Modelling and control of laser surface treatment

The results of laser surface treatment may vary significantly during laser surface processing. These variations arise from the sensitivity of the process to disturbances, such as varying absorptivity and the small dimensions of the work piece. To increase the reproducibility of the process, a real-time feedback control system was designed and tested. Process models were developed to gain insight in the process behavior. As a test case, laser alloying of titanium (Ti6Al4V) with nitrogen was considered. Unfortunately, not all the desired processing results, such as the thickness of the alloyed layer, can be measured during processing. The quantities, which can be measured, are temperature related, e.g. the melt pool temperature and the melt pool surface area. Dynamic and steady-state models were developed, which relate the processing results to the measured quantities. A thermographic CCD camera was developed to measure the melt pool surface area in real-time. Pyrometers were applied to measure its temperature. The effects of the laser power, the beam velocity and the disturbances (absorptivity, thin work piece) on the temperature distribution and melt pool surface area, were analyzed theoretically, as well as experimentally. The width and length of the temperature distribution and the melt pool vary due to the disturbances. In the case of a thin work piece, the length varies more than the width. In the case of an absorptivity disturbance, the variation of the length and width are of the same order. In addition, it was found that the laser power can be best applied to counteract an absorptivity disturbance. The beam velocity can be best applied to suppress the negative effects introduced by small dimensions of the work piece. Based on these results, several controller algorithms, including multivariable algorithms, were implemented and tested. A mode-switch controller was able to produce a constant melt pool depth despite disturbances. This controller applied the laser power to suppress an absorptivity disturbance, and the beam velocity to counteract a geometrical disturbance. Hence, although it is not possible to measure the thickness of the alloyed layer directly, it is possible to control it by measuring and controlling temperature related quantities (temperature, melt pool area) at the surface

Proceedings of the NASA Conference on Space Telerobotics, volume 4

Papers presented at the NASA Conference on Space Telerobotics are compiled. The theme of the conference was man-machine collaboration in space. The conference provided a forum for researchers and engineers to exchange ideas on the research and development required for the application of telerobotic technology to the space systems planned for the 1990's and beyond. Volume 4 contains papers related to the following subject areas: manipulator control; telemanipulation; flight experiments (systems and simulators); sensor-based planning; robot kinematics, dynamics, and control; robot task planning and assembly; and research activities at the NASA Langley Research Center

Impact of Sensing and Actuation Characteristics on Artificial Pancreas Design

Type 1 diabetes mellitus (T1DM) is a chronic disease characterized by the body’s inability to produce insulin, leading to chronically high blood glucose (BG) concentrations. T1DM is treated by frequent self-administration of insulin based on BG measurements; however, there is a fine line between too little and too much insulin, and an overdose can lead to a dangerous drop in BG. The artificial pancreas (AP), consisting of a glucose sensor, an insulin pump, and a feedback control algorithm, will replace self-treatment by automatically calculating and delivering insulin dosages based on continuous glucose measurements. Many iterations of the AP utilize commercially available subcutaneous (SC) insulin pumps and glucose sensors, but these devices introduce physiological limitations that make control difficult. In this work, we present a clinical evaluation of an AP that uses SC devices, as well as an investigation of the intraperitoneal (IP) space as an alternative site for insulin delivery and glucose sensing to improve AP performance. Our results show that glucose sensors placed in the IP space have a lower time constant than SC sensors, allowing the controller to respond more quickly to BG disturbances. Similarly, insulin delivered through the IP space has faster pharmacokinetic and pharmacodynamic characteristics than SC insulin. Based on models of the sensing and actuation dynamics, a proportional-integral-derivative control algorithm with anti-reset windup protection was designed for the IP-IP route and evaluated on 10 simulated T1DM subjects. Using the IP-IP route led to a more robust controller that provided excellent control during the simulation studies. Our results support the development of a fully implantable AP that will operate within the IP space to safely and effectively control BG levels

Hopping, Landing, and Balancing with Springs

This work investigates the interaction of a planar double pendulum robot and springs, where the lower body (the leg) has been modified to include a spring-loaded passive prismatic joint. The thesis explores the mechanical advantage of adding a spring to the robot in hopping, landing, and balancing activities by formulating the motion problem as a boundary value problem; and also provides a control strategy for such scenarios. It also analyses the robustness of the developed controller to uncertain spring parameters, and an observer solution is provided to estimate these parameters while the robot is performing a tracking task. Finally, it shows a study of how well IMUs perform in bouncing conditions, which is critical for the proper operation of a hopping robot or a running-legged one

Microelectromechanical Systems and Devices

The advances of microelectromechanical systems (MEMS) and devices have been instrumental in the demonstration of new devices and applications, and even in the creation of new fields of research and development: bioMEMS, actuators, microfluidic devices, RF and optical MEMS. Experience indicates a need for MEMS book covering these materials as well as the most important process steps in bulk micro-machining and modeling. We are very pleased to present this book that contains 18 chapters, written by the experts in the field of MEMS. These chapters are groups into four broad sections of BioMEMS Devices, MEMS characterization and micromachining, RF and Optical MEMS, and MEMS based Actuators. The book starts with the emerging field of bioMEMS, including MEMS coil for retinal prostheses, DNA extraction by micro/bio-fluidics devices and acoustic biosensors. MEMS characterization, micromachining, macromodels, RF and Optical MEMS switches are discussed in next sections. The book concludes with the emphasis on MEMS based actuators

Computationally efficient algorithms and implementations of adaptive deep brain stimulation systems for Parkinson's disease

Clinical deep brain stimulation (DBS) is a tool used to mitigate pharmacologically intractable neurodegenerative diseases such as Parkinson's disease (PD), tremor and dystonia. Present implementations of DBS use continuous, high frequency voltage or current pulses so as to mitigate PD. This results in some limitations, among which there is stimulation induced side effects and shortening of pacemaker battery life. Adaptive DBS (aDBS) can be used to overcome a number of these limitations. Adaptive DBS is intended to deliver stimulation precisely only when needed. This thesis presents work undertaken to investigate, propose and develop novel algorithms and implementations of systems for adapting DBS. This thesis proposes four system implementations that could facilitate DBS adaptation either in the form of closed-loop DBS or spatial adaptation. The first method involved the use of dynamic detection to track changes in local field potentials (LFP) which can be indicative of PD symptoms. The work on dynamic detection included the synthesis of validation dataset using mainly autoregressive moving average (ARMA) models to enable the evaluation of a subset of PD detection algorithms for accuracy and complexity trade-offs. The subset of algorithms consisted of feature extraction (FE), dimensionality reduction (DR) and dynamic pattern classification stages. The combination with the best trade-off in terms of accuracy and complexity consisted of discrete wavelet transform (DWT) for FE, maximum ratio method (MRM) for DR and k-nearest neighbours (k-NN) for classification. The MRM is a novel DR method inspired by Fisher's separability criterion. The best combination achieved accuracy measures: F1-score of 97.9%, choice probability of 99.86% and classification accuracy of 99.29%. Regarding complexity, it had an estimated microchip area of 0.84 mm² for estimates in 90 nm CMOS process. The second implementation developed the first known PD detection and monitoring processor. This was achieved using complementary detection, which presents a hardware-efficient method of implementing a PD detection processor for monitoring PD progression in Parkinsonian patients. Complementary detection is achieved by using a combination of weak classifiers to produce a classifier with a higher consistency and confidence level than the individual classifiers in the configuration. The PD detection processor using the same processing stages as the first implementation was validated on an FPGA platform. By mapping the implemented design on a 45 nm CMOS process, the most optimal implementation achieved a dynamic power per channel of 2.26 μW and an area per channel of 0.2384 mm². It also achieved mean accuracy measures: Mathews correlation coefficient (MCC) of 0.6162, an F1-score of 91.38%, and mean classification accuracy of 91.91%. The third implementation proposed a framework for adapting DBS based on a critic-actor control approach. This models the relationship between a trained clinician (critic) and a neuro-modulation system (actor) for modulating DBS. The critic was implemented and validated using machine learning models, and the actor was implemented using a fuzzy controller. Therapy is modulated based on state estimates obtained through the machine learning models. PD suppression was achieved in seven out of nine test cases. The final implementation introduces spatial adaptation for aDBS. Spatial adaptation adjusts to variation in lead position and/or stimulation focus, as poor stimulation focus has been reported to affect therapeutic benefits of DBS. The implementation proposes dynamic current steering systems as a power-efficient implementation for multi-polar multisite current steering, with a particular focus on the output stage of the dynamic current steering system. The output stage uses dynamic current sources in implementing push-pull current sources that are interfaced to 16 electrodes so as to enable current steering. The performance of the output stage was demonstrated using a supply of 3.3 V to drive biphasic current pulses of up to 0.5 mA through its electrodes. The preliminary design of the circuit was implemented in 0.18 μm CMOS technology

Design, Development, and Evaluation of a Teleoperated Master-Slave Surgical System for Breast Biopsy under Continuous MRI Guidance

The goal of this project is to design and develop a teleoperated master-slave surgical system that can potentially assist the physician in performing breast biopsy with a magnetic resonance imaging (MRI) compatible robotic system. MRI provides superior soft-tissue contrast compared to other imaging modalities such as computed tomography or ultrasound and is used for both diagnostic and therapeutic procedures. The strong magnetic field and the limited space inside the MRI bore, however, restrict direct means of breast biopsy while performing real-time imaging. Therefore, current breast biopsy procedures employ a blind targeting approach based on magnetic resonance (MR) images obtained a priori. Due to possible patient involuntary motion or inaccurate insertion through the registration grid, such approach could lead to tool tip positioning errors thereby affecting diagnostic accuracy and leading to a long and painful process, if repeated procedures are required. Hence, it is desired to develop the aforementioned teleoperation system to take advantages of real-time MR imaging and avoid multiple biopsy needle insertions, improving the procedure accuracy as well as reducing the sampling errors. The design, implementation, and evaluation of the teleoperation system is presented in this dissertation. A MRI-compatible slave robot is implemented, which consists of a 1 degree of freedom (DOF) needle driver, a 3-DOF parallel mechanism, and a 2-DOF X-Y stage. This slave robot is actuated with pneumatic cylinders through long transmission lines except the 1-DOF needle driver is actuated with a piezo motor. Pneumatic actuation through long transmission lines is then investigated using proportional pressure valves and controllers based on sliding mode control are presented. A dedicated master robot is also developed, and the kinematic map between the master and the slave robot is established. The two robots are integrated into a teleoperation system and a graphical user interface is developed to provide visual feedback to the physician. MRI experiment shows that the slave robot is MRI-compatible, and the ex vivo test shows over 85%success rate in targeting with the MRI-compatible robotic system. The success in performing in vivo animal experiments further confirm the potential of further developing the proposed robotic system for clinical applications