Controls and Control Theory Commons^™

Control System Emendation And Ai-Driven Optimization For Enhancing A Smart Residential Microgrid, Anthony Nyoyoko

Master’s Theses

This thesis began with a simple idea: to restore the control system of a smart residential microgrid to respond intelligently to electricity prices while remaining safe, reliable, and practical on low-cost hardware. At the start, the goal was to design an economically aware microgrid that could look at electricity prices from the PJM market and make better operational decisions than traditional rule-based control. The motivation was straightforward. As residential renewable energy adoption increases, microgrids are expected to do more than just supply power. They are expected to respond to price volatility, integrate renewable generation, and operate reliably using embedded controllers …

Brrbox, Shawn J. Myers, Lane Cline, Michael Davis, Christian Secrest

Williams Honors College, Honors Research Projects

This report details the project known as “The BRRBOX”, a reusable, insulated thermoelectric cooler developed to keep internal temperatures at refrigeration levels or cooler for at least 48 hours. The cooler will track its internal temperature during this period and be able to give the data at the end of its delivery cycle to keep up with food and pharmaceutical standards during delivery. The BRRBOX uses Peltier-based cooling alongside vacuum insulation panels and fans to achieve efficient thermal control. An onboard microcontroller will monitor temperature, record the data, and adjust the cooling output to minimize power consumption. The box will …

Timing And Stability Of Uav Operations In Smart City Environments: Experimental Analysis And Design Implications, Rabia Ipek Yasar

Theses and Dissertations

This thesis investigates the deployment and performance of unmanned aerial vehicles (UAVs) within the Virginia Commonwealth University Open Cyber City (OCC) testbed. The study focuses on evaluating real-time indoor positioning performance using the Crazyflie drone platform. High-precision position measurements are obtained using the Vicon motion capture system, enabling analysis of the latency between the drone’s actual position and the system-reported position. In a closed-loop control system, the time difference between the position measurement and the application of the control command is called the system delay. Both stationary (hovering) and trajectory-following experiments are conducted to evaluate system performance. Communication delays in …

Robotizing Complex Welding Processes Through Imitation Learning And Generative Models From Human Demonstration, Yue Cao

Theses and Dissertations--Electrical and Computer Engineering

Arc welding processes demand real-time adaptive control that current robotic systems cannot achieve autonomously. This dissertation develops a systematic framework to robotize complex welding by learning from human demonstration, integrating generative modeling, physics-informed reconstruction, and model-based imitation learning. First, human--robot collaboration systems are established for both Gas Tungsten Arc Welding (GTAW) and Double-Electrode Gas Metal Arc Welding, combining robotic teleoperation with virtual reality interfaces to capture high-quality operator demonstrations. Second, a physics-informed neural network framework reconstructs complete molten pool flow fields from high-speed imaging, enriching process understanding beyond direct sensor observation. Third, generative models, including a hybrid latent variational autoencoder …

End-To-End Development And Experimental Validation Of A 1/10-Scale Autonomous Vehicle, Rikkin Pankaj Panchal

Electrical Engineering Theses

Autonomous vehicle development demands vast resources, making scaled down platforms a critical alternative for solving core algorithmic challenges. The primary contribution of this thesis is the end to end development and validation of a complete real time autonomous driving pipeline deployed on a one tenth scale vehicle. To streamline platform development, an AI assisted annotation framework automates dataset generation, significantly reducing manual labor while improving training data quality. The system perception stack features a reinforcement learning guided online multi camera calibration framework that enables adaptive surround view stitching without the need for offline recalibration. This is paired with robust lane …

Multi-Level Energy Optimization For Connected And Automated Vehicles: From Cooperative Multi-Vehicle Control To Individual Powertrain Management, Pruthwiraj Santhosh

Dissertations, Master's Theses and Master's Reports

The transportation sector currently accounts for nearly 30% of global energy consumption, necessitating urgent advancements in vehicle efficiency to meet Net Zero targets. Leveraging connectivity and automation, this dissertation proposes and validates methodologies to reduce the energy consumption of light-duty vehicles at both fleet and individual levels.

First, a validation framework is developed to bridge the “simulation-to-real world” gap in Cooperative Automated Vehicle (CAV) research. Moving beyond virtual simulations, the study establishes a methodology for physically validating centralized control architectures via a custom Cellular V2X network. By synchronizing vehicle-powertrain models with physical test vehicles, the framework successfully orchestrates complex arterial …

Experimental Rate Feedback Control Of A Model-Scale Hourglass-Shaped Heaving Point Absorber, James R. Halverson

Dissertations, Master's Theses and Master's Reports

Buoy geometry greatly affects a point absorber wave energy converter's dynamic response to waves. Finding the optimal buoy shape and control method remains an open research area focused on maximizing the conversion of wave kinetic energy into electricity. This work presents an experimental comparison of closed-loop energy extraction between a cylindrical and a truncated cone buoy, both with the same submerged volume, across various wave frequencies and amplitudes. To ensure a fair comparison, the optimal rate feedback gain is calculated for each buoy at each wave condition. Multiple metrics, including power output, capture width, and actuator force, are used to …

Learning - Augmented Stochastic Model Predictive Control For Adaptive Battery Energy Storage Operation Under Net Load Uncertainty, Muhammad Amad Asif

Electronic Theses & Dissertations (2024 - present)

Battery energy storage systems play a critical role in enabling reliable operation of power systems with high penetration of renewable energy. However, optimal control of BESS is challenging due to uncertainty in net load and electricity prices. Deterministic MPC, which relies on point forecasts, can perform suboptimally under forecast errors. SMPC addresses this limitation by incorporating uncertainty through scenario-based optimization. Nevertheless, its performance depends on fixed objective parameters, particularly the cycling penalty, which governs the trade-off between economic cost and battery utilization. This thesis develops a learning-augmented SMPC framework for battery control under net load uncertainty. The proposed approach integrates …

Automation And Habitat Development For A Space Based Marine Life Environment, Logan Trimmer, Alexander Hang

Harrisburg University Other Works

This project was a cross-collaboration between the Environmental Sciences and Advanced Manufacturing and Robotics programs for the company Monolith Space.

The goal of this project was to design an autonomous system to be able to track qualities of water in an aquaculture system designed to be sent to space.

Study Of The Influence Of Material Heterogeneities In Humidity Measurements In A Closed Volume, Hulkar Asadullayevna Usmanova, Asadulla Turgunbaev, Umid Uralovich Xudaykulov

Technical science and innovation

The article presents generalized theoretical studies of the errors arising in the conversion of material moisture into informative parameters of the microwave field. Such informative parameters of a material include structural inhomogeneities, non-uniform moisture distribution throughout the volume and by the forms of moisture bonding, density, temperature, and mass. The influence of material inhomogeneities during moisture measurements in an enclosed volume on the errors of primary measuring transducers is investigated, along with the errors accompanying the conversion of moisture into the dielectric characteristics of the material and the conversion of dielectric characteristics into informative parameters of the microwave field. A …

Improvement Of Technology And Control And Management Systems Of The Plant Oil Refination Process, Elyor Samadov

Chemical Technology, Control and Management

The article examines modern approaches to modernizing vegetable oil refining processes from the perspective of increasing energy efficiency, environmental friendliness, and final product quality. Traditional and innovative technologies are analyzed, with a focus on the implementation of membrane filtration, automated control systems, heat exchange complexes, and renewable energy sources. It has been shown that the use of intelligent control systems, high-precision membrane filters, and heat regeneration allows for a 25% reduction in energy consumption, a 20% reduction in greenhouse gas emissions, and a significant improvement in acidity, color, and impurity content in the product. The obtained results show the effectiveness …

Analog Systems Of Physical Quantities And Their Graph Models In Determining The Parameters Of Mechatronic Modules, Temurbek Omonboevich Rakhimov, Elmira Eshmurod Qizi Raxmanova, Guzal Khujaniyazova

Chemical Technology, Control and Management

This article is dedicated to the analogy systems of physical quantities and their graph models in determining the parameters of mechatronic modules. It presents the general sets and fundamental laws of analogy systems of physical quantities and their elements for determining the parameters of mechatronic modules. Based on the general sets and fundamental laws of analogy systems of physical quantities and their elements, principles have been developed for mechatronic modules with a heterogeneous structure consisting of electrical, magnetic, and mechanical parts. In these modules, the field as a form of matter exists, forming the basis for developing mathematical models to …

Improving Self-Diagnostic Methods Of Flow Measurement Systems Based On Artificial Intelligence, Elbek Ortikov

Chemical Technology, Control and Management

The article examines methods for improving self-diagnostics of consumption measurement systems based on artificial intelligence in the context of industry digitalization and the development of cyber-physical systems. It has been shown that traditional flow meters used to measure the flow rate of liquids and gases are subject to mechanical, hydraulic, electronic, and hidden failures, which reduce the accuracy and reliability of measurements. A justification for the need to transition from classical maintenance methods to intelligent self-control methods that ensure the detection of anomalies and hidden malfunctions in real time is presented. A multi-level architecture of intelligent self-diagnosis is proposed, including …

Performance Assessment Of Operators Under Digital Simulator-Based Training In Automated Industrial Systems, Kamola Abdullaeva

Chemical Technology, Control and Management

This article proposes a comprehensive approach to training process operators, based on computer simulators integrated with digital twins, SCADA/DCS systems, real production data, and artificial intelligence algorithms. This approach is particularly relevant given the increased requirements for safety, productivity, and reliability of industrial facilities, as human factors are often the main cause of accidents (up to 60-80% of cases). The architecture of the simulator complex is designed to accurately emulate steady-state, transient, and pre-emergency operating modes of equipment, which are not achievable under actual production conditions. The experiment compared the training effectiveness of two groups of operators: one that used …

Expansion Of The Functionality Of Discrete Fiber-Optic Liquid Level Sensors, Azimjon Mamadaliyevich Khusanov, Yuriy Gennadyevich Shipulin

Chemical Technology, Control and Management

The article examines the problems and prospects of creating discrete fiber-optic liquid level gauges. Functional diagrams of single-channel and multi-channel discrete fiber-optic liquid level meters are presented. Examining the challenges of keeping this system running continuously reveals that sustaining its required reliability necessitates replacing malfunctioning measurement systems with operational ones. Investigations into differential equation models demonstrate that system recovery and failure events occur independently and follow a Poisson distribution pattern.

Investigation Of Nonlinear Magnetic Circuits Of Measuring Transducers With A Special Parameter Distribution Structure, Javhar Sulton O'G'Li Fayzullayev

Chemical Technology, Control and Management

The article proposes a new analytical method for investigating nonlinear magnetic circuits with a special structure of parameter distribution. The method is based on introducing into the system of nonlinear differential equations of such circuits the condition that the second derivative of the magnetic flux along the length of the circuit is equal to zero, as well as on the assumption that one of the geometric parameters of the studied magnetic circuit – the size of the air gap between the ferromagnetic rods, their thickness, width, or the linear value of the number of turns of the distributed excitation winding …

Comprehensive Mechatronic Design Methodology For 6-Dof Stationary Lower Limb Rehabilitation Exoskeleton Robot With Safety-Integrated Control Architecture, Javlonbek Rakhmatillaev Mr., Nodirbek Kimsanboyev Mr., Umidjon Takabaev Mr., Zafar Juraev3 Dr.

Chemical Technology, Control and Management

This paper presents the mechatronic design of a proposed six-degree-of-freedom (6-DOF) stationary lower limb rehabilitation exoskeleton (SLLRE) robot, developed through a comprehensive mechatronic design methodology that includes mechanical design, actuator and sensor selection, and the development of a control structure based on a safety-oriented control approach. The proposed 6-DOF SLLRE mechanical design introduces a comprehensive mechanical structure comprising bilateral hip, knee, and ankle joints, degrees of freedom, joint ranges of motion, human parameters for rehabilitation training, as well as the frames and structural elements of the proposed system. Furthermore, the selection of appropriate sensors, actuators, and control architecture for the …

Design Of A Fuzzy Set-Point Tracking Controller Based On The State-Dependent Control Characteristics Of Fuzzy Logic And Pid For Temperature Control Systems In Electrical Equipment, Rahim Mammadzada

Chemical Technology, Control and Management

This paper presents a Fuzzy-SPT (Fuzzy Set-Point Tracking) controller designed to combine the strengths of FLC (Fuzzy Logic Control) and PID (Proportional-Integral-Derivative) control while mitigating their limitations across varying operating conditions. Inspired by state-dependent reasoning but implemented within a single controller, the method uses internal logic to adapt the control effort across different operating regions. FLC handles rapid changes and nonlinear transients, enabling fast system response without overshoot, while PID-like integral action is activated when the FLC reaches steady state or enters a threshold region near the set point, eliminating residual errors and maintaining stable performance. By applying each behavior …

Empirical Analysis Of Machine Learning Models For Predicting Equipment Failures Using Iot Sensor Data, Yusuf Shodiyevich Avazov

Chemical Technology, Control and Management

This article examines the problem of detecting and predicting industrial equipment faults using IoT sensor data through machine learning techniques. Sensor readings such as temperature, vibration, pressure, voltage, and current, as well as FFT-based features, were statistically analyzed. Class imbalance and low signal informativeness were identified as key factors limiting model accuracy. Results obtained from Logistic Regression, Random Forest, and XGBoost models were comparatively evaluated, showing that when ROC-AUC values remain around 0.5, distinguishing fault and non-fault states becomes challenging. Correlation and feature-importance analyses confirmed the absence of strong dominant indicators. The findings highlight the need to improve sensor architecture …

Current Trends In Industrial Gas Furnace Control And Monitoring Systems, Utkir Uktam O'Gli Kholmanov

Chemical Technology, Control and Management

This paper presents a critical review of modern control methods applied to industrial gas-fired furnaces. Conventional PID controllers and their advanced modifications, including Advanced Process Control (APC) and cascade control schemes, are analyzed alongside Model Predictive Control (MPC) approaches. In addition, intelligent control algorithms—such as fuzzy logic and neuro-fuzzy systems (Fuzzy, ANFIS)—are examined. The study also considers advanced sensing technologies, including Tunable Diode Laser Absorption Spectroscopy (TDLAS), acoustic pyrometry, and infrared pyrometers, as well as digital twins and CFD-based modeling techniques. Particular attention is given to a comparative evaluation of control strategies based on key performance criteria, including energy efficiency, …

Skeleton-Based Human Action Recognition Using Spatio-Temporal Latent Features With A Gcn Model, Avazjon Marakhimov, Kabul Khudaybergenov, Mominov Zakhriddin

Chemical Technology, Control and Management

Owing to its resilience to visual noise and viewpoint variations, skeleton-based analysis has become a cornerstone of human action recognition research. Despite its practical significance, existing methodologies often suffer from a reliance on single-stream skeletal representations, which fail to encompass the full complexity of action features. This study introduces Latent Features for Human Action Recognition (LFHAR), a novel architecture designed to overcome these limitations by utilizing diverse spatio-temporal latent representations for improved feature extraction. The approach applies graph-based transformations to individual skeletal frames in temporal sequences, then arranges the derived graph features into spatio-temporal matrices. Evaluation of standard datasets demonstrates …

Stability Analysis Of Thermohaline Convection With A Time-Varying Shear Flow Using The Lyapunov Method, Kalin Kochnev

Honors Scholar Theses

This work applies the Lyapunov method to identify instabilities and compute the growth rate of a linear time-varying system. The linear system studied describes cold fresh water on top of hot salty water with a periodically time-varying background shear flow. A time-dependent weighting matrix is employed to construct a Lyapunov function candidate. The resulting linear matrix inequalities are discretized in time using the forward Euler method. As the number of temporal discretization points increases, the growth rate predicted by the Lyapunov method or Floquet theory, used for comparison, will converge to the same value obtained from numerical simulations. Furthermore, the …

Graph Based Planning With Guarantees, Trazon Tyrik Jimerson

Mechanical Engineering ETDs

This thesis presents two graph-based methods with formal guarantees for motion planning and routing. First, the invariant-set motion planner (ISMP), which uses constraint admissible positive invariant (CAPI) sets of closed-loop dynamics, is adapted for spacecraft attitude planning to avoid moving keep-out zones. Contributions include time bounds for maneuvers via exponential stability, a single-stage reachability graph from one-step backward reachable CAPI sets, and its multi-stage expansion to certify node safety over time. Simulations verify safe attitude control with moving obstacles. Second, we formulate a convex optimization problem over a network for evacuation planning with operational constraints such as helicopter capacity and …

Parameter Informed Reinforcement Learning For Vehicle System Identification, Nathan Schaff

Doctoral Dissertations and Master's Theses

Accurate system identification is essential for modeling and controlling vehicle dynamics. This dissertation explores the application of Parameter Informed Reinforcement Learning (PIRL) as a novel approach to system identification (SYSID). PIRL integrates prior system knowledge, such as physical parameters, into reinforcement learning (RL) frameworks to improve estimation accuracy. The study begins with an overview of traditional SYSID methods and then introduces PIRL as a modification of standard RL. The research applies PIRL to short-period aircraft dynamics, demonstrating its effectiveness in both offline and online learning frameworks. The dissertation then further explores PIRL’s utility in an indirect model reference adaptive control …

Hallucination Techniques For Self-Supervised Synthetic Datasets For Mobile Robots, Wyatt D. Colburn

Master's Theses

Classical techniques in autonomous navigation struggle in tightly constrained spaces. Machine learning has been shown to perform better in these difficult environments but most techniques require large amounts of navigation experience for training. Using a new machine learning paradigm learning from hallucination (LfH), training data can be collected in a safe environment and not require supervision. Data is collected in real time while an agent performs a random walk in free space, supervision is not required as there are no obstacles for the robot to run into. After a random walk a post processing pipeline will hallucinate a safety corridor …

Optimal & Robust Control Of A Bidirectional Dc-Dc Converter In Ev Systems, Yasser Ayeva

Electrical Engineering and Computer Science Faculty Publications and Presentations

This paper analyzes the performance of PI, LQR, and H∞ controllers for the regulation of a bidirectional buck boost converter in electric vehicle systems. To get the system state space equations, a continuous conduction average model is linearized. For analysis a PI controller will be used as baseline, the LQR controller will be used to improve transient response, and the H∞ controller will be used for the system robustness and disturbance rejection. The simulation results show that the advanced controllers surpass the PI controller in terms of overshoot, settling time, and voltage ripple, with the H∞ controller offering the best …

Robust Data-Driven Predictive Control Of Nonlinear Systems Under Modeling Uncertainty, Pegah Ghafghanbari

All Dissertations

Data-driven predictive control enables designing controllers directly from data, making it attractive for complex systems with hard-to-model dynamics. However, practical deployment is challenged by modeling inaccuracies and changing operating conditions. This dissertation develops predictive control frameworks that incorporate robustness and adaptability to address these issues in uncertain nonlinear systems.

The first part employs the Linear Parameter-Varying (LPV) framework, which represents nonlinear dynamics through simple linear form representation. To characterize the plant-model-mismatch often caused by limited data and numerical calculations, Bayesian Neural Networks (BNNs) are used, and their uncertainty estimates are integrated into two robust control approaches. The first is a …

Analysis Of A Cloud-Based Robot Motion Planning System, Yusif Mardanzade, Latafat Abbas Gardashova

Chemical Technology, Control and Management

As a result of the integration of cloud computing technologies into the field of robotics, the concept of "cloud robotics" has emerged. Unlike traditional robots, cloud-based robot systems remove computation, memory, and even some software from the local device and rely on remote resources obtained over the network. This approach ensures that robots are not limited only by their internal computing capabilities and allows them to take advantage of the wide range of opportunities offered by the cloud infrastructure. As a result, robots have access to large databases, highly parallel computing, and collective learning capabilities anytime and anywhere. In addition, …

Intelligent Decision-Making Systems In Smart Greenhouses, Muso Berdiyor Ugli Allanov

Chemical Technology, Control and Management

Smart greenhouses offer a solution to sustainable food production under climate uncertainty, yet their management often depends on fixed rules or human intuition. This study proposes an intelligent decision-making framework that integrates optimization, simulation, and a neural set into a self-learning system. By generating “conditionally real data” through simulation and evolutionary algorithms, the system can predict microclimatic changes and optimize control of water, energy, and nutrients. Continuous digital feedback enables adaptive, data-efficient operation even with limited real data. Experimental results demonstrate reduced resource use and improved yield stability, advancing the development of autonomous and resilient greenhouse ecosystems.

The Use Of Diagnostic And Restructuring Methods To Build Reliable Management Systems, Khurshid Salim Ugli Turayev

Chemical Technology, Control and Management

This article is devoted to the applied analysis of diagnostic and restructuring methods aimed at ensuring the reliability of control systems in the event of failures. The paper considers practical implementations of diagnostic and control algorithms using a servo drive setup as an example. The results of experiments are presented, demonstrating the system's behavior under various fault conditions. A comparative analysis of the effectiveness of the proposed solutions is carried out in terms of stability and operational accuracy. The obtained data confirm the feasibility of using adaptive control structures to increase the fault tolerance of technical systems. The article concludes …