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- Creators: Barrett, The Honors College
Description
Image stabilization is a highly desired feature for many systems involving cameras. A camera stabilizer effectively prevents or compensates for unwanted camera movement to provide this stabilization. The use of stabilized camera technology on board aerial vehicles is one such application where the stabilization can greatly improve the overall capability of the system. The requirements for such a system include a continuous control algorithm and hardware to determine and adjust the camera orientation. The topic of developing an aerial camera control and electronic stabilization system is thus explored in the contents of this paper.
ContributorsJauregui, Joseph (Co-author) / Brown, Steven (Co-author) / Burger, Kevin (Thesis director) / Hansen, Mark (Committee member) / Barrett, The Honors College (Contributor) / Computer Science and Engineering Program (Contributor)
Created2014-05
Description
This study aims to showcase the results of a quadrotor model and the mathematical techniques used to arrive at the proposed design. Multicopters have made an explosive appearance in recent years by the controls engineering community because of their unique flight performance capabilities and potential for autonomy. The ultimate goal of this research is to design a robust control system that guides and tracks the quadrotor's trajectory, while responding to outside disturbances and obstacles that will realistically be encountered during flight. The first step is to accurately identify the physical system and attempt to replicate its behavior with a simulation that mimics the system's dynamics. This becomes quite a complex problem in itself because many realistic systems do not abide by simple, linear mathematical models, but rather nonlinear equations that are difficult to predict and are often numerically unstable. This paper explores the equations and assumptions used to create a model that attempts to match roll and pitch data collected from multiple test flights. This is done primarily in the frequency domain to match natural frequency locations, which can then be manipulated judiciously by altering certain parameters.
ContributorsDuensing, Jared Christopher (Author) / Takahashi, Timothy (Thesis director) / Garrett, Frederick (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2014-05
Description
In this project, an existing waveform generator designed by the vagus nerve stimulation (VNS) technology firm Hoolest Performance Technologies was modified and characterized. Voltage feedback and current feedback systems were designed in order to improve output voltage and current regulation. A wireless communication system was implemented onboard the newly designed waveform generator in order to improve user experience and allow the system to be controlled remotely. Finally, a custom printed circuit board was designed according to the established circuit schematics for the above components, and the layout was miniaturized to a total board footprint area of 1.5 square inches. The completed device was characterized according to several figures of merit including current consumption, voltage and current regulation, and short-circuit behavior.
ContributorsPatterson, John Michael (Author) / Kozicki, Michael (Thesis director) / Mian, Sami (Committee member) / Electrical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
In this thesis, we focus on some of the NP-hard problems in control theory. Thanks to the converse Lyapunov theory, these problems can often be modeled as optimization over polynomials. To avoid the problem of intractability, we establish a trade off between accuracy and complexity. In particular, we develop a sequence of tractable optimization problems - in the form of Linear Programs (LPs) and/or Semi-Definite Programs (SDPs) - whose solutions converge to the exact solution of the NP-hard problem. However, the computational and memory complexity of these LPs and SDPs grow exponentially with the progress of the sequence - meaning that improving the accuracy of the solutions requires solving SDPs with tens of thousands of decision variables and constraints. Setting up and solving such problems is a significant challenge. The existing optimization algorithms and software are only designed to use desktop computers or small cluster computers - machines which do not have sufficient memory for solving such large SDPs. Moreover, the speed-up of these algorithms does not scale beyond dozens of processors. This in fact is the reason we seek parallel algorithms for setting-up and solving large SDPs on large cluster- and/or super-computers.
We propose parallel algorithms for stability analysis of two classes of systems: 1) Linear systems with a large number of uncertain parameters; 2) Nonlinear systems defined by polynomial vector fields. First, we develop a distributed parallel algorithm which applies Polya's and/or Handelman's theorems to some variants of parameter-dependent Lyapunov inequalities with parameters defined over the standard simplex. The result is a sequence of SDPs which possess a block-diagonal structure. We then develop a parallel SDP solver which exploits this structure in order to map the computation, memory and communication to a distributed parallel environment. Numerical tests on a supercomputer demonstrate the ability of the algorithm to efficiently utilize hundreds and potentially thousands of processors, and analyze systems with 100+ dimensional state-space. Furthermore, we extend our algorithms to analyze robust stability over more complicated geometries such as hypercubes and arbitrary convex polytopes. Our algorithms can be readily extended to address a wide variety of problems in control such as Hinfinity synthesis for systems with parametric uncertainty and computing control Lyapunov functions.
We propose parallel algorithms for stability analysis of two classes of systems: 1) Linear systems with a large number of uncertain parameters; 2) Nonlinear systems defined by polynomial vector fields. First, we develop a distributed parallel algorithm which applies Polya's and/or Handelman's theorems to some variants of parameter-dependent Lyapunov inequalities with parameters defined over the standard simplex. The result is a sequence of SDPs which possess a block-diagonal structure. We then develop a parallel SDP solver which exploits this structure in order to map the computation, memory and communication to a distributed parallel environment. Numerical tests on a supercomputer demonstrate the ability of the algorithm to efficiently utilize hundreds and potentially thousands of processors, and analyze systems with 100+ dimensional state-space. Furthermore, we extend our algorithms to analyze robust stability over more complicated geometries such as hypercubes and arbitrary convex polytopes. Our algorithms can be readily extended to address a wide variety of problems in control such as Hinfinity synthesis for systems with parametric uncertainty and computing control Lyapunov functions.
ContributorsKamyar, Reza (Author) / Peet, Matthew (Thesis advisor) / Berman, Spring (Committee member) / Rivera, Daniel (Committee member) / Artemiadis, Panagiotis (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2016
Description
This project compared two optimization-based formulations for solving multi-robot task allocation problems with tether constraints. The first approach, or the ”Iterative Method,” used the common multiple traveling salesman (mTSP) formulation and implemented an algorithm over the formulation to filter out solutions that failed to satisfy the tether constraint. The second approach, named the ”Timing Formulation,” involved constructing a new formulation specifically designed account for robot timings, including the tether constraint in the formulation itself. The approaches were tested against each other in 10-city simulations and the results were compared. The Iterative Method could provide answers in 1- and 2-norm variations quickly, but its mTSP model formulation broke down and became infeasible at low city numbers. The 1-norm Timing Formulation quickly and reliably produced solutions but faced high computation times in its 2-norm manifestation. Ultimately, while the Timing Formulation is a more optimal method for solving tether-constrained task allocation problems, its reliance on the 1-norm for low computation times causes it to sacrifice some realism.
ContributorsGoodwin, Walter (Author) / Yong, Sze Zheng (Thesis director) / Grewal, Anoop (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2022-05
ContributorsGoodwin, Walter (Author) / Yong, Sze Zheng (Thesis director) / Grewal, Anoop (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2022-05
ContributorsGoodwin, Walter (Author) / Yong, Sze Zheng (Thesis director) / Grewal, Anoop (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2022-05