences Research Council, the British Heart Foundation and the National Institute for Health Research Oxford Biomedical Research Centre for funding this work

Improving Radiotherapy Targeting for Cancer Treatment Through Space and Time

Radiotherapy is a common medical treatment in which lethal doses of ionizing radiation are preferentially delivered to cancerous tumors. In external beam radiotherapy, radiation is delivered by a remote source which sits several feet from the patient\u27s surface. Although great effort is taken in properly aligning the target to the path of the radiation beam, positional uncertainties and other errors can compromise targeting accuracy. Such errors can lead to a failure in treating the target, and inflict significant toxicity to healthy tissues which are inadvertently exposed high radiation doses. Tracking the movement of targeted anatomy between and during treatment fractions provides valuable localization information that allows for the reduction of these positional uncertainties. Inter- and intra-fraction anatomical localization data not only allows for more accurate treatment setup, but also potentially allows for 1) retrospective treatment evaluation, 2) margin reduction and modification of the dose distribution to accommodate daily anatomical changes (called `adaptive radiotherapy\u27), and 3) targeting interventions during treatment (for example, suspending radiation delivery while the target it outside the path of the beam). The research presented here investigates the use of inter- and intra-fraction localization technologies to improve radiotherapy to targets through enhanced spatial and temporal accuracy. These technologies provide significant advancements in cancer treatment compared to standard clinical technologies. Furthermore, work is presented for the use of localization data acquired from these technologies in adaptive treatment planning, an investigational technique in which the distribution of planned dose is modified during the course of treatment based on biological and/or geometrical changes of the patient\u27s anatomy. The focus of this research is directed at abdominal sites, which has historically been central to the problem of motion management in radiation therapy

Dynamical models and machine learning for supervised segmentation

This thesis is concerned with the problem of how to outline regions of interest in medical images, when the boundaries are weak or ambiguous and the region shapes are irregular. The focus on machine learning and interactivity leads to a common theme of the need to balance conflicting requirements. First, any machine learning method must strike a balance between how much it can learn and how well it generalises. Second, interactive methods must balance minimal user demand with maximal user control. To address the problem of weak boundaries,methods of supervised texture classification are investigated that do not use explicit texture features. These methods enable prior knowledge about the image to benefit any segmentation framework. A chosen dynamic contour model, based on probabilistic boundary tracking, combines these image priors with efficient modes of interaction. We show the benefits of the texture classifiers over intensity and gradient-based image models, in both classification and boundary extraction. To address the problem of irregular region shape, we devise a new type of statistical shape model (SSM) that does not use explicit boundary features or assume high-level similarity between region shapes. First, the models are used for shape discrimination, to constrain any segmentation framework by way of regularisation. Second, the SSMs are used for shape generation, allowing probabilistic segmentation frameworks to draw shapes from a prior distribution. The generative models also include novel methods to constrain shape generation according to information from both the image and user interactions. The shape models are first evaluated in terms of discrimination capability, and shown to out-perform other shape descriptors. Experiments also show that the shape models can benefit a standard type of segmentation algorithm by providing shape regularisers. We finally show how to exploit the shape models in supervised segmentation frameworks, and evaluate their benefits in user trials