Thesis (M.Eng.)--Memorial University of Newfoundland, 2000. / Bibliography: p. 132-135.

In this thesis, performance of relay-assisted Device-to-device (D2D) communication is investigated where D2D traffic is carried through relay nodes. I develop resource management schemes to maximize end-to-end rate as well as conversing rate requirements for cellular and D2D UEs under total power constraint. I also develop a low-complexity distributed solution using the concept of message passing. Considering the uncertainties in wireless links (e.g., when interference from other relay nodes and the link gains are not exactly known), I extend the formulation using robust resource allocation techniques. In addition, a distributed solution approach using stable matching is developed to allocate radio resources in an efficient and computationally inexpensive way under the bounded channel uncertainties. Numerical results show that, there is a distance threshold beyond which relay-assisted D2D communication significantly improves network performance at the cost of small increase in end-to-end delay when compared to conventional approach.

Significant research has been initiated recently to devise control strategies that could predict and compensate manufacturing errors using so called explicit Stream-of-Variation(SoV) models that relate process parameters in a Multistage Manufacturing Process (MMP) with product quality. This doctoral dissertation addresses several important scientific and engineering problems that will significantly advance the model-based, active control of quality in MMPs. First, we will formally introduce and study the new concept of compensability in MMPs, analogous to the concept of controllability in the traditional control theory. The compensability in an MMP is introduced as the property denoting one’s ability to compensate the errors in quality characteristics of the workpiece, given the allocation and character of measurements and controllable tooling. The notions of “within-station” and “between-station” compensability are also introduced to describe the ability to compensate upstream product errors within a given operation or between arbitrarily selected operations, respectively. The previous research also failed to concurrently utilize the historical and on-line measurements of product key characteristics for active model-based quality control. This dissertation will explore the possibilities of merging the well-known Run-to-Run (RtR) quality control methods with the model-based feed-forward process control methods. The novel method is applied to the problem of control of multi-layer overlay errors in lithography processes in semiconductor manufacturing. In this work, we first devised a multi-layer overlay model to describe the introduction and flow of overlay errors from one layer to the next, which was then used to pursue a unified approach to RtR and feedforward compensation of overlay errors in the wafer. At last, we extended the existing methodologies by considering inaccurately indentified noise characteristics in the underlying error flow model. This is also a very common situation, since noise characteristics are rarely known with absolute accuracy. We formulated the uncertainty in process noise characteristics using Linear Fractional Transformation (LFT) representation and solved the problem by deriving a robust control law that guaranties the product quality even under the worst case scenario of parametric uncertainties. Theoretical results have been evaluated and demonstrated using a linear state-space model of an actual industrial process for automotive cylinder head machining. / text

In this thesis, we develop an algorithm for detecting independent motion in real-time from 2D image sequences of arbitrarily complex 3D scenes. We discuss the necessary background information in image formation, optical flow, multiple view geometry, robust estimation, and real-time camera and scene pose estimation for constructing and understanding the operation of our algorithm. Furthermore, we provide an overview of existing independent motion detection techniques and compare them to our proposed solution. Unfortunately, the existing independent motion detection techniques were not evaluated quantitatively nor were their source code made publicly available. Therefore, it is not possible to make direct comparisons. Instead, we constructed several comparison algorithms which should have comparable performance to these previous approaches. We developed methods for quantitatively comparing independent motion detection algorithms and found that our solution had the best performance. By establishing a method for quantitatively evaluating these algorithms and publishing our results, we hope to foster better research in this area and help future investigators more quickly advance the state of the art. / text

This dissertation studies hybrid dc-dc power conversion systems based on multiple-input converters (MICs), or more generally, multiport converters. MICs allow for the integration of multiple distributed generation sources and loads. Thanks to the modular design, an MIC yields a scalable system with independent control in all sources. Additional characteristics of MICs include the improved reliability and reduced cost. This dissertation mainly studies three issues of MICs: efficiency improvement, modeling, and control. First, this work develops a cost-effective design of a highly-efficient non-isolated MIC without additional components. Time-multiplexing (TM) MICs, which are driven by a time-multiplexing switching control scheme, contain forward-conducting-bidirectional-blocking (FCBB) switches. TM-MICs are considered to be subject to low efficiency because of high power loss introduced by FCBB switches. In order to reduce the power loss in FCBB switches, this work adopts a modified realization of the FCBB switch and proposes a novel switching control strategy. The design and experimental verifications are motivated through a multiple-input (MI) SEPIC converter. Through the design modifications, the switching transients are improved (comparing to the switching transients in a conventional MI-SEPIC) and the power loss is significantly reduced. Moreover, this design maintains a low parts-count because of the absence of additional components. Experimental results show that for output power ranging from 1 W to 220 W, the modified MIC presents high efficiency (96 % optimally). The design can be readily extended to a general n-input SEPIC. The same modifications can be applied to an MI-Ćuk converter. Second, this dissertation examines the modeling of TM-MICs. In the dynamic equations of a TM-MIC, a state variable from one input leg is possible to be affected by state variables and switching functions associated with other input legs. In this way, inputs are coupled both topologically and in terms of control actions through switching functions. Coupling among the state variable and the time-multiplexing switching functions complicate TM-MICs’ behavior. Consequently, substantial modeling errors may occur when a classical averaging approach is used to model an MIC even with moderately high switching frequencies or small ripples. The errors may increase with incremental number of input legs. In addition to demonstrating the special features on MIC modeling, this dissertation uses the generalized averaging approach to generate a more accurate model, which is also used to derive a small-signal model. The proposed model is an important tool that yields better results when analyzing power budgeting, performing large-signal simulations, and designing controllers for TM-MICs via a more precise representation than classical averaging methods. Analyses are supported by simulations and experimental results. Third, this dissertation studies application of a decentralized controller on an MI-SEPIC. For an MIC, a multiple-input-multiple-output (MIMO) state-space representation can be derived by an averaging method. Based on the averaged MIMO model, an MIMO small-signal model can be generated. Both conventional method and modern multivariable frequency analysis are applied to the small-signal model of an MI-SEPIC to evaluate open-loop and closed-loop characteristics. In addition to verifying the nominal stability and nominal performance, this work evaluates robust stability and robust performance with the structured singular value. The robust performance test shows that a compromised performance may be expected under the decentralized control. Simulations and experimental results verify the theoretical analysis on stability and demonstrate that the decentralized PI controller could be effective to regulate the output of an MIC under uncertainties. Finally, this work studies the control of the MIMO dc-dc converter serving as an active distribution node in an intelligent dc distribution grid. The unified model of a MIMO converter is derived, enabling a systematical analysis and control design that allows this converter to control power flow in all its ports and to act as a power buffer that compensates for mismatches between power generation and consumption. Based on the derived high-order multivariable model, a robust controller is designed with disturbance-attenuation and pole-placement constraints via the linear matrix inequality (LMI) synthesis. The closed-loop robust stability and robust performance are tested through the structured singular value synthesis. Again, the desirable stability and performance are verified by simulations and experimental results. / text

Many of the first robots ever built, decades even before the first industrial robots, were humanoids. It seems like researchers have always sought to imitate the human form with their robots, and with good reason. Humans are incredibly flexible; they can perform a huge variety of tasks, from locomotion over rough terrain, to delicate assembly, to heavy lifting. A human’s second arm allows him to lift twice as much weight. His workspace is approximately doubled, and he can perform a broader variety of tasks as items are passed back and forth between hands. We sought to impart some of that same functionality to a strong, rigid, dual-arm robot. Specifically, we developed a control law that allows two robot arms to lift and manipulate an object in cooperation. As opposed to the prior art, our control law is tailored for industrial robots. These robots do not usually allow torque control and their control frequency is generally 60 Hz. Through the use of fuzzy logic, the control law is quite robust at 60 Hz control rates. Its simple structure reduces the computational cost of the algorithm by approximately 75% over Jacobian-based methods. Stability is proven and the controller parameters can be adjusted to handle perturbances of arbitrary magnitude. Since the robots behave as an admittance, torque control is not required. Several experiments were conducted to benchmark and validate the performance of this control law. The controller is able to maintain a clamp force within ± 4N despite a wide variation in trajectory and control frequency. This fine level of force control makes the controller suitable for delicate tasks. The conclusion suggests several extensions that would make this control law more useful. For example, adaptive control would improve the performance. A position feedback controller should be cascaded so that the robot arms’ tracking accuracy is improved. Many tasks (such as co-robotics) require external compliance, and we show how external compliance could easily be incorporated. / text

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The performance of a manufacturing system relies on its four types of elements: operators, machines, computer system and material handling system. To ensure the performance of these elements, operational data containing various aspects of information are collected for monitoring and analysis. This dissertation focuses on the operator performance evaluation and machine failure prediction. The proposed research work is motivated by the following challenges in analyzing operational data. (i) the complex relationship between the variables, (ii) the implicit information important to failure prediction, and (iii) data with outliers, missing and erroneous measurements. To overcome these challenges, the following research has been conducted. To compare operator performance, a methodology combining regression modeling and multiple comparisons technique is proposed. The regression model quantifies and removes the complex effects of other impacting factors on the operator performance. A robust zero-inflated Poisson (ZIP) model is developed to reduce the impacts of the excessive zeros and outliers in the performance metric, i.e. the number of defects (NoD), on regression analysis. The model residuals are plotted in non-parametric statistical charts for performance comparison. The estimated model coefficients are also used to identify under-performing machines. To detect temporal patterns from operational data sequence, an algorithm is proposed for detecting interval-based asynchronous periodic patterns (APP). The algorithm effectively and efficiently detects pattern through a modified clustering and a convolution-based template matching method. To predict machine failures based on the covariates with erroneous measurements, a new method is proposed for statistical inference of proportional hazard model under a mixture of classical and Berkson errors. The method estimates the model coefficients with an expectation-maximization (EM) algorithm with expectation step achieved by Monte Carlo simulation. The model estimated with the proposed method will improve the accuracy of the inference on machine failure probability. The research work presented in this dissertation provides a package of solutions to improve manufacturing system performance. The effectiveness and efficiency of the proposed methodologies have been demonstrated and justified with both numerical simulations and real-world case studies.

Stochastic programming is a mathematical technique for decision making under uncertainty using probabilistic statements in the problem objective and constraints. In practice, the distribution of the unknown quantities are often known only through observed or simulated data. This dissertation discusses several methods of using this data to formulate, solve, and evaluate the quality of solutions of stochastic programs. The central contribution of this dissertation is to investigate the use of techniques from simulation and statistics to enable data-driven models and methods for stochastic programming. We begin by extending the method of overlapping batches from simulation to assessing solution quality in stochastic programming. The Multiple Replications Procedure, where multiple stochastic programs are solved using independent batches of samples, has previously been used for assessing solution quality. The Overlapping Multiple Replications Procedure overlaps the batches, thus losing the independence between samples, but reducing the variance of the estimator without affecting its bias. We provide conditions under which the optimality gap estimators are consistent, the variance reduction benefits are obtained, and give a computational illustration of the small-sample behavior. Our second result explores the use of phi-divergences for distributionally robust optimization, also known as ambiguous stochastic programming. The phi-divergences provide a method of measuring distance between probability distributions, are widely used in statistical inference and information theory, and have recently been proposed to formulate data-driven stochastic programs. We provide a novel classification of phi-divergences for stochastic programming and give recommendations for their use. A value of data condition is derived and the asymptotic behavior of the phi-divergence constrained stochastic program is described. Then a decomposition-based solution method is proposed to solve problems computationally. The final portion of this dissertation applies the phi-divergence method to a problem of water allocation in a developing region of Tucson, AZ. In this application, we integrate several sources of uncertainty into a single model, including (1) future population growth in the region, (2) amount of water available from the Colorado River, and (3) the effects of climate variability on water demand. Estimates of the frequency and severity of future water shortages are given and we evaluate the effectiveness of several infrastructure options.