Efficient Control of DC Servomotor Systems Using Backpropagation Neural Networks

DC motor systems have played an important role in the improvement and development of the industrial revolution, making them the heart of different applications beside AC motor systems. Therefore, the development of a more efficient control strategy that can be used for the control of a DC servomotor system, and a well defined mathematical model that can be used for off line simulation are essential for this type of systems Servomotor systems are known to have nonlinear parameters and dynamic factors, such as backlash, dead zone and Coulomb friction that make the systems hard to control using conventional control methods such as PID controllers. Also, the dynamics of the servomotor and outside factors add more complexity to the analysis of the system, for example when the load attached to the control system changes. Due to these parameters and factors new intelligent control techniques such as Neural Networks, genetic algorithms and Fuzzy logic methods are under research consideration in order to solve the complex problems related to the control of these nonlinear systems. In this research we are using a combination of two multilayer neural networks to implement the control system: a) The first network is used to build a model that mimics the function of DC servomotor system, and b) a second network is used to implement the controller that controls the operation of the model network using backpropagation learning technique. The proposed combination of the two neural networks will be able to deal with the nonlinear parameters and dynamic factors involved in the original servomotor system and hence generate the proper control of the output speed and position. Off line simulation using MATLAB Neural Network toolbox is used to show final results, and to compare them with a conventional PID controller results for the same model

Enhancement of the Performance of GaAs based Solar Cells by using Plasmonic, Anti-Reflection Coating and Hydrophobic Effects

Investigation of renewable energy resources is gaining huge momentum in recent years due to the limited fossil fuels, and their detriment impact on the environment. Solar energy is promising to meet the increased energy demand. In order to achieve this goal, solar energy has to be harvested efficiently at low cost. Therefore, higher efficiency solar cells are the primary focus of research worldwide. Photovoltaics based on InAs/GaAs intermediate band solar cells and their device performance enhancements are investigated in this dissertation. The device enhancement is carried out by surface modification methods. The dissertation work is inspired by the need of improved efficiency solar cells to meet the new energy demands. In this project, InAs/GaAs intermediate band solar cell and their device performance enhancement are investigated. The device enhancement is carried out though implementing surface modification by using plasmonic effect, anti-reflection coatings and self-cleaning surfaces. Single junction and quantum dots solar cells performance has been unsatisfying due to several optical losses especially high surface reflection. Hence, in this project, potential application of plasmonic effect and significant device performance enhancement implementing anti-reflection coating are studied experimentally. Significantly, GaAs based photovoltaics solar cells efficiencies were improved by 40 - 50 %. In addition, self-cleaning surfaces with contact angle above 156o has been achieved. This self-cleaning surface can ensure proper functionality of the anti-reflection coatings

Comprehensive design analysis of ZnO anti-reflection nanostructures for Si solar cells

Six different shapes of zinc oxide (ZnO) nanostructured antireflective coatings on Si substrate, namely hemispheres, pyramids, motheye-like, cubes, rods and hexagonal prisms, were modeled numerically using finite element simulation, by solving Maxwell wave equation for periodic nanostructure arrays. COMSOL Multiphysics commercial package was used to perform the simulation by adopting the wave optics module. Geometrical parameters including structure size and period were studied in order to obtain better insight on how light and matter interaction is affected by structure geometry. However, among the studied structure geometries, hemispherical ones have the worst optical performance with a maximum reflectance of 45% for a 20 nm hemisphere radius. Pyramidal, motheye and cubic shapes yielded an intermediate optical performance while rod and hexagonal shapes revealed a minimum reflectance (less than 5%) values over a wide range of wavelengths and also showing interesting interference patterns.This research was fully supported by the Jordanian Royal Hashemite Court under the Royal Initiative for Innovative Projects in Nanotechnology

Preparation and Characterization of Blank and Nerolidol-Loaded Chitosan–Alginate Nanoparticles

Recently, there has been a growing interest in using natural products as treatment alternatives in several diseases. Nerolidol is a natural product which has been shown to have protective effects in several conditions. The low water solubility of nerolidol and many other natural products limits their delivery to the body. In this research, a drug delivery system composed of alginate and chitosan was fabricated and loaded with nerolidol to enhance its water solubility. The chitosan–alginate nanoparticles were fabricated using a new method including the tween 80 pre-gelation, followed by poly-ionic crosslinking between chitosan negative and alginate positive groups. Several characterization techniques were used to validate the fabricated nanoparticles. The molecular interactions between the chitosan, alginate, and nerolidol molecules were confirmed using the Fourier transform infrared spectroscopy. The ultraviolet spectroscopy showed an absorbance peak of the blank nanoparticles at 200 nm and for the pure nerolidol at 280 nm. Using both scanning and transmission electron microscopy, the nanoparticles were found to be spherical in shape with an average size of 12 nm and 35 nm for the blank chitosan–alginate nanoparticles and the nerolidol-loaded chitosan–alginate nanoparticles, respectively. The nanoparticles were also shown to have a loading capacity of 51.7% and an encapsulation efficiency of 87%. A controlled release profile of the loaded drug for up to 28 h using an in vitro model was also observed, which is more efficient than the free form of nerolidol. In conclusion, chitosan–alginate nanoparticles and nerolidol loaded chitosan–alginate nanoparticles were successfully fabricated and characterized to show potential encapsulation and delivery using an in vitro model