• Login
    View Item 
    •   Home
    • Theses and Dissertations
    • Theses and Dissertations
    • View Item
    •   Home
    • Theses and Dissertations
    • Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of TUScholarShareCommunitiesDateAuthorsTitlesSubjectsGenresThis CollectionDateAuthorsTitlesSubjectsGenres

    My Account

    LoginRegister

    Help

    AboutPeoplePoliciesHelp for DepositorsData DepositFAQs

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Adaptive Neural Control of Gimbaled Laser Targeting System

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    TETDEDXGiorgi-temple-0225M-117 ...
    Size:
    6.000Mb
    Format:
    PDF
    Download
    Genre
    Thesis/Dissertation
    Date
    2014
    Author
    Giorgi, Salvatore
    Advisor
    Won, Chang-Hee, 1967-
    Committee member
    Helferty, John J.
    Silage, Dennis
    Department
    Electrical and Computer Engineering
    Subject
    Engineering
    Permanent link to this record
    http://hdl.handle.net/20.500.12613/2921
    
    Metadata
    Show full item record
    DOI
    http://dx.doi.org/10.34944/dspace/2903
    Abstract
    Space-based solar power is a better alternative to the ground-based solar system, because of its round-the-clock availability. Accurately pointing the laser from space to a ground receiver can increase transmission efficiency and decrease the receiving area. A superior pointing performance requires an accurate system model and controller. In this thesis, we investigate Adaptive Neural Control for a laser targeting application. A two-axis Gimbaled Laser Target System (GLTS) is used as hardware test bench of the space-based solar power transmission system. The Adaptive Neural Control (ANC) system, first proposed by D.C. Hyland, is a neural control system within a Model Reference Control (MRAC) architecture. It is composed of five separate neural networks, two of which are used to replicate an unknown plant, while the remaining three are used to control the plant's output to match that of an ideal reference system. The system has been successfully used in hardware such as the NASA / LaRC Mini-MAST testbed and the ASTREX testbed at Air Force Philips Laboratory. It has been shown to be very effective in terms of robustness, fault tolerance, and optimality. The objective of this research is to apply the ANC system to the problem of pointing and tracking the line of sight of a GLTS. A software model of the ANC system is built using Matlab / Simulink. We then simulate control of a linear stochastic model of our GLTS test bed and compare the ANC system's performance to that of a Proportional Integral Derivative (PID) controller. Next, we consider a separate nonlinear stochastic model of a two-axis gimbal, and consider the problem of platform stabilization using the ANC system. Next, we examine the ANC system's resiliency, defined in terms of how the controller maintains operational normalcy in response to anomalies, both unexpected and malicious. These anomalies will be in the form of added latencies, plant parameter changes, false data injection, and sensor data alteration. We simulate the attacks on the GLTS model and determine the system's resiliency to each attack through four metrics. These metrics are recovery time, performance degradation, protection time, and degrading time. We then compare these results to that of a PID controller subjected to the same attacks. Finally, the ANC system software model is translated to a fixed point hardware model for implementation on a Field Programmable Gate Array (FPGA) using Xilinx / System Generator. This software to hardware translation considers special attention to floating point to fixed point conversion, division, representing nonlinear neural functions, and hardware resource allocation. System replication and control simulations are run for the linear stochastic GLTS model. These simulations include ``hardware in the loop" simulations, where the actual FPGA is used within the Matlab simulation. Simulations show that the software model of the ANC system is able to replicate and control the linear GLTS model as well as a separate nonlinear gimbal model in the presence of process and measurement noise, with no prior modeling information. Additionally, the simulations demonstrate the resiliency of the ANC system when exposed to attacks, in terms of recovery time, performance degradation, and degrading time. Lastly, hardware simulations confirm the ANC system's ability to control the GLTS model using the FPGA.
    ADA compliance
    For Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
    Collections
    Theses and Dissertations

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Temple University Libraries | 1900 N. 13th Street | Philadelphia, PA 19122
    (215) 204-8212 | scholarshare@temple.edu
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.