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.

Investigation into the impact of grain boundaries, film interface, and crystallographic orientation on the ionic conductivity of thin film gadolinium-doped ceria

The research reported in this dissertation investigates the impact of grain boundaries, film interface, and crystallographic orientation on the ionic conductivity of thin film Gd-doped CeO2 (GDC). Chapter 2 of this work addresses claims in the literature that submicron grain boundaries have the potential to dramatically increase the ionic conductivity of GDC films. Unambiguous testing of this claim requires directly comparing the ionic conductivity of single-crystal GDC films to films that are identical except for the presence of submicron grain boundaries. In this work techniques have been developed to grow GDC films by RF magnetron sputtering from a GDC target on single crystal r plane sapphire substrates. These techniques allow the growth of films that are single crystals or polycrystalline with 80 nm diameter grains. The ionic conductivities of these films have been measured and the data shows that the ionic conductivity of single crystal GDC is greater than that of the polycrystalline films by more than a factor of 4 over the 400-700°C temperature range. Chapter 3 of this work investigates the ionic conductivity of surface and interface regions of thin film Gd-doped CeO2. In this study, single crystal GDC films have been grown to thicknesses varying from 20 to 500 nm and their conductivities have been measured in the 500-700°C temperature range. Decreasing conductivity with decreasing film thickness was observed. Analysis of the conductivity data is consistent with the presence of an approximately 50 nm layer of less conductive material in every film. This study concludes that the surface and interface regions of thin film GDC are less conductive than the bulk single crystal regions, rather than being highly conductive paths. Chapter 4 of this work investigates the ionic conductivity of thin film Gd-doped CeO2 (GDC) as a function of crystallographic orientation. A theoretical expression has been developed for the ionic conductivity of the [100] and [110] directions in single crystal GDC. This relationship is compared to experimental data collected from a single crystal GDC film. The film was grown to a thickness of _300 nm and its conductivity measured along the [100] and [110] orientations in the 500-700°C temperature range. The experimental data shows no statistically significant difference in the conductivities of the [100] and [110] directions in single crystal GDC. This result agrees with the theoretical model which predicts no difference between the conductivities of the two directions.

Space trajectories optimization using variable-chromosome-length genetic algorithms

The problem of optimal design of a multi-gravity-assist space trajectories, with free number of deep space maneuvers (MGADSM) poses multi-modal cost functions. In the general form of the problem, the number of design variables is solution dependent. To handle global optimization problems where the number of design variables varies from one solution to another, two novel genetic-based techniques are introduced: hidden genes genetic algorithm (HGGA) and dynamic-size multiple population genetic algorithm (DSMPGA). In HGGA, a fixed length for the design variables is assigned for all solutions. Independent variables of each solution are divided into effective and ineffective (hidden) genes. Hidden genes are excluded in cost function evaluations. Full-length solutions undergo standard genetic operations. In DSMPGA, sub-populations of fixed size design spaces are randomly initialized. Standard genetic operations are carried out for a stage of generations. A new population is then created by reproduction from all members based on their relative fitness. The resulting sub-populations have different sizes from their initial sizes. The process repeats, leading to increasing the size of sub-populations of more fit solutions. Both techniques are applied to several MGADSM problems. They have the capability to determine the number of swing-bys, the planets to swing by, launch and arrival dates, and the number of deep space maneuvers as well as their locations, magnitudes, and directions in an optimal sense. The results show that solutions obtained using the developed tools match known solutions for complex case studies. The HGGA is also used to obtain the asteroids sequence and the mission structure in the global trajectory optimization competition (GTOC) problem. As an application of GA optimization to Earth orbits, the problem of visiting a set of ground sites within a constrained time frame is solved. The J2 perturbation and zonal coverage are considered to design repeated Sun-synchronous orbits. Finally, a new set of orbits, the repeated shadow track orbits (RSTO), is introduced. The orbit parameters are optimized such that the shadow of a spacecraft on the Earth visits the same locations periodically every desired number of days.

Nano-engineering of composite material via reactive mechanical alloying/milling (RMA/M)

Attempts to strengthen a chromium-modified titanium trialuminide by a combination of grain size refinement and dispersoid strengthening led to a new means to synthesize such materials. This Reactive Mechanical Alloying/Milling process uses in situ reactions between the metallic powders and elements from a process control agent and/or a gaseous environment to assemble a dispersed small hard particle phase within the matrix by a bottom-up approach. In the current research milled powders of the trialuminide alloy along with titanium carbide were produced. The amount of the carbide can be varied widely with simple processing changes and in this case the milling process created trialuminide grain sizes and carbide particles that are the smallest known from such a process. Characterization of these materials required the development of x-ray diffraction means to determine particle sizes by deconvoluting and synthesizing components of the complex multiphase diffraction patterns and to carry out whole pattern analysis to analyze the diffuse scattering that developed from larger than usual highly defective grain boundary regions. These identified regions provide an important mass transport capability in the processing and not only facilitate the alloy development, but add to the understanding of the mechanical alloying process. Consolidation of the milled powder that consisted of small crystallites of the alloy and dispersed carbide particles two nanometers in size formed a unique, somewhat coarsened, microstructure producing an ultra-high strength solid material composed of the chromium-modified titanium trialuminide alloy matrix with small platelets of the complex carbides Ti2AlC and Ti3AlC2. This synthesis process provides the unique ability to nano-engineer a wide variety of composite materials, or special alloys, and has shown the ability to be extended to a wide variety of metallic materials.