Matching Items (36)

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Dynamic Modeling, System Identification, and Control Engineering Approaches for Designing Optimized and Perpetually Adaptive Behavioral Health Interventions

Description

Behavior-driven obesity has become one of the most challenging global epidemics since the 1990s, and is presently associated with the leading causes of death in the U.S. and worldwide, including diabetes, cardiovascular disease, strokes, and some forms of cancer. The

Behavior-driven obesity has become one of the most challenging global epidemics since the 1990s, and is presently associated with the leading causes of death in the U.S. and worldwide, including diabetes, cardiovascular disease, strokes, and some forms of cancer. The use of system identification and control engineering principles in the design of novel and perpetually adaptive behavioral health interventions for promoting physical activity and healthy eating has been the central theme in many recent contributions. However, the absence of experimental studies specifically designed with the purpose of developing control-oriented behavioral models has restricted prior efforts in this domain to the use of hypothetical simulations to demonstrate the potential viability of these interventions. In this dissertation, the use of first-of-a-kind, real-life experimental results to develop dynamic, participant-validated behavioral models essential for the design and evaluation of optimized and adaptive behavioral interventions is examined. Following an intergenerational approach, the first part of this work aims to develop a dynamical systems model of intrauterine fetal growth with the prime goal of predicting infant birth weight, which has been associated with subsequent childhood and adult-onset obesity. The use of longitudinal input-output data from the “Healthy Mom Zone” intervention study has enabled the estimation and validation of this fetoplacental model. The second part establishes a set of data-driven behavioral models founded on Social Cognitive Theory (SCT). The “Just Walk” intervention experiment, developed at Arizona State University using system identification principles, has lent a unique opportunity to estimate and validate both black-box and semiphysical SCT models for predicting physical activity behavior. Further, this dissertation addresses some of the model estimation challenges arising from the limitations of “Just Walk”, including the need for developing nontraditional modeling approaches for short datasets, as well as delivers a new theoretical and algorithmic framework for structured state-space model estimation that can be used in a broader set of application domains. Finally, adaptive closed-loop intervention simulations of participant-validated SCT models from “Just Walk” are presented using a Hybrid Model Predictive Control (HMPC) control law. A simple HMPC controller reconfiguration strategy for designing both single- and multi-phase intervention designs is proposed.

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2021

Control for Resonant Microbeam Vibrotactile Haptic Displays

Description

The world’s population is currently 9% visually impaired. Medical sciences do not have a biological fix that can cure this visual impairment. Visually impaired people are currently being assisted with biological fixes or assistive devices. The current assistive devices are

The world’s population is currently 9% visually impaired. Medical sciences do not have a biological fix that can cure this visual impairment. Visually impaired people are currently being assisted with biological fixes or assistive devices. The current assistive devices are limited in size as well as resolution. This thesis presents the development and experimental validation of a control system for a new vibrotactile haptic display that is currently in development. In order to allow the vibrotactile haptic display to be used to represent motion, the control system must be able to change the image displayed at a rate of at least 30 frames/second. In order to achieve this, this thesis introduces and investigates the use of three improvements: threading, change filtering, and wave libraries. Through these methods, it is determined that an average of 40 frames/second can be achieved.

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2018

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Modeling and H-Infinity Loop Shaping Control of a Vertical Takeoff and Landing Drone

Description

VTOL drones were designed and built at the beginning of the 20th century for military applications due to easy take-off and landing operations. Many companies like Lockheed, Convair, NASA and Bell Labs built their own aircrafts but only a few

VTOL drones were designed and built at the beginning of the 20th century for military applications due to easy take-off and landing operations. Many companies like Lockheed, Convair, NASA and Bell Labs built their own aircrafts but only a few from them came in to the market. Usually, flight automation starts from first principles modeling which helps in the controller design and dynamic analysis of the system.

In this project, a VTOL drone with a shape similar to a Convair XFY-1 is studied and the primary focus is stabilizing and controlling the flight path of the drone in
its hover and horizontal flying modes. The model of the plane is obtained using first principles modeling and controllers are designed to stabilize the yaw, pitch and roll rotational motions.

The plane is modeled for its yaw, pitch and roll rotational motions. Subsequently, the rotational dynamics of the system are linearized about the hover flying mode, hover to horizontal flying mode, horizontal flying mode, horizontal to hover flying mode for ease of implementation of linear control design techniques. The controllers are designed based on an H∞ loop shaping procedure and the results are verified on the actual nonlinear model for the stability of the closed loop system about hover flying, hover to horizontal transition flying, horizontal flying, horizontal to hover transition flying. An experiment is conducted to study the dynamics of the motor by recording the PWM input to the electronic speed controller as input and the rotational speed of the motor as output. A theoretical study is also done to study the thrust generated by the propellers for lift, slipstream velocity analysis, torques acting on the system for various thrust profiles.

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2018

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How to Think About Resilient Infrastructure Systems

Description

Resilience is emerging as the preferred way to improve the protection of infrastructure systems beyond established risk management practices. Massive damages experienced during tragedies like Hurricane Katrina showed that risk analysis is incapable to prevent unforeseen infrastructure failures and shifted

Resilience is emerging as the preferred way to improve the protection of infrastructure systems beyond established risk management practices. Massive damages experienced during tragedies like Hurricane Katrina showed that risk analysis is incapable to prevent unforeseen infrastructure failures and shifted expert focus towards resilience to absorb and recover from adverse events. Recent, exponential growth in research is now producing consensus on how to think about infrastructure resilience centered on definitions and models from influential organizations like the US National Academy of Sciences. Despite widespread efforts, massive infrastructure failures in 2017 demonstrate that resilience is still not working, raising the question: Are the ways people think about resilience producing resilient infrastructure systems?

This dissertation argues that established thinking harbors misconceptions about infrastructure systems that diminish attempts to improve their resilience. Widespread efforts based on the current canon focus on improving data analytics, establishing resilience goals, reducing failure probabilities, and measuring cascading losses. Unfortunately, none of these pursuits change the resilience of an infrastructure system, because none of them result in knowledge about how data is used, goals are set, or failures occur. Through the examination of each misconception, this dissertation results in practical, new approaches for infrastructure systems to respond to unforeseen failures via sensing, adapting, and anticipating processes. Specifically, infrastructure resilience is improved by sensing when data analytics include the modeler-in-the-loop, adapting to stress contexts by switching between multiple resilience strategies, and anticipating crisis coordination activities prior to experiencing a failure.

Overall, results demonstrate that current resilience thinking needs to change because it does not differentiate resilience from risk. The majority of research thinks resilience is a property that a system has, like a noun, when resilience is really an action a system does, like a verb. Treating resilience as a noun only strengthens commitment to risk-based practices that do not protect infrastructure from unknown events. Instead, switching to thinking about resilience as a verb overcomes prevalent misconceptions about data, goals, systems, and failures, and may bring a necessary, radical change to the way infrastructure is protected in the future.

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2018

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Impact of Continuous Improvement in a School District

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The most common approach to improvement of educational systems has been the adoption of episodic initiatives and short-lived improvement programs. In recent years, a continuous improvement (CI) approach has made it onto the education scene, but the effects of continuous

The most common approach to improvement of educational systems has been the adoption of episodic initiatives and short-lived improvement programs. In recent years, a continuous improvement (CI) approach has made it onto the education scene, but the effects of continuous improvement in education remain largely unstudied. This study addressed the need to examine the long term impacts of CI in educational organizations.

Using a CI framework, this mixed methods action research (MMAR) study was conducted to examine the impact of CI on one school district’s measures of quality, stakeholder satisfaction, and cost savings, as well as to determine the value of CI leadership coaching, according to district leaders.

Qualitative data included interviews with and observations of school district leaders and the CI leadership coach. Quantitative data included stakeholder surveys, district scorecards, and state data dashboard reports. Results indicated improvement from a CI approach was slow to occur, though CI positively impacted some quality outcomes, primarily in the area of math. CI positively impacted student satisfaction, though it had minimal impact on employee and parent satisfaction. The district experienced cost savings as a result of CI, and CI leadership coaching was reported as highly valued by district leaders. The results of the study suggest a systematic CI approach and coaching support can impact change over time, but requires patience and a within district executive leader champion.

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2019

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Model predictive control for resilient operation of hybrid microgrids

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This dissertation develops advanced controls for distributed energy systems and evaluates performance on technical and economic benefits. Microgrids and thermal systems are of primary focus with applications shown for residential, commercial, and military applications that have differing equipment, rate structures,

This dissertation develops advanced controls for distributed energy systems and evaluates performance on technical and economic benefits. Microgrids and thermal systems are of primary focus with applications shown for residential, commercial, and military applications that have differing equipment, rate structures, and objectives. Controls development for residential energy heating and cooling systems implement adaptive precooling strategies and thermal energy storage, with comparisons made of each approach separately and then together with precooling and thermal energy storage. Case studies show on-peak demand and annual energy related expenses can be reduced by up to 75.6% and 23.5%, respectively, for a Building America B10 Benchmark home in Phoenix Arizona, Los Angeles California, and Kona Hawaii. Microgrids for commercial applications follow after with increased complexity. Three control methods are developed and compared including a baseline logic-based control, model predictive control, and model predictive control with ancillary service control algorithms. Case studies show that a microgrid consisting of 326 kW solar PV, 634 kW/ 634 kWh battery, and a 350 kW diesel generator can reduce on-peak demand and annual energy related expenses by 82.2% and 44.1%, respectively. Findings also show that employing a model predictive control algorithm with ancillary services can reduce operating expenses by 23.5% when compared to a logic-based algorithm. Microgrid evaluation continues with an investigation of off-grid operation and resilience for military applications. A statistical model is developed to evaluate the survivability (i.e. probability to meet critical load during an islanding event) to serve critical load out to 7 days of grid outage. Case studies compare the resilience of a generator-only microgrid consisting of 5,250 kW in generators and hybrid microgrid consisting of 2,250 kW generators, 3,450 kW / 13,800 kWh storage, and 16,479 kW solar photovoltaics. Findings show that the hybrid microgrid improves survivability by 10.0% and decreases fuel consumption by 47.8% over a 168-hour islanding event when compared to a generator-only microgrid under nominal conditions. Findings in this dissertation can increase the adoption of reliable, low cost, and low carbon distributed energy systems by improving the operational capabilities and economic benefits to a variety of customers and utilities.

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2019

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Analysis and Control of Space Systems Dynamics via Floquet Theory, Normal Forms and Center Manifold Reduction

Description

It remains unquestionable that space-based technology is an indispensable component of modern daily lives. Success or failure of space missions is largely contingent upon the complex system analysis and design methodologies exerted in converting the initial idea

into an elaborate functioning

It remains unquestionable that space-based technology is an indispensable component of modern daily lives. Success or failure of space missions is largely contingent upon the complex system analysis and design methodologies exerted in converting the initial idea

into an elaborate functioning enterprise. It is for this reason that this dissertation seeks to contribute towards the search for simpler, efficacious and more reliable methodologies and tools that accurately model and analyze space systems dynamics. Inopportunely, despite the inimical physical hazards, space systems must endure a perturbing dynamical environment that persistently disorients spacecraft attitude, dislodges spacecraft from their designated orbital locations and compels spacecraft to follow undesired orbital trajectories. The ensuing dynamics’ analytical models are complexly structured, consisting of parametrically excited nonlinear systems with external periodic excitations–whose analysis and control is not a trivial task. Therefore, this dissertation’s objective is to overcome the limitations of traditional approaches (averaging and perturbation, linearization) commonly used to analyze and control such dynamics; and, further obtain more accurate closed-form analytical solutions in a lucid and broadly applicable manner. This dissertation hence implements a multi-faceted methodology that relies on Floquet theory, invariant center manifold reduction and normal forms simplification. At the heart of this approach is an intuitive system state augmentation technique that transforms non-autonomous nonlinear systems into autonomous ones. Two fitting representative types of space systems dynamics are investigated; i) attitude motion of a gravity gradient stabilized spacecraft in an eccentric orbit, ii) spacecraft motion in the vicinity of irregularly shaped small bodies. This investigation demonstrates how to analyze the motion stability, chaos, periodicity and resonance. Further, versal deformation of the normal forms scrutinizes the bifurcation behavior of the gravity gradient stabilized attitude motion. Control laws developed on transformed, more tractable analytical models show that; unlike linear control laws, nonlinear control strategies such as sliding mode control and bifurcation control stabilize the intricate, unwieldy astrodynamics. The pitch attitude dynamics are stabilized; and, a regular periodic orbit realized in the vicinity of small irregularly shaped bodies. Importantly, the outcomes obtained are unconventionally realized as closed-form analytical solutions obtained via the comprehensive approach introduced by this dissertation.

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2019

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Self-organizing Coordination of Multi-Agent Microgrid Networks

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This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions

This work introduces self-organizing techniques to reduce the complexity and burden of coordinating distributed energy resources (DERs) and microgrids that are rapidly increasing in scale globally. Technical and financial evaluations completed for power customers and for utilities identify how disruptions are occurring in conventional energy business models. Analyses completed for Chicago, Seattle, and Phoenix demonstrate site-specific and generalizable findings. Results indicate that net metering had a significant effect on the optimal amount of solar photovoltaics (PV) for households to install and how utilities could recover lost revenue through increasing energy rates or monthly fees. System-wide ramp rate requirements also increased as solar PV penetration increased. These issues are resolved using a generalizable, scalable transactive energy framework for microgrids to enable coordination and automation of DERs and microgrids to ensure cost effective use of energy for all stakeholders. This technique is demonstrated on a 3-node and 9-node network of microgrid nodes with various amounts of load, solar, and storage. Results found that enabling trading could achieve cost savings for all individual nodes and for the network up to 5.4%. Trading behaviors are expressed using an exponential valuation curve that quantifies the reputation of trading partners using historical interactions between nodes for compatibility, familiarity, and acceptance of trades. The same 9-node network configuration is used with varying levels of connectivity, resulting in up to 71% cost savings for individual nodes and up to 13% cost savings for the network as a whole. The effect of a trading fee is also explored to understand how electricity utilities may gain revenue from electricity traded directly between customers. If a utility imposed a trading fee to recoup lost revenue then trading is financially infeasible for agents, but could be feasible if only trying to recoup cost of distribution charges. These scientific findings conclude with a brief discussion of physical deployment opportunities.

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2019

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Optimization Based Verification and Synthesis for Safe Autonomy

Description

Autonomous systems should satisfy a set of requirements that guarantee their safety, efficiency, and reliability when working under uncertain circumstances. These requirements can have financial, or legal implications or they can describe what is assigned to autonomous systems.As a result,

Autonomous systems should satisfy a set of requirements that guarantee their safety, efficiency, and reliability when working under uncertain circumstances. These requirements can have financial, or legal implications or they can describe what is assigned to autonomous systems.As a result, the system controller needs to be designed in order to comply with these - potentially complicated - requirements, and the closed-loop system needs to be tested and verified against these requirements.
However, when the complexity of the system and its requirements increases, designing a requirement-based controller for the system and analyzing the closed-loop system against the requirement becomes very challenging. In this case, existing design and test methodologies based on trial-and-error would fail, and hence disciplined scientific approaches should be considered.
To address some of these challenges, in this dissertation, I present different methods that facilitate efficient testing, and control design based on requirements:
1. Gradient-based methods for improved optimization-based testing,
2. Requirement-based learning for the design of neural-network controllers,
3. Methods based on barrier functions for designing control inputs that ensure the satisfaction of safety constraints.

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2021

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Communicating intent in autonomous vehicles

Description

The prospects of commercially available autonomous vehicles are surely tantalizing, however the implementation of these vehicles and their strain on the social dynamics between motorists and pedestrians remains unknown. Questions concerning how autonomous vehicles will communicate safety and intent to

The prospects of commercially available autonomous vehicles are surely tantalizing, however the implementation of these vehicles and their strain on the social dynamics between motorists and pedestrians remains unknown. Questions concerning how autonomous vehicles will communicate safety and intent to pedestrians remain largely unanswered. This study examines the efficacy of various proposed technologies for bridging the communication gap between self-driving cars and pedestrians. Displays utilizing words like “safe” and “danger” seem to be effective in communicating with pedestrians and other road users. Future research should attempt to study different external notification interfaces in real-life settings to more accurately gauge pedestrian responses.

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2019