Matching Items (52)
Description
This thesis addresses the design and control of three phase inverters. Such inverters are
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.
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.
ContributorsSarkar, Aratrik (Author) / Rodriguez, Armando A. (Thesis advisor) / Si, Jennie (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2015
Description
In many applications, measured sensor data is meaningful only when the location of sensors is accurately known. Therefore, the localization accuracy is crucial. In this dissertation, both location estimation and location detection problems are considered.
In location estimation problems, sensor nodes at known locations, called anchors, transmit signals to sensor nodes at unknown locations, called nodes, and use these transmissions to estimate the location of the nodes. Specifically, the location estimation in the presence of fading channels using time of arrival (TOA) measurements with narrowband communication signals is considered. Meanwhile, the Cramer-Rao lower bound (CRLB) for localization error under different assumptions is derived. Also, maximum likelihood estimators (MLEs) under these assumptions are derived.
In large WSNs, distributed location estimation algorithms are more efficient than centralized algorithms. A sequential localization scheme, which is one of distributed location estimation algorithms, is considered. Also, different localization methods, such as TOA, received signal strength (RSS), time difference of arrival (TDOA), direction of arrival (DOA), and large aperture array (LAA) are compared under different signal-to-noise ratio (SNR) conditions. Simulation results show that DOA is the preferred scheme at the low SNR regime and the LAA localization algorithm provides better performance for network discovery at high SNRs. Meanwhile, the CRLB for the localization error using the TOA method is also derived.
A distributed location detection scheme, which allows each anchor to make a decision as to whether a node is active or not is proposed. Once an anchor makes a decision, a bit is transmitted to a fusion center (FC). The fusion center combines all the decisions and uses a design parameter $K$ to make the final decision. Three scenarios are considered in this dissertation. Firstly, location detection at a known location is considered. Secondly, detecting a node in a known region is considered. Thirdly, location detection in the presence of fading is considered. The optimal thresholds are derived and the total probability of false alarm and detection under different scenarios are derived.
In location estimation problems, sensor nodes at known locations, called anchors, transmit signals to sensor nodes at unknown locations, called nodes, and use these transmissions to estimate the location of the nodes. Specifically, the location estimation in the presence of fading channels using time of arrival (TOA) measurements with narrowband communication signals is considered. Meanwhile, the Cramer-Rao lower bound (CRLB) for localization error under different assumptions is derived. Also, maximum likelihood estimators (MLEs) under these assumptions are derived.
In large WSNs, distributed location estimation algorithms are more efficient than centralized algorithms. A sequential localization scheme, which is one of distributed location estimation algorithms, is considered. Also, different localization methods, such as TOA, received signal strength (RSS), time difference of arrival (TDOA), direction of arrival (DOA), and large aperture array (LAA) are compared under different signal-to-noise ratio (SNR) conditions. Simulation results show that DOA is the preferred scheme at the low SNR regime and the LAA localization algorithm provides better performance for network discovery at high SNRs. Meanwhile, the CRLB for the localization error using the TOA method is also derived.
A distributed location detection scheme, which allows each anchor to make a decision as to whether a node is active or not is proposed. Once an anchor makes a decision, a bit is transmitted to a fusion center (FC). The fusion center combines all the decisions and uses a design parameter $K$ to make the final decision. Three scenarios are considered in this dissertation. Firstly, location detection at a known location is considered. Secondly, detecting a node in a known region is considered. Thirdly, location detection in the presence of fading is considered. The optimal thresholds are derived and the total probability of false alarm and detection under different scenarios are derived.
ContributorsZhang, Xue (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Berisha, Visar (Committee member) / Arizona State University (Publisher)
Created2016
Description
Physical activity helps in reducing the risk of many chronic diseases, and plays a key role in maintaining good health of an individual. Just Walk is an intensively adaptive physical activity intervention, which has been designed based on system identification and control engineering principles. The goal of Just Walk is to design interventions that are responsive to an individual's changing needs, and thus encourage the individual to increase the number of steps walked.
Regularization is widely used in the field of machine learning. The goal of this thesis is to see how classical system identification principles in combination with machine learning methods like regularization help towards getting improved model estimates for complex systems. Estimating individual behavioral models using traditional prediction error methods can be done using an order selection. However, this method is can be computationally expensive due to the extensive search performed on a large set of order combination. If order selection is not done properly, it can cause bias (low order) and variance (high order) issues. In such cases regularization plays an important role in addressing the bias-variance trade-off.
One of the most important applications of identifying individual behavioral models is to understand what factors impact most the behavior of the person. Here "factors" can be considered as inputs (designed or environmental) to the participant over the course of the study, and the "behavior" is the step count of the participant under study. This is done by estimating models with different input combinations and then seeing which combinations of inputs (influence behavior most) give the best model estimate (best describe behavior of the person). As a part of this thesis, it is studied how regularized models can give a better estimation of personalized behavioral models, for the Just Walk study, which can further help in designing personalized interventions.
Regularization is widely used in the field of machine learning. The goal of this thesis is to see how classical system identification principles in combination with machine learning methods like regularization help towards getting improved model estimates for complex systems. Estimating individual behavioral models using traditional prediction error methods can be done using an order selection. However, this method is can be computationally expensive due to the extensive search performed on a large set of order combination. If order selection is not done properly, it can cause bias (low order) and variance (high order) issues. In such cases regularization plays an important role in addressing the bias-variance trade-off.
One of the most important applications of identifying individual behavioral models is to understand what factors impact most the behavior of the person. Here "factors" can be considered as inputs (designed or environmental) to the participant over the course of the study, and the "behavior" is the step count of the participant under study. This is done by estimating models with different input combinations and then seeing which combinations of inputs (influence behavior most) give the best model estimate (best describe behavior of the person). As a part of this thesis, it is studied how regularized models can give a better estimation of personalized behavioral models, for the Just Walk study, which can further help in designing personalized interventions.
ContributorsMandal, Tarunima (Author) / Rivera, Daniel E (Thesis advisor) / Si, Jennie (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2020
Description
One potential application of multi-robot systems is collective transport, a task in which multiple mobile robots collaboratively transport a payload that is too large or heavy to be carried by a single robot. Numerous control schemes have been proposed for collective transport in environments where robots can localize themselves (e.g., using GPS) and communicate with one another, have information about the payload's geometric and dynamical properties, and follow predefined robot and/or payload trajectories. However, these approaches cannot be applied in uncertain environments where robots do not have reliable communication and GPS and lack information about the payload. These conditions characterize a variety of applications, including construction, mining, assembly in space and underwater, search-and-rescue, and disaster response.
Toward this end, this thesis presents decentralized control strategies for collective transport by robots that regulate their actions using only their local sensor measurements and minimal prior information. These strategies can be implemented on robots that have limited or absent localization capabilities, do not explicitly exchange information, and are not assigned predefined trajectories. The controllers are developed for collective transport over planar surfaces, but can be extended to three-dimensional environments.
This thesis addresses the above problem for two control objectives. First, decentralized controllers are proposed for velocity control of collective transport, in which the robots must transport a payload at a constant velocity through an unbounded domain that may contain strictly convex obstacles. The robots are provided only with the target transport velocity, and they do not have global localization or prior information about any obstacles in the environment. Second, decentralized controllers are proposed for position control of collective transport, in which the robots must transport a payload to a target position through a bounded or unbounded domain that may contain convex obstacles. The robots are subject to the same constraints as in the velocity control scenario, except that they are assumed to have global localization. Theoretical guarantees for successful execution of the task are derived using techniques from nonlinear control theory, and it is shown through simulations and physical robot experiments that the transport objectives are achieved with the proposed controllers.
Toward this end, this thesis presents decentralized control strategies for collective transport by robots that regulate their actions using only their local sensor measurements and minimal prior information. These strategies can be implemented on robots that have limited or absent localization capabilities, do not explicitly exchange information, and are not assigned predefined trajectories. The controllers are developed for collective transport over planar surfaces, but can be extended to three-dimensional environments.
This thesis addresses the above problem for two control objectives. First, decentralized controllers are proposed for velocity control of collective transport, in which the robots must transport a payload at a constant velocity through an unbounded domain that may contain strictly convex obstacles. The robots are provided only with the target transport velocity, and they do not have global localization or prior information about any obstacles in the environment. Second, decentralized controllers are proposed for position control of collective transport, in which the robots must transport a payload to a target position through a bounded or unbounded domain that may contain convex obstacles. The robots are subject to the same constraints as in the velocity control scenario, except that they are assumed to have global localization. Theoretical guarantees for successful execution of the task are derived using techniques from nonlinear control theory, and it is shown through simulations and physical robot experiments that the transport objectives are achieved with the proposed controllers.
ContributorsFarivarnejad, Hamed (Author) / Berman, Spring (Thesis advisor) / Mignolet, Marc (Committee member) / Tsakalis, Konstantinos (Committee member) / Artemiadis, Panagiotis (Committee member) / Gil, Stephanie (Committee member) / Arizona State University (Publisher)
Created2020
Description
This work is concerned with how best to reconstruct images from limited angle tomographic measurements. An introduction to tomography and to limited angle tomography will be provided and a brief overview of the many fields to which this work may contribute is given.
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.
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.
ContributorsDallmann, Nicholas A. (Author) / Tsakalis, Konstantinos (Thesis advisor) / Hardgrove, Craig (Committee member) / Rodriguez, Armando (Committee member) / Si, Jennie (Committee member) / Arizona State University (Publisher)
Created2020
Description
In the last few decades, with the revolution of availability of low-cost microelectronics, which allow fast and complex computations to be performed on board, there has been increasing attention to aerial vehicles, especially rotary-wing vehicles. This is because of their ability to vertically takeoff and land (VTOL), which make them appropriate for urban environments where no runways are needed. Quadrotors took considerable attention in research and development due to their symmetric body, which makes them simpler to model and control compared to other configurations.
One contribution of this work is the design of a new open-source based Quadrotor platform for research. This platform is compatible with both HTC Vive Tracking System (HVTS) and OptiTrack Motion Capture System, Robot Operating System (ROS), and MAVLINK communication protocol.
The thesis examined both nonlinear and linear modeling of a 6-DOF rigid-body quadrotor's dynamics along with actuator dynamics. Nonlinear/linear models are used to develop control laws for both low-level and high-level hierarchical control structures. Both HVTS and OptiTrack were used to demonstrate path following for single and multiple quadrotors. Hardware and simulation data are compared. In short, this work establishes a foundation for future work on formation flight of multi-quadrotor.
One contribution of this work is the design of a new open-source based Quadrotor platform for research. This platform is compatible with both HTC Vive Tracking System (HVTS) and OptiTrack Motion Capture System, Robot Operating System (ROS), and MAVLINK communication protocol.
The thesis examined both nonlinear and linear modeling of a 6-DOF rigid-body quadrotor's dynamics along with actuator dynamics. Nonlinear/linear models are used to develop control laws for both low-level and high-level hierarchical control structures. Both HVTS and OptiTrack were used to demonstrate path following for single and multiple quadrotors. Hardware and simulation data are compared. In short, this work establishes a foundation for future work on formation flight of multi-quadrotor.
ContributorsAltawaitan, Abdullah (Author) / Rodriguez, Armando A (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Berman, Spring (Committee member) / Arizona State University (Publisher)
Created2019
Description
Vertical take-off and landing (VTOL) systems have become a crucial component of aeronautical and commercial applications alike. Quadcopter systems are rather convenient to analyze and design controllers for, owing to symmetry in body dynamics. In this work, a quadcopter model at hover equilibrium is derived, using both high and low level control. The low level control system is designed to track reference Euler angles (roll, pitch and yaw) as shown in previous work [1],[2]. The high level control is designed to track reference X, Y, and Z axis states [3].
The objective of this paper is to model, design and simulate platooning (separation) control for a fleet of 6 quadcopter units, each comprising of high and low level control systems, using a leader-follower approach. The primary motivation of this research is to examine the ”accordion effect”, a phenomenon observed in leader-follower systems due to which positioning or spacing errors arise in follower vehicles due to sudden changes in lead vehicle velocity. It is proposed that the accordion effect occurs when lead vehicle information is not directly communicated with the rest of the
system [4][5]
.
In this paper, the effect of leader acceleration feedback is observed for the quadcopter platoon. This is performed by first designing a classical platoon controller for a nominal case, where communication within the system is purely ad-hoc (i.e from one quadcopter to it’s immediate successor in the fleet). Steady state separation/positioning errors for each member of the fleet are observed and documented during simulation. Following this analysis, lead vehicle acceleration is provided to the controller (as a feed forward term), to observe the extent of it’s effect on steady state separation, specifically along tight maneuvers. Thus the key contribution of this work is a controller that stabilizes a platoon of quadcopters in the presence of the accordion effect, when employing a leader-follower approach. The modeling shown in this paper builds on previous research to design a low costquadcopter platform, the Mark 3 copter [1]. Prior to each simulation, model nonlinearities and hardware constants are measured or derived from the Mark 3 model, in an effort to observe the working of the system in the presence of realistic hardware constraints. The system is designed in compliance with Robot Operating System (ROS) and the Micro Air Vehicle Link (MAVLINK) communication protocol.
ContributorsSrinivasan, Anshuman (Author) / Rodriguez, Armando A. (Thesis advisor) / Si, Jennie (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2021
Description
The Inverted Pendulum on a Cart is a classical control theory problem that helps understand the importance of feedback control systems for a coupled plant. In this study, a custom built pendulum system is coupled with a linearly actuated cart and a control system is designed to show the stability of the pendulum. The three major objectives of this control system are to swing up the pendulum, balance the pendulum in the inverted position (i.e. $180^\circ$), and maintain the position of the cart. The input to this system is the translational force applied to the cart using the rotation of the tires. The main objective of this thesis is to design a control system that will help in balancing the pendulum while maintaining the position of the cart and implement it in a robot. The pendulum is made free rotating with the help of ball bearings and the angle of the pendulum is measured using an Inertial Measurement Unit (IMU) sensor. The cart is actuated by two Direct Current (DC) motors and the position of the cart is measured using encoders that generate pulse signals based on the wheel rotation. The control is implemented in a cascade format where an inner loop controller is used to stabilize and balance the pendulum in the inverted position and an outer loop controller is used to control the position of the cart. Both the inner loop and outer loop controllers follow the Proportional-Integral-Derivative (PID) control scheme with some modifications for the inner loop. The system is first mathematically modeled using the Newton-Euler first principles method and based on this model, a controller is designed for specific closed-loop parameters. All of this is implemented on hardware with the help of an Arduino Due microcontroller which serves as the main processing unit for the system.
ContributorsNamasivayam, Vignesh (Author) / Tsakalis, Konstantinos (Thesis advisor) / Rodriguez, Armando (Committee member) / Si, Jennie (Committee member) / Shafique, Md. Ashfaque Bin (Committee member) / Arizona State University (Publisher)
Created2021
Description
Gas turbine engine for aircraft propulsion represents one of the most physics-complex and safety-critical systems in the world. Its failure diagnostic is challenging due to the complexity of the model system, difficulty involved in practical testing and the infeasibility of creating homogeneous diagnostic performance evaluation criteria for the diverse engine makes.
NASA has designed and publicized a standard benchmark problem for propulsion engine gas path diagnostic that enables comparisons among different engine diagnostic approaches. Some traditional model-based approaches and novel purely data-driven approaches such as machine learning, have been applied to this problem.
This study focuses on a different machine learning approach to the diagnostic problem. Some most common machine learning techniques, such as support vector machine, multi-layer perceptron, and self-organizing map are used to help gain insight into the different engine failure modes from the perspective of big data. They are organically integrated to achieve good performance based on a good understanding of the complex dataset.
The study presents a new hierarchical machine learning structure to enhance classification accuracy in NASA's engine diagnostic benchmark problem. The designed hierarchical structure produces an average diagnostic accuracy of 73.6%, which outperforms comparable studies that were most recently published.
NASA has designed and publicized a standard benchmark problem for propulsion engine gas path diagnostic that enables comparisons among different engine diagnostic approaches. Some traditional model-based approaches and novel purely data-driven approaches such as machine learning, have been applied to this problem.
This study focuses on a different machine learning approach to the diagnostic problem. Some most common machine learning techniques, such as support vector machine, multi-layer perceptron, and self-organizing map are used to help gain insight into the different engine failure modes from the perspective of big data. They are organically integrated to achieve good performance based on a good understanding of the complex dataset.
The study presents a new hierarchical machine learning structure to enhance classification accuracy in NASA's engine diagnostic benchmark problem. The designed hierarchical structure produces an average diagnostic accuracy of 73.6%, which outperforms comparable studies that were most recently published.
ContributorsWu, Qiyu (Author) / Si, Jennie (Thesis advisor) / Wu, Teresa (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2015
Description
Operational efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance. Monitoring utility-scale solar arrays was shown to minimize the cost of maintenance and help optimize the performance of photovoltaic (PV) arrays under various conditions. This dissertation describes a project that focuses on the development of machine learning and neural network algorithms. It also describes an 18kW solar array testbed for the purpose of PV monitoring and control. The use of the 18kW Sensor Signal and Information Processing (SenSIP) PV testbed which consists of 104 modules fitted with smart monitoring devices (SMDs) is described in detail. Each of the SMDs has embedded, a wireless transceiver, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. Data is obtained in real time using the SenSIP PV testbed. Machine learning and neural network algorithms for PV fault classification is are studied in depth. More specifically, the development of a series of customized neural networks for detection and classification of solar array faults that include soiling, shading, degradation, short circuits and standard test conditions is considered. The evaluation of fault detection and classification methods using metrics such as accuracy, confusion matrices, and the Risk Priority Number (RPN) is performed. The examination and assessment the classification performance of customized neural networks with dropout regularizers is presented in detail. The development and evaluation of neural network pruning strategies and illustration of the trade-off between fault classification model accuracy and algorithm complexity is studied. This study includes data from the National Renewable Energy Laboratory (NREL) database and also real-time data collected from the SenSIP testbed at MTW under various loading and shading conditions. The overall approach for detection and classification promises to elevate the performance and robustness of PV arrays.
ContributorsRao, Sunil (Author) / Spanias, Andreas (Thesis advisor) / Tepedelenlioğlu, Cihan (Thesis advisor) / Tsakalis, Konstantinos (Committee member) / Srinivasan, Devarajan (Committee member) / Arizona State University (Publisher)
Created2021