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SEQUENTIAL MONTE CARLO METHODS FOR PARAMETER ESTIMATION, DYNAMIC STATE ESTIMATION AND CONTROL IN POWER SYSTEMS
Title
SEQUENTIAL MONTE CARLO METHODS FOR PARAMETER ESTIMATION, DYNAMIC STATE ESTIMATION AND CONTROL IN POWER SYSTEMS
Creator
Maldonado, Daniel Adrian
Date
2017, 2017-05
Description
The estimation, operation and control of electrical power systems have always contained a degree of uncertainty. It is expected that, with the...
Show moreThe estimation, operation and control of electrical power systems have always contained a degree of uncertainty. It is expected that, with the introduction of technologies such as distributed generation and demand-side management, the ability of system operators to forecast the dynamic behavior of the system will deteriorate and as a result, the cost of keeping the system together will increase. Sequential Monte Carlo or Particle Filtering is a family of algorithms to efficiently perform inference in non-linear dynamic systems by exploiting their structure without assuming any linearity or normality structure. In this thesis we provide two novel ways of employing these algorithms for inference and control of power systems. First, we motivate the use Bayesian statistics in load modelling by introducing a novel statistical model to capture the aggregated response of a set of loads. We then use the model to characterize load with measurement data and prior information using the Sequential Monte Carlo algorithm. Second, we introduce the Model Predictive Control for power system stabilization. We present the use of the Sequential Monte Carlo algorithm as a way of solving the stochastic Model Predictive Control problem and we compare its performance to existing regulators. In addition, Model Predictive Control is applied to load shedding Finally, we test the performance of the algorithm in a large power system scenario.
Ph.D. in Electrical Engineering, May 2017
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ADVANCING KNOWLEDGE OF INDOOR AEROSOL SOURCES, FATE, TRANSPORT, AND CONTROL
Title
ADVANCING KNOWLEDGE OF INDOOR AEROSOL SOURCES, FATE, TRANSPORT, AND CONTROL
Creator
Azimi, Parham
Date
2016, 2016-12
Description
Recent evidence suggests that particulate matter (of both indoor and outdoor origin) is one of the most important airborne pollutants driving...
Show moreRecent evidence suggests that particulate matter (of both indoor and outdoor origin) is one of the most important airborne pollutants driving adverse health effects worldwide. Despite our understanding of major indoor aerosol sources that contribute to adverse health effects across the population, gaps in our knowledge of some aspects of the sources, fate, transport, and control of indoor aerosols still remain. This dissertation focuses on filling three major gaps related to indoor aerosols. The first objective of this dissertation is to improve knowledge of the impacts of particle filtration in central heating, ventilation, and air-conditioning (HVAC) systems on fine particles smaller than 2.5 μm in diameter (i.e., PM2.5) and ultrafine particles smaller than 100 nm in diameter (i.e., UFPs) of outdoor origin that penetrate into the indoor environment. Results demonstrate that higher-efficiency HVAC filters can significantly reduce indoor proportions of outdoor PM2.5 and UFPs inside residences, but home vintage, climate zone, and ventilation strategy strongly influence the outcomes due to widely varying air exchange rates, HVAC system runtimes, and sources of ventilation air. The second objective of this dissertation is to improve knowledge of emissions and control of particulate matter from a recently established source of indoor pollutants: desktop three-dimensional (3D) printers. Median estimates of time-varying UFP emission rates ranged from ~108 to ~1011 #/min across all tested combinations, varying primarily by filament material and, to a lesser extent, bed temperature. It was also shown that UFP concentrations within close or moderate proximity to some desktop 3D printer and filament combinations can exceed recommended exposure levels. The most effective control strategies for reducing pollutant concentrations emitted from desktop 3D printers wereinstalling a high-flow spot ventilation system and operating the printer in a sealed enclosure with high efficiency gas and particle filtration. Finally, the third objective of this dissertation is to improve knowledge of the fate, transport, and control of infectious diseases in indoor environments through mathematical modeling of bioaerosol transmission and infection risk. Results demonstrate that Recirculating HVAC filtration can achieve risk reductions at lower costs of operation than equivalent levels of outdoor air ventilation, particularly for MERV 13-16 filters. It was also shown that in addition to the biological characteristics of respiratory pathogens, human activities, interzonal airflows, and physical properties of bioaerosols can substantially impact the infection transmission risk. Further, the dominant pathway for influenza transmission indoors under most conditions was airborne transmission. Finally, estimations of the back-calculated quanta generation rate for influenza viruses were directly in line with the existing data gathered from prior epidemiology studies.
Ph.D. in Environmental Engineering, December 2016
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Performance and NOx Emissions Control for Modern Diesel Engine and SCR Systems
Title
Performance and NOx Emissions Control for Modern Diesel Engine and SCR Systems
Creator
Sui, Wenbo
Date
2018
Description
High combustion efficiency and low emissions output are two important targets for modern diesel engine system designs and for their control...
Show moreHigh combustion efficiency and low emissions output are two important targets for modern diesel engine system designs and for their control systems. In this work, different control strategies are investigated to improve the combustion efficiency of engines and to reduce the nitrogen oxide (NOx) emissions of vehicles.There are three main contributions of this work. First, to address emissions concerns, neural network based control algorithms were applied to selective catalyst reduction (SCR) systems. Compared with conventional model-based control, the control strategy based on neural networks can reduce the amount of time and cost required for model identification for these complex systems. The neural network controllers are developed and tested in simulations at different operating conditions for the Fe-zeolite SCR system first. In addition, methods for Jacobian information prediction are also discussed. According to the simulation results, the control strategy based on neural networks can track the desired reference and have reasonable NOx reduction efficiencies in most operating conditions. However, the NOx reduction efficiencies are poor at the low temperature situations in Fe-zeolite SCR systems. To improve this issue, the neural network control strategy was applied to a Cu-zeolite SCR and an improvement in the NOx reduction efficiencies was observed with reductions over 98% at different operating conditions. Second, to address efficiency concerns, a nonlinear model-based combustion control approach was investigated. This control approach aims to track a desired optimal combustion timing and leverages a combustion phasing model for a diesel engine that was developed and validated as part of this work. An intake gas properties model is also developed to capture the cylinder-to-cylinder difference of the temperature and pressure at intake valve closing (IVC). An adaptive controller and model-based controller were then designed for the diesel engine. These control strategies are evaluated in simulations and results show that the combustion phasing control system can track the optimal CA50 (crank angle at 50% mass of fuel burned). The combustion phasing control strategies were also expanded for use on dual-fuel compression ignition engines. The dual-fuel compression ignition engine is being considered as one of the candidates for the next generation of the modern diesel engines due to its ability to achieve high combustion efficiency and low emissions. To track the optimal combustion phasing in a dual-fuel engine, a non-linear combustion phasing model for this application was also developed and calibrated based on simulations. With the control-oriented model, controllers based on an adaptive control strategy and a feedforward control strategy are designed. The controllers are evaluated and shown to track the reference CA50s at varied operating conditions.
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Modeling and Control of Boundary Constrained Granular Swarm Robots
Title
Modeling and Control of Boundary Constrained Granular Swarm Robots
Creator
Mulroy, Declan Augustine
Date
2023
Description
Soft robots offer many advantages that traditional robotic systems do not. Soft robotic systems are able to safely interact with their...
Show moreSoft robots offer many advantages that traditional robotic systems do not. Soft robotic systems are able to safely interact with their environment and tolerate large deformations. This is due to being composed of soft materials, which allows them to be subjected to and experience large deformations. However, they still have limitations in their maneuverability, locomotion, and force exertion. Moreover, they usually require external tethering or other specialized systems, such as pneumatic devices, to function. To address some of these limitations, a novel class of robotic systems has emerged called a boundary-constrained granular swarm robot.A boundary-constrained granular swarm robot is composed of a closed-loop series of active sub-robots, each with the ability to locomote. Each sub-robot is connected to its neighbors with an elastic membrane, which forms a single robot. The membrane encloses a passive granular interior, which provides structure and allows the robot to switch between rigid and soft states via granular jamming phase transitions. This allows for the robotic system to exploit the desirable characteristics of both soft and rigid robots. However, there is limited research with regards to modeling and controlling this system due to its novelty. This thesis addresses this gap by presenting several simulation frameworks, which incorporates multi-body dynamics and non-smooth contact dynamics to model the forward dynamics of the system. These models are able to account for the frictional effects, and the contact forces experienced by the system. The developed models are verified through experimental prototypes to ensure the models are able to capture the general behaviors of the system. Additionally, gradient-based control algorithms are presented and applied to simulated and experimental systems to have each of them form arbitrary shapes, morph between shapes, grasp arbitrarily shaped objects, and navigate narrow corridors. All of these objectives have been accomplished in previous systems, however, this thesis will demonstrate this system is one of the first to be able to accomplish all four. Moreover, it is able to by using a single control framework. In addition, this thesis will present the application distance functions, R-functions, and space-time transfinite interpolation for control purposes. These techniques are commonly utilized in graphics and animation theory, and will be applied to gradient-based controllers. These controllers will be used for boundary constrained granular swarms to form desired shapes and morph between shapes in both 2D and 3D simulated systems and experimental systems. Moreover, this thesis will explore the use of grasping metrics for boundary-constrained granular swarms. The Ferrari Canny metric, a well-established tool for assessing grasp quality in robotic manipulators, is utilized to evaluate the system’s grasp performance. This thesis will also demonstrate the application of this metric for boundary-constrained swarms to find the optimal angle of approach for the system to grasp a target object.
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Development of Granular Jamming Soft Robots from Boundary Constrained to Interconnected Systems
Title
Development of Granular Jamming Soft Robots from Boundary Constrained to Interconnected Systems
Creator
Tanaka, Koki
Date
2023
Description
This dissertation provides a detailed study on the conceptualization, creation, and optimization of a unique, interconnected soft robot system...
Show moreThis dissertation provides a detailed study on the conceptualization, creation, and optimization of a unique, interconnected soft robot system. It introduces a flexible assembly of locomotive robotic modules interconnected by an envelope, capable of granular jamming. In doing so, it highlights the practical capabilities of these interconnected modules to adapt and function cohesively as a single robot system.As a precursor to the primary investigation, the study initially presents the development and experimental validation of a boundary constrained mobile soft robot. This design leverages granular jamming for locomotion and object grasping, thereby laying a robust foundation for the subsequent exploration of complex soft robotic systems.The cornerstone of this study is the development of an interconnected soft robot system, where locomotive robotic modules, primarily composed of an elastic material, are bound together by a flexible envelope designed for granular jamming. The robotic modules, fundamentally constructed from an elastic material, incorporate origami-inspired artificial muscle actuators. These actuators, with their semi-soft characteristics, complement the inherent flexibility of the modules and play a significant role in facilitating module propulsion. Although the design incorporates a traditional rigid power source, as opposed to a fully soft robot system, the integration of a pneumatic power method into the system successfully reduces the mechanical intricacy and unwieldiness typically associated with rigid mechanisms.This research further probes into the diverse applications of this interconnected soft robot system. Its ability to shape-shift and maintain these forms during locomotion exemplifies a robust control strategy for the system that may undergo substantial deformation, proving instrumental in dynamic environments. The study demonstrates a methodology for object manipulation and obstacle avoidance that does not rely heavily on precise control and sensing. Instead, it utilizes the inherent compliance of the soft robot system. In a notable departure from previous studies, the system also exhibits a unique capability for ascending and traversing inclined surfaces.Additionally, the study dives into the optimization of the interconnected robot system via a physics-based simulation and genetic algorithm. This approach results in an assortment of optimized configurations that excel in object grasping tasks of various shapes, thereby laying a robust groundwork for the progression of soft robotics in the future.In conclusion, this investigation reveals groundbreaking insights into the field of soft robotics through the successful design and optimization of an interconnected soft robot system. Its standout performances in deformation, manipulation, and navigation tasks set it apart. This work serves to significantly enhance the adaptability and functionality of future robotic systems, pushing the edge of what is possible across a diverse range of sectors. By portraying a significant step towards a future where robots can dynamically adapt to their environments and efficiently accomplish complex tasks, this dissertation exemplifies a transformative stride in the field.
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