ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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- Creators: Tsakalis, Konstantinos
One contribution of the work was the design of a new printed-circuit-board (PCB) flight controller (called MARK3). Key features/capabilities are as follows:
(1) a Teensy 3.2 microcontroller with 168MHz overclock –used for communications, full-state estimation and inner-outer loop hierarchical rate-angle-speed-position control,
(2) an on-board MEMS inertial-measurement-unit (IMU) which includes an LSM303D (3DOF-accelerometer and magnetometer), an L3GD20 (3DOF-gyroscope) and a BMP180 (barometer) for attitude estimation (barometer/magnetometer not used),
(3) 6 pulse-width-modulator (PWM) output pins supports up to 6 rotors
(4) 8 PWM input pins support up to 8-channel 2.4 GHz transmitter/receiver for manual control,
(5) 2 5V servo extension outputs for other requirements (e.g. gimbals),
(6) 2 universal-asynchronous-receiver-transmitter (UART) serial ports - used by flight controller to process data from Xbee; can be used for accepting outer-loop position commands from NVIDIA TX2 (future work),
(7) 1 I2C-serial-protocol two-wire port for additional modules (used to read data from IMU at 400 Hz),
(8) a 20-pin port for Xbee telemetry module connection; permits Xbee transceiver on desktop PC to send position/attitude commands to Xbee transceiver on quadcopter.
The quadcopter platform consists of the new MARK3 PCB Flight Controller, an ATG-250 carbon-fiber frame (250 mm), a DJI Snail propulsion-system (brushless-three-phase-motor, electronic-speed-controller (ESC) and propeller), an HTC VIVE Tracker and RadioLink R9DS 9-Channel 2.4GHz Receiver. This platform is completely compatible with the HTC VIVE Tracking System (HVTS) which has 7ms latency, submillimeter accuracy and a much lower price compared to other millimeter-level tracking systems.
The thesis describes nonlinear and linear modeling of the quadcopter’s 6DOF rigid-body dynamics and brushless-motor-actuator dynamics. These are used for hierarchical-classical-control-law development near hover. The HVTS was used to demonstrate precision hover-control and path-following. Simulation and measured flight-data are shown to be similar. This work provides a foundation for future precision multi-quadcopter formation-flight-control.
used to produce three-phase sinusoidal voltages and currents from a DC source. They
are critical for injecting power from renewable energy sources into the grid. This is
especially true since many of these sources of energy are DC sources (e.g. solar
photovoltaic) or need to be stored in DC batteries because they are intermittent (e.g. wind
and solar). Two classes of inverters are examined in this thesis. A control-centric design
procedure is presented for each class. The first class of inverters is simple in that they
consist of three decoupled subsystems. Such inverters are characterized by no mutual
inductance between the three phases. As such, no multivariable coupling is present and
decentralized single-input single-output (SISO) control theory suffices to generate
acceptable control designs. For this class of inverters several families of controllers are
addressed in order to examine command following as well as input disturbance and noise
attenuation specifications. The goal here is to illuminate fundamental tradeoffs. Such
tradeoffs include an improvement in the in-band command following and output
disturbance attenuation versus a deterioration in out-of-band noise attenuation.
A fundamental deficiency associated with such inverters is their large size. This can be
remedied by designing a smaller core. This naturally leads to the second class of inverters
considered in this work. These inverters are characterized by significant mutual
inductances and multivariable coupling. As such, SISO control theory is generally not
adequate and multiple-input multiple-output (MIMO) theory becomes essential for
controlling these inverters.
The traditional tomographic image reconstruction approach involves Fourier domain representations. The classic Filtered Back Projection algorithm will be discussed and used for comparison throughout the work. Bayesian statistics and information entropy considerations will be described. The Maximum Entropy reconstruction method will be derived and its performance in limited angular measurement scenarios will be examined.
Many new approaches become available once the reconstruction problem is placed within an algebraic form of Ax=b in which the measurement geometry and instrument response are defined as the matrix A, the measured object as the column vector x, and the resulting measurements by b. It is straightforward to invert A. However, for the limited angle measurement scenarios of interest in this work, the inversion is highly underconstrained and has an infinite number of possible solutions x consistent with the measurements b in a high dimensional space.
The algebraic formulation leads to the need for high performing regularization approaches which add constraints based on prior information of what is being measured. These are constraints beyond the measurement matrix A added with the goal of selecting the best image from this vast uncertainty space. It is well established within this work that developing satisfactory regularization techniques is all but impossible except for the simplest pathological cases. There is a need to capture the "character" of the objects being measured.
The novel result of this effort will be in developing a reconstruction approach that will match whatever reconstruction approach has proven best for the types of objects being measured given full angular coverage. However, when confronted with limited angle tomographic situations or early in a series of measurements, the approach will rely on a prior understanding of the "character" of the objects measured. This understanding will be learned by a parallel Deep Neural Network from examples.