https://etd.auburn.edu/handle/10415/2 Wed, 20 May 2026 11:42:32 GMT 2026-05-20T11:42:32Z https://etd.auburn.edu/handle/10415/10416 Tube MPC for Robust Lateral Control of a Class 8 Tractor-Trailer with Parameter Uncertainty Ellison, Evan This thesis presents a Model Predictive Control (MPC) design for motion planning and control of a five axle tractor-trailer vehicle. The targeted use case for this design is for Society of Automotive Engineers (SAE) Level 3-4 features which may include automated highway driving and lane change or obstacle avoidance maneuvers. Autonomous control of commercial tractor trailer vehicles, specifically class 8 trucks, presents unique challenges due to the need for specific safety guarantees and lack of accurate knowledge of all of the model parameters, such as the mass and yaw inertia of the payload in the trailer. These challenges can be handled in part by MPC due to its ability to enforce constraints and find optimal trajectories with respect to an objective. Additionally, many techniques for ensuring constraints are satisfied under uncertainty exist which can prove useful for this application. In this thesis, a commonly used dynamic model for tractortrailers is first presented. Next, a full prediction model for use in the MPC is developed, which combines the equations for propagating position with respect to the road and a model for the steering actuator with the lateral dynamic model of the vehicle. An MPC design is then introduced by defining the optimal control problem and solving it as a Quadratic Program (QP). The MPC is able to plan and execute a trajectory that ensures constraints related to the vehicle’s position and trailer states such as the hitch angle can be met. A higher update rate feedback controller is used to aid the tracking of the latest solution between MPC updates. A constraint tightening technique is also applied which constructs an error tube around the planned trajectory based on the uncertainty of model parameters. An analysis of the total accuracy and performance in different scenarios is presented. The combined online planning and control scheme is validated in simulation, and the MPC performance with and without constraint tightening is compared for several relevant scenarios, and improvements in the number of scenarios that satisfy the lateral position and hitch angle constraints is demonstrated. Finally, the real-world capability of the design is demonstrated on an autonomous Peterbilt 579 with a trailer attached. The experimental testing demonstrates the feasibility of the concept for real-time control through lane keeping tests, resulting in absolute tracking errors with at most a mean of 18.9 cm and a standard deviation of 11.9 cm. Tue, 19 May 2026 00:00:00 GMT https://etd.auburn.edu/handle/10415/10416 2026-05-19T00:00:00Z https://etd.auburn.edu/handle/10415/10415 Design and Analysis of a Hot Start Vector Tracking Receiver Hubbard, Hendrix This thesis presents a novel method of initializing vector processing on a software-defined Global Positioning System receiver. By leveraging observables and ephemeris data obtained from a base station, the proposed receiver is able to instantly begin vector tracking. Conventional vector tracking approaches require an initial position estimate and decoded ephemeris data to commence processing, which is typically provided by scalar tracking. The proposed method overcomes this limitation and generates an initial position estimate without prior knowledge of the signal or receiver states. The receiver architecture integrates additional correlators located beyond the conventional half-chip range of the GPS L1 replica spacing vector. These extended correlators provide key insight into the signal power distribution, allowing the tracking loop to accurately align the code replica even when the initial position estimate is inadequate or the code phase lies outside the typical Delay Lock Loop pull-in region. This capability is particularly advantageous in dynamic or degraded environments where robust tracking is essential. By combining base station assisted initialization with extended correlator design, the proposed approach effectively merges the rapid time-to-first-fix of hot start methods with the robustness of vector tracking. The resulting system provides a computationally efficient, practical, and reliable solution for GPS receivers operating in a variety of signal conditions. In this thesis, the HSVT algorithm results show state convergence in under 1 second for static receivers and under 2 seconds for dynamic receivers, demonstrating performance comparable to existing hot start methods. These convergence times are validated through both experimental live-sky scenarios and simulated Monte Carlo analyses. Thu, 14 May 2026 00:00:00 GMT https://etd.auburn.edu/handle/10415/10415 2026-05-14T00:00:00Z https://etd.auburn.edu/handle/10415/10414 Exploring the Metacognitive Model: A Comparative Study of Anxiety, Cannabis Use, and Comorbid Conditions Gorday, Julia The metacognitive model of emotional disorders is a transdiagnostic theory that suggests that psychopathology (e.g., anxiety- and fear-related disorders) occurs as a result of metacognitive beliefs (i.e., beliefs about one’s own thinking) and the subsequent activation of the Cognitive Attentional Syndrome (CAS; i.e., set of maladaptive self-regulation strategies; Wells & Matthews, 1996). A recent adaption of the metacognitive model (i.e., the metacognitive formulation of substance use) proposes that specific metacognitive beliefs about substance use lead to the development and maintenance of problematic substance use. Despite the abundance of literature that has examined the metacognitive model, no known study has compared the components of the metacognitive model across individuals with anxiety- and fear-related pathology and cannabis users. Further, no study to date has examined the metacognitive formulation of substance use (i.e., negative and positive metacognitive beliefs) in cannabis users. In an effort to fill these gaps in the literature, the present study sought to compare components of the metacognitive model (i.e., metacognitive beliefs and CAS activation) across participants with anxiety- and fear-related pathology, regular and frequent cannabis users, and healthy controls. Further, the present study aimed to evaluate the psychometric properties of an adapted measure of metacognitive beliefs about cannabis use. Adult participants (N = 46) completed a clinical interview to determine eligibility for one of three diagnostic groups and completed a subsequent battery of self-report measures. Results revealed that individuals with anxiety- and fear-related psychopathology and regular, frequent cannabis users may experience greater levels of metacognitive model components compared to healthy controls. However, this effect may be driven by anxiety- and fear-related pathology. Moreover, the present study evidenced positive associations between negative metacognitions about cannabis use and cannabis use frequency and problems. Present findings point to the potential benefits of treatment options that target generic metacognitive beliefs, CAS activation, and negative metacognitions about cannabis use. Given notable study limitations, future work is needed to confirm and expand upon present findings. Wed, 13 May 2026 00:00:00 GMT https://etd.auburn.edu/handle/10415/10414 2026-05-13T00:00:00Z https://etd.auburn.edu/handle/10415/10413 Adaptive Human–Robot Collaborative Assembly: Integrating Planning, Behavior, and Gaze Schirmer, Fabian Human–robot collaborative assembly has high potential for manufacturing with high product variety, frequent product changes, and small production volumes. In such settings, assembly systems must be fexible. Rigid and highly specialized automation is often not suitable. Instead, assembly processes must be adapted quickly to new products, variants, and changing task conditions. Human–robot collaboration combines human fexibility and decision-making with robotic precision, repeatability, and physical support. However, this requires effcient planning, reliable interpretation of human behavior, and adaptive robot responses in dynamic environments. This thesis addresses these challenges and presents key approaches for adaptive human–robot collaborative assembly. First, an Extract–Enrich–Assess–Plan–Review (E2APR) framework is introduced to automate the generation of assembly sequence plans from heterogeneous engineering data, including CAD models, technical drawings, and assembly instructions. The framework supports task allocation between humans and robots, expert-guided refnement, and the generation of multiple collaborative assembly strategies. Second, this thesis presents an anomaly detection framework for collaborative assembly based on an LSTM autoencoder. Instead of explicitly classifying all possible worker actions, the system learns normal assembly behavior and detects deviations during execution. By combining reconstruction-error-based anomaly detection with object detection and the Assembly Sequence Plan, the framework distinguishes between valid alternative assembly paths and actual assembly errors. Third, a gaze-based intention recognition approach is proposed to enable more proactive collaboration. Eye gaze is interpreted as a non-verbal signal of worker attention and intention and is categorized into fxation, scanning, and task-switching behaviors. Experimental results demonstrate promising classifcation performance across all three categories, indicating that gaze behavior can provide useful contextual information for anticipating human actions. Finally, the thesis investigates adaptive robot path planning and communication in shared workspaces. Human arm movements are integrated as dynamic obstacles into the planning scene, and robot state changes are communicated through visual, auditory, and light-based modalities. A pilot user study indicates that communication does not negatively affect execution time, while light-based feedback reduces perceived frustration. In total, this thesis contributes a coherent approach for linking planning, perception, and interaction in human-robot collaborative assembly. The presented methods provide a foundation for collaborative systems that are not only automatically planned, but also capable of interpreting human behavior and adapting robot actions in a transparent and human-centered manner. Mon, 11 May 2026 00:00:00 GMT https://etd.auburn.edu/handle/10415/10413 2026-05-11T00:00:00Z