Dynamic modeling, simulation and control design of a parafoil-payload system for ship launched aerial delivery system (SLADS)

The objective of this research was to develop a high-fidelity dynamic model of a parafoilpayload system with respect to its application for the Ship Launched Aerial Delivery System (SLADS). SLADS is a concept in which cargo can be transfered from ship to shore using a parafoil-payload system. It is accomplished in two phases: An initial towing phase when the glider follows the towing vessel in a passive lift mode and an autonomous gliding phase when the system is guided to the desired point. While many previous researchers have analyzed the parafoil-payload system when it is released from another airborne vehicle, limited work has been done in the area of towing up the system from ground or sea. One of the main contributions of this research was the development of a nonlinear dynamic model of a towed parafoil-payload system. After performing an extensive literature review of the existing methods of modeling a parafoil-payload system, a five degree-of-freedom model was developed. The inertial and geometric properties of the system were investigated to predict accurate results in the simulation environment. Since extensive research has been done in determining the aerodynamic characteristics of a paraglider, an existing aerodynamic model was chosen to incorporate the effects of air flow around the flexible paraglider wing. During the towing phase, it is essential that the parafoil-payload system follow the line of the towing vessel path to prevent an unstable flight condition called ‘lockout’. A detailed study of the causes of lockout, its mathematical representation and the flight conditions and the parameters related to lockout, constitute another contribution of this work. A linearized model of the parafoil-payload system was developed and used to analyze the stability of the system about equilibrium conditions. The relationship between the control surface inputs and the stability was investigated. In addition to stability of flight, one more important objective of SLADS is to tow up the parafoil-payload system as fast as possible. The tension in the tow cable is directly proportional to the rate of ascent of the parafoil-payload system. Lockout instability is more favorable when tow tensions are large. Thus there is a tradeoff between susceptibility to lockout and rapid deployment. Control strategies were also developed for optimal tow up and to maintain stability in the event of disturbances.

Control design and genetic algorithm optimization for electrostatic MEMS

This dissertation discusses structural-electrostatic modeling techniques, genetic algorithm based optimization and control design for electrostatic micro devices. First, an alternative modeling technique, the interpolated force model, for electrostatic micro devices is discussed. The method provides improved computational efficiency relative to a benchmark model, as well as improved accuracy for irregular electrode configurations relative to a common approximate model, the parallel plate approximation model. For the configuration most similar to two parallel plates, expected to be the best case scenario for the approximate model, both the parallel plate approximation model and the interpolated force model maintained less than 2.2% error in static deflection compared to the benchmark model. For the configuration expected to be the worst case scenario for the parallel plate approximation model, the interpolated force model maintained less than 2.9% error in static deflection while the parallel plate approximation model is incapable of handling the configuration. Second, genetic algorithm based optimization is shown to improve the design of an electrostatic micro sensor. The design space is enlarged from published design spaces to include the configuration of both sensing and actuation electrodes, material distribution, actuation voltage and other geometric dimensions. For a small population, the design was improved by approximately a factor of 6 over 15 generations to a fitness value of 3.2 fF. For a larger population seeded with the best configurations of the previous optimization, the design was improved by another 7% in 5 generations to a fitness value of 3.0 fF. Third, a learning control algorithm is presented that reduces the closing time of a radiofrequency microelectromechanical systems switch by minimizing bounce while maintaining robustness to fabrication variability. Electrostatic actuation of the plate causes pull-in with high impact velocities, which are difficult to control due to parameter variations from part to part. A single degree-of-freedom model was utilized to design a learning control algorithm that shapes the actuation voltage based on the open/closed state of the switch. Experiments on 3 test switches show that after 5-10 iterations, the learning algorithm lands the switch with an impact velocity not exceeding 0.2 m/s, eliminating bounce.

SVPWM SWITCHING PATTERN FOR Z-SOURCE INVERTER, SIMULATION AND APPLICATION

The voltage source inverter (VSI) and current voltage source inverter (CSI) are widely used in industrial application. But the traditional VSIs and CSIs have one common problem: can’t boost or buck the voltage come from battery, which make them impossible to be used alone in Hybrid Electric Vehicle (HEV/EV) motor drive application, other issue is the traditional inverter need to add the dead-band time into the control sequence, but it will cause the output waveform distortion. This report presents an impedance source (Z-source network) topology to overcome these problems, it can use one stage instead of two stages (VSI or CSI + boost converter) to buck/boost the voltage come from battery in inverter system. Therefore, the Z-source topology hardware design can reduce switching element, entire system size and weight, minimize the system cost and increase the system efficiency. Also, a modified space vector pulse-width modulation (SVPWM) control method has been selected with the Z-source network together to achieve the best efficiency and lower total harmonic distortion (THD) at different modulation indexes. Finally, the Z-source inverter controlling will modulate under two control sequences: sinusoidal pulse width modulation (SPWM) and SVPWM, and their output voltage, ripple and THD will be compared.

DATA DRIVEN INTELLIGENT AGENT NETWORKS FOR ADAPTIVE MONITORING AND CONTROL

To analyze the characteristics and predict the dynamic behaviors of complex systems over time, comprehensive research to enable the development of systems that can intelligently adapt to the evolving conditions and infer new knowledge with algorithms that are not predesigned is crucially needed. This dissertation research studies the integration of the techniques and methodologies resulted from the fields of pattern recognition, intelligent agents, artificial immune systems, and distributed computing platforms, to create technologies that can more accurately describe and control the dynamics of real-world complex systems. The need for such technologies is emerging in manufacturing, transportation, hazard mitigation, weather and climate prediction, homeland security, and emergency response. Motivated by the ability of mobile agents to dynamically incorporate additional computational and control algorithms into executing applications, mobile agent technology is employed in this research for the adaptive sensing and monitoring in a wireless sensor network. Mobile agents are software components that can travel from one computing platform to another in a network and carry programs and data states that are needed for performing the assigned tasks. To support the generation, migration, communication, and management of mobile monitoring agents, an embeddable mobile agent system (Mobile-C) is integrated with sensor nodes. Mobile monitoring agents visit distributed sensor nodes, read real-time sensor data, and perform anomaly detection using the equipped pattern recognition algorithms. The optimal control of agents is achieved by mimicking the adaptive immune response and the application of multi-objective optimization algorithms. The mobile agent approach provides potential to reduce the communication load and energy consumption in monitoring networks. The major research work of this dissertation project includes: (1) studying effective feature extraction methods for time series measurement data; (2) investigating the impact of the feature extraction methods and dissimilarity measures on the performance of pattern recognition; (3) researching the effects of environmental factors on the performance of pattern recognition; (4) integrating an embeddable mobile agent system with wireless sensor nodes; (5) optimizing agent generation and distribution using artificial immune system concept and multi-objective algorithms; (6) applying mobile agent technology and pattern recognition algorithms for adaptive structural health monitoring and driving cycle pattern recognition; (7) developing a web-based monitoring network to enable the visualization and analysis of real-time sensor data remotely. Techniques and algorithms developed in this dissertation project will contribute to research advances in networked distributed systems operating under changing environments.

DEVELOPMENT OF AN OPTIMIZATION MODEL FOR BIOFUEL FACILITY SIZE AND LOCATION AND A SIMULATION MODEL FOR DESIGN OF A BIOFUEL SUPPLY CHAIN

A range of societal issues have been caused by fossil fuel consumption in the transportation sector in the United States (U.S.), including health related air pollution, climate change, the dependence on imported oil, and other oil related national security concerns. Biofuels production from various lignocellulosic biomass types such as wood, forest residues, and agriculture residues have the potential to replace a substantial portion of the total fossil fuel consumption. This research focuses on locating biofuel facilities and designing the biofuel supply chain to minimize the overall cost. For this purpose an integrated methodology was proposed by combining the GIS technology with simulation and optimization modeling methods. The GIS based methodology was used as a precursor for selecting biofuel facility locations by employing a series of decision factors. The resulted candidate sites for biofuel production served as inputs for simulation and optimization modeling. As a precursor to simulation or optimization modeling, the GIS-based methodology was used to preselect potential biofuel facility locations for biofuel production from forest biomass. Candidate locations were selected based on a set of evaluation criteria, including: county boundaries, a railroad transportation network, a state/federal road transportation network, water body (rivers, lakes, etc.) dispersion, city and village dispersion, a population census, biomass production, and no co-location with co-fired power plants. The simulation and optimization models were built around key supply activities including biomass harvesting/forwarding, transportation and storage. The built onsite storage served for spring breakup period where road restrictions were in place and truck transportation on certain roads was limited. Both models were evaluated using multiple performance indicators, including cost (consisting of the delivered feedstock cost, and inventory holding cost), energy consumption, and GHG emissions. The impact of energy consumption and GHG emissions were expressed in monetary terms to keep consistent with cost. Compared with the optimization model, the simulation model represents a more dynamic look at a 20-year operation by considering the impacts associated with building inventory at the biorefinery to address the limited availability of biomass feedstock during the spring breakup period. The number of trucks required per day was estimated and the inventory level all year around was tracked. Through the exchange of information across different procedures (harvesting, transportation, and biomass feedstock processing procedures), a smooth flow of biomass from harvesting areas to a biofuel facility was implemented. The optimization model was developed to address issues related to locating multiple biofuel facilities simultaneously. The size of the potential biofuel facility is set up with an upper bound of 50 MGY and a lower bound of 30 MGY. The optimization model is a static, Mathematical Programming Language (MPL)-based application which allows for sensitivity analysis by changing inputs to evaluate different scenarios. It was found that annual biofuel demand and biomass availability impacts the optimal results of biofuel facility locations and sizes.

EXPERIMENTAL AND COMPUTATIONAL STUDIES OF ELECTROMAGNETIC CLOAKING AT MICROWAVES

An invisibility cloak is a device that can hide the target by enclosing it from the incident radiation. This intriguing device has attracted a lot of attention since it was first implemented at a microwave frequency in 2006. However, the problems of existing cloak designs prevent them from being widely applied in practice. In this dissertation, we try to remove or alleviate the three constraints for practical applications imposed by loosy cloaking media, high implementation complexity, and small size of hidden objects compared to the incident wavelength. To facilitate cloaking design and experimental characterization, several devices and relevant techniques for measuring the complex permittivity of dielectric materials at microwave frequencies are developed. In particular, a unique parallel plate waveguide chamber has been set up to automatically map the electromagnetic (EM) field distribution for wave propagation through the resonator arrays and cloaking structures. The total scattering cross section of the cloaking structures was derived based on the measured scattering field by using this apparatus. To overcome the adverse effects of lossy cloaking media, microwave cloaks composed of identical dielectric resonators made of low loss ceramic materials are designed and implemented. The effective permeability dispersion was provided by tailoring dielectric resonator filling fractions. The cloak performances had been verified by full-wave simulation of true multi-resonator structures and experimental measurements of the fabricated prototypes. With the aim to reduce the implementation complexity caused by metamaterials employment for cloaking, we proposed to design 2-D cylindrical cloaks and 3-D spherical cloaks by using multi-layer ordinary dielectric material (εr>1) coating. Genetic algorithm was employed to optimize the dielectric profiles of the cloaking shells to provide the minimum scattering cross sections of the cloaked targets. The designed cloaks can be easily scaled to various operating frequencies. The simulation results show that the multi-layer cylindrical cloak essentially outperforms the similarly sized metamaterials-based cloak designed by using the transformation optics-based reduced parameters. For the designed spherical cloak, the simulated scattering pattern shows that the total scattering cross section is greatly reduced. In addition, the scattering in specific directions could be significantly reduced. It is shown that the cloaking efficiency for larger targets could be improved by employing lossy materials in the shell. At last, we propose to hide a target inside a waveguide structure filled with only epsilon near zero materials, which are easy to implement in practice. The cloaking efficiency of this method, which was found to increase for large targets, has been confirmed both theoretically and by simulations.