The resilience of manufacturing enterprises is an important research topic, since disruptions have severe effects on the normal operation of manufacturing enterprises, especially as manufacturing supply chains become global. Although many case studies have been carried out to address resilience in organizations, a systematic method to model and analyze the resilience dynamics in manufacturing enterprises is not well developed. This study is intended to conduct research on quantitative analysis and control for resilience. After reviewing the literature addressing resilience, a modeling framework is presented to characterize the resilience of a manufacturing enterprise responding to disruptive events, which includes inventory ow between enterprise nodes, different costs, resource, demand, etc. Each node within the network is represented as a dynamic model with associated costs of production and inventory. This mathematical model is the foundation of quantitative analysis and control. With this model, an optimal control problem is formulated, by which the control can be solved to achieve minimum cost. Several different types of systems are defined and analyzed in this work. We develop the approach of aggregation to simplify the network structures. The study is mainly focused on two categories of network systems: serial network systems and assembly tree network systems. The analysis on these two categories covers two conditions: in discrete time domain without considering capacities, and in continuous time domain with considering capacities. The methods to determining optimal operations are developed under different conditions. In the serial network systems analysis, a practical case study is introduced to show the corresponding method developed. Finally, the problems are discussed for future research. Based on the results of these analyses, we present optimal control policies for resilience. Our method can support the analysis of the impact of disruptions, and the development of control strategies that reduce the impact of the disruption.

Energy consumption has increased drastically during the last century. Currently, the worldwide energy consumption is about 17.4 TW and is predicted to reach 25 TW by 2035. Solar energy has emerged as one of the potential renewable energy sources. Since its first physical recognition in 1887 by Adams and Day till nowadays, research in solar energy is continuously developing. This has lead to many achievements and milestones that introduced it as one of the most reliable and sustainable energy sources. Recently, the International Energy Agency declared that solar energy is predicted to be one of the major electricity production energy sources by 2035. Enhancing the efficiency and lifecycle of photovoltaic (PV) modules leads to significant cost reduction. Reducing the temperature of the PV module improves its efficiency and enhances its lifecycle. To better understand the PV module performance, it is important to study the interaction between the output power and the temperature. A model that is capable of predicting the PV module temperature and its effects on the output power considering the individual contribution of the solar spectrum wavelengths significantly advances the PV module designs toward higher efficiency. In this work, a thermoelectrical model is developed to predict the effects of the solar spectrum wavelengths on the PV module performance. The model is characterized and validated under real meteorological conditions where experimental temperature and output power of the PV module measurements are shown to agree with the predicted results. The model is used to validate the concept of active optical filtering. Since this model is wavelength-based, it is used to design an active optical filter for PV applications. Applying this filter to the PV module is expected to increase the output power of the module by filtering the spectrum wavelengths. The active filter performance is optimized, where different cutoff wavelengths are used to maximize the module output power. It is predicted that if the optimized active optical filter is applied to the PV module, the module efficiency is predicted to increase by about 1%. Different technologies are considered for physical implementation of the active optical filter.

In manual control, the welding gun’s moving speed can significantly influence the welding results and critical welding operations usually require welders to concentrate consistently in order to react rapidly and accurately. However, human welders always have some habitual action which can have some subtle influence the welding process. It takes countless hours to train an experienced human welder. Using vision and IMU sensor will be able to set up a system and allow the worker got more accurate visual feedback like an experienced worker. The problem is that monitor and measuring of the control process not always easy under a complex working environment like welding. In this thesis, a new method is developed that use two different methods to compensate each other to obtain accurate monitoring results. Vision sensor and IMU sensor both developed to obtain the accurate data from the control process in real-time but don’t influence other. Although both vision and IMU sensor has their own limits, they also have their own advantage which can contribute to the measuring system.

A microgrid is a subnetwork of power system that consists of a group of distributed energy sources and loads. It is designed to integrate distributed generation, loads, energy storage devices, converters, and monitoring and protection devices. Generally, a successful microgrid could run both in island mode (off-grid) and in grid-connected mode (on-grid), being able to convert between two modes at any time. With continuous development of the power system, distributed renewable generation unit accounts for an increasing proportion, since microgrid could effectively connect these generation units to the main grid, thereby improving the energy efficiency and the energy structure. Microgrid is increasingly playing an important role in the power system. This thesis focuses on reducing the cost of microgrids through economic operation, including both static and dynamic economic operations. Three cases are tested based on these two methods. Also, each case will include four situations including one without ESS and three situations with 2MWh ESS, 3MWh ESS, 4MWh ESS, respectively.

The US Department of Energy in 2010 has identified several rare earth elements as critical materials to enable clean technologies. As part of ongoing research in REEs (rare earth elements) recovery from coal sources, the University of Kentucky has designed, developed and is demonstrating a ¼ ton/hour pilot-scale processing plant to produce high-grade REEs from coal sources. Due to the need to control critical variables (e.g. pH, tank level, etc.), process control is required. To ensure adequate process control, a study was conducted on leaching and solvent extraction control to evaluate the potential of achieving low-cost REE recovery in addition to developing a process control PLC system. The overall operational design and utilization of Six Sigma methodologies is discussed. Further, the application of the controls design, both procedural and electronic for the control of process variables such as pH is discussed. Variations in output parameters were quantified as a function of time. Data trends show that the mean process variable was maintained within prescribed limits. Future work for the utilization of data analysis and integration for data-based decision-making will be discussed.

Increased government funding and incentives in recent years has led to an increase in the number of grid-tied renewable energy sources as their economic benefits become more renowned. Unfortunately, the outputs of these renewable resources are often highly variable, resulting in undesirable voltage disruptions that are harmful to sensitive loads. In addition to the energy variability of renewable energy sources, random voltage sags, swells and disruptions are already a major issue in power systems. Recent advances in power electronic devices have provided a platform for new solutions to the voltage support problem in power systems. One promising solution is the Dynamic Voltage Regulator (DVR), a series compensating device used to protect a sensitive load that is connected downstream from voltage sag or swell. For this thesis, the design, modeling, and analysis of a DVR system were performed using PSCAD software. Results from simulation demonstrate the DVR’s effectiveness in protecting a sensitive load from load and source side voltage disturbances as well as regulate the load bus voltage to its rated value.

Single stage cycloconverters generally incorporate hard switching at turn on and soft switching at turn off. This hard switching at turn on combined with the slow switching speeds of thyristors (the switch of choice for standard cycloconverters) limits their use to lower frequency applications. This thesis explores the analysis and design of a pulse density modulated (PDM), soft switching cycloconverter. Unlike standard cycloconverters, the controller in this converter does not adjust thyristor firing angles. It lets only complete half cycles of the input waveform through to the output. This allows and requires a much greater frequency step down from the input to the output. The advantages, shortcomings and tradeoffs of this topology are explored as this converter is designed, built and tested. The resulting cycloconverter has many deficiencies, but proves the concept of the PDM soft switching technique. Cases for further improvement and study are outlined. In the end, this converter shows much promise for applications requiring a high step down in frequency, as well as where the lower electromagnetic interference (EMI) of soft switching may be beneficial.

Teleoperation of ground/aerial vehicle extends operator's ability (e.g. expertise, strength, mobility) into the remote environment, and haptic feedback enhances the human operator's perception of the slave environment. In my thesis, two cases are studied: wheeled mobile robot (MWR) haptic tele-driving over the Internet and unmanned aerial vehicle (UAV) haptic teleoperation over the Internet. We propose novel control frameworks for both dynamic WMR and kinematic WMR in various tele-driving modes, and for a "mixed" UAV with translational dynamics and attitude kinematics. The recently proposed passive set-position modulation (PSPM) framework is extended to guarantee the passivity and/or stability of the closed-loop system with time-varying/packet-loss in the communication; and proved performance in steady state is shown by theoretical measurements.For UAV teleoperation, we also derive a backstepping trajectory tracking control with robustness analysis. Experimental results for dynamic/kinematic WMR and an indoor quadrotor-type UAV are presented to show the efficacy of the proposed control framework.

Submerged Arc Welding (SAW) for fillet joints is one of the major applications in the shipbuilding industry. Due to the requirement for the weld size, a sufficient amount of metal must be deposited. In conventional SAW process, the heat input is proportional to the amount of metal melted and is thus determined by the required weld size. To meet this requirement, an excessive amount of heat is applied causing large distortions on the welded structures whose follow-up straightening is highly costly. In order to reduce the needed heat input, Double-Electrode (DE) technology has been practiced creating the Double-Electrode SAW (DE-SAW) method for fillet joints. The reduction in the heat input, however, also reduces the penetration capability of the process, and the ability to produce required weld beads has to be compromised. To eliminate the unwanted side effect after using DE-SAW, a root opening between the panel and the tee has been proposed in this dissertation to form a modified fillet joint design. Experimental results verified that the use of root opening improves the ability of DE-SAW to produce the required weld beads at reduced heat input and penetration capability. Unfortunately, the use of root opening decreases the stability of the process significantly. To control the heat input at a minimally necessary level that guarantees the weld size and meanwhile the process stability, a feedback is needed to control the currents at their desired levels. To this end, the fillet DE-SAW process is modeled and a multivariable predictive control algorithm is developed based on the process model. Major parameters including the root opening size, travel speed and heat input level have been selected/optimized/minimized to produce required fillet weld beads with a minimized heat input based on qualitative and quantitative analyses. At the end of this dissertation, a series of experiments validated the feasibility and repeatability of the predictive control based DE-SAW process for fillet joints with root opening.

Combining human welder (with intelligence and sensing versatility) and automated welding robots (with precision and consistency) can lead to next generation intelligent welding systems. In this dissertation intelligent welding robots are developed by process modeling / control method and learning the human welder behavior. Weld penetration and 3D weld pool surface are first accurately controlled for an automated Gas Tungsten Arc Welding (GTAW) machine. Closed-form model predictive control (MPC) algorithm is derived for real-time welding applications. Skilled welder response to 3D weld pool surface by adjusting the welding current is then modeled using Adaptive Neuro-Fuzzy Inference System (ANFIS), and compared to the novice welder. Automated welding experiments confirm the effectiveness of the proposed human response model. A virtualized welding system is then developed that enables transferring the human knowledge into a welding robot. The learning of human welder movement (i.e., welding speed) is first realized with Virtual Reality (VR) enhancement using iterative K-means based local ANFIS modeling. As a separate effort, the learning is performed without VR enhancement utilizing a fuzzy classifier to rank the data and only preserve the high ranking “correct” response. The trained supervised ANFIS model is transferred to the welding robot and the performance of the controller is examined. A fuzzy weighting based data fusion approach to combine multiple machine and human intelligent models is proposed. The data fusion model can outperform individual machine-based control algorithm and welder intelligence-based models (with and without VR enhancement). Finally a data-driven approach is proposed to model human welder adjustments in 3D (including welding speed, arc length, and torch orientations). Teleoperated training experiments are conducted in which a human welder tries to adjust the torch movements in 3D based on his observation on the real-time weld pool image feedback. The data is off-line rated by the welder and a welder rating system is synthesized. ANFIS model is then proposed to correlate the 3D weld pool characteristic parameters and welder’s torch movements. A foundation is thus established to rapidly extract human intelligence and transfer such intelligence into welding robots.