CHALLENGES OF CONTROL DESIGN FOR PRECISION SERVO SYSTEM WITH APPLICATION ON HARD DISK DRIVE

Ph.DDOCTOR OF PHILOSOPH

Uncertainty modeling in reliability analysis of floating wind turbine support structures

Acknowledgments: The first author would like to thank the Petroleum Technology Development Fund (PTDF), Nigeria for the funding of this PhD research.Peer reviewedPostprin

Probabilistic surrogate modeling of offshore wind-turbine loads with chained Gaussian processes

Heteroscedastic Gaussian process regression, based on the concept of chained Gaussian processes, is used to build surrogates to predict site-specific loads on an offshore wind turbine. Stochasticity in the inflow turbulence and irregular waves results in load responses that are best represented as random variables rather than deterministic values. Moreover, the effect of these stochastic sources on the loads depends strongly on the mean environmental conditions -- for instance, at low mean wind speeds, inflow turbulence produces much less variability in loads than at high wind speeds. Statistically, this is known as heteroscedasticity. Deterministic and most stochastic surrogates do not account for the heteroscedastic noise, giving an incomplete and potentially misleading picture of the structural response. In this paper, we draw on the recent advancements in statistical inference to train a heteroscedastic surrogate model on a noisy database to predict the conditional pdf of the response. The model is informed via 10-minute load statistics of the IEA-10MW-RWT subject to both aero- and hydrodynamic loads, simulated with OpenFAST. Its performance is assessed against the standard Gaussian process regression. The predicted mean is similar in both models, but the heteroscedastic surrogate approximates the large-scale variance of the responses significantly better.Comment: 10 pages. To be published in the IOP Journal of Physics: Conference Series. To be presented at TORQUE 202

Unified Behavior Framework in an Embedded Robot Controller

Robots of varying autonomy have been used to take the place of humans in dangerous tasks. While robots are considered more expendable than human beings, they are complex to develop and expensive to replace if lost. Recent technological advances produce small, inexpensive hardware platforms that are powerful enough to match robots from just a few years ago. There are many types of autonomous control architecture that can be used to control these hardware platforms. One in particular, the Unified Behavior Framework, is a flexible, responsive control architecture that is designed to simplify the control system’s design process through behavior module reuse, and provides a means to speed software development. However, it has not been applied on embedded systems in robots. This thesis presents a development of the Unified Behavior Framework on the Mini-WHEGS™, a biologically inspired, embedded robotic platform. The Mini-WHEGS™ is a small robot that utilize wheel- legs to emulate cockroach walking patterns. Wheel-legs combine wheels and legs for high mobility without the complex control system required for legs. A color camera and a rotary encoder completes the robot, enabling the Mini-WHEGS™ to identify color objects and track its position. A hardware abstraction layer designed for the Mini-WHEGS™ in this configuration decouples the control system from the hardware and provide the interface between the software and the hardware. The result is a highly mobile embedded robot system capable of exchanging behavior modules with much larger robots while requiring little or no change to the modules

Enhanced pre-clinical assessment of total knee replacement using computational modelling with experimental corroboration & probabilistic applications

Demand for Total Knee Replacement (TKR) surgery is high and rising; not just in numbers of procedures, but in the diversity of patient demographics and increase of expectations. Accordingly, greater efforts are being invested into the pre-clinical analysis of TKR designs, to improve their performance in-vivo. A wide range of experimental and computational methods are used to analyse TKR performance pre-clinically. However, direct validation of these methods and models is invariably limited by the restrictions and challenges of clinical assessment, and confounded by the high variability of results seen in-vivo.Consequently, the need exists to achieve greater synergy between different pre-clinical analysis methods. By demonstrating robust corroboration between in-silico and in-vitro testing, and both identifying & quantifying the key sources of uncertainty, greater confidence can be placed in these assessment tools. This thesis charts the development of a new generation of fast computational models for TKR test platforms, with closer collaboration with in-vitro test experts (and consequently more rigorous corroboration with experimental methods) than previously.Beginning with basic tibiofemoral simulations, the complexity of the models was progressively increased, to include in-silico wear prediction, patellofemoral & full lower limb models, rig controller-emulation, and accurate system dynamics. At each stage, the models were compared extensively with data from the literature and experimental tests results generated specifically for corroboration purposes.It is demonstrated that when used in conjunction with, and complementary to, the corresponding experimental work, these higher-integrity in-silico platforms can greatly enrich the range and quality of pre-clinical data available for decision-making in the design process, as well as understanding of the experimental platform dynamics. Further, these models are employed within a probabilistic framework to provide a statistically-quantified assessment of the input factors most influential to variability in the mechanical outcomes of TKR testing. This gives designers a much richer holistic visibility of the true system behaviour than extant 'deterministic' simulation approaches (both computational and experimental).By demonstrating the value of better corroboration and the benefit of stochastic approaches, the methods used here lay the groundwork for future advances in pre-clinical assessment of TKR. These fast, inexpensive models can complement existing approaches, and augment the information available for making better design decisions prior to clinical trials, accelerating the design process, and ultimately leading to improved TKR delivery in-vivo to meet future demands

Autonomous Visual Servo Robotic Capture of Non-cooperative Target

This doctoral research develops and validates experimentally a vision-based control scheme for the autonomous capture of a non-cooperative target by robotic manipulators for active space debris removal and on-orbit servicing. It is focused on the final capture stage by robotic manipulators after the orbital rendezvous and proximity maneuver being completed. Two challenges have been identified and investigated in this stage: the dynamic estimation of the non-cooperative target and the autonomous visual servo robotic control. First, an integrated algorithm of photogrammetry and extended Kalman filter is proposed for the dynamic estimation of the non-cooperative target because it is unknown in advance. To improve the stability and precision of the algorithm, the extended Kalman filter is enhanced by dynamically correcting the distribution of the process noise of the filter. Second, the concept of incremental kinematic control is proposed to avoid the multiple solutions in solving the inverse kinematics of robotic manipulators. The proposed target motion estimation and visual servo control algorithms are validated experimentally by a custom built visual servo manipulator-target system. Electronic hardware for the robotic manipulator and computer software for the visual servo are custom designed and developed. The experimental results demonstrate the effectiveness and advantages of the proposed vision-based robotic control for the autonomous capture of a non-cooperative target. Furthermore, a preliminary study is conducted for future extension of the robotic control with consideration of flexible joints