Acoustics, Dynamics, and Controls Commons^™

Versatile Exoskeleton Control Paradigms For Agile Human Locomotion: Task-Agnostic And Stability-Augmented Assistance, Miao Yu

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Lower-limb exoskeletons have shown great potential for assisting human locomotion, but designing controllers that provide assistance across diverse locomotor tasks and under external perturbations remains a major challenge. Many existing controllers for steady-state walking rely on pre-defined reference trajectories, which constrain voluntary human motion and limit adaptability across tasks. Moreover, they usually assume stable walking, while their performance under unstable conditions remains largely unexplored. In addition, existing gait stability augmentation controllers are often reactive rather than proactive to unstable conditions. In this dissertation, we propose control frameworks that preserve voluntary human motion while addressing two major challenges: providing task-agnostic assistance …

Safe Control Design For Quadruped Locomotion In Unstructured Environments Using Linear Transfer Operators, Sriram Sundar Krishnamoorthy Shankara Narayanan

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Deploying quadruped robots in unstructured, obstacle-rich environments requires control and planning methods that remain safe and reliable despite complex terrain geometry, limited sensing, and inevitable modeling errors. This thesis develops operator-theoretic tools for safe control design of robotic systems using linear transfer operators, with a focus on quadruped locomotion in unstructured environments. The central goal is to develop a unified operator-theoretic framework for safe control design based on the Perron–Frobenius (P–F) and Koopman operators. In particular, the thesis leverages \emph{density functions} to develop safe navigation frameworks in the dual space of densities. In the operator-theoretic perspective, the P–F operator governs …

Machine Learning-Enabled Safety-Critical Model Predictive Control For Uncertain Dynamical Systems, Hossein Nejatbakhsh Esfahani

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This dissertation explores the interactions between Model Predictive Control (MPC), Safety-critical Control, and Artificial Intelligence (AI)/Machine Learning (ML) methods, with a particular focus on Reinforcement Learning (RL) and Bayesian Optimization (BO). We then leverage AI/ML to address several challenges in the control design and safe operation of uncertain dynamical systems. In many applications, ranging from autonomous vehicles and robotics to energy systems and industrial processes, ensuring safety is as essential as satisfying control objectives. The use of Control Barrier Functions (CBFs) within the MPC framework recently has emerged as a powerful tool to guarantee safety by enforcing constraints in optimal …

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

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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 …

Convex Approach To Data-Driven Optimal Control With Safety Constraints Using Linear Transfer Operator, Joseph Raphel Moyalan

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This thesis is concerned with the data-driven solution to the optimal control problem with safety constraints for a class of control-affine nonlinear systems. Designing optimal control satisfying safety constraints is a problem of interest in various applications, including robotics, power systems, transportation networks, and manufacturing. This problem is known to be non-convex. One of this thesis's main contributions is providing a convex formulation to this non-convex problem. The second main contribution is providing a data-driven framework for solving the control problem with safety constraints. The linear operator theoretic framework involving Perron-Frobenius and Koopman operators provides the convex formulation and associated …

Numerical Simulation Of Laser Induced Elastic Waves In Response To Short And Ultrashort Laser Pulses., Alireza Zarei

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In an era of intensified market competition, the demand for cost-effective, high-quality, high-performance, and reliable products continues to rise. Meeting this demand necessitates the mass production of premium products through the integration of cutting-edge technologies and advanced materials while ensuring their integrity and safety. In this context, Nondestructive Testing (NDT) techniques emerge as indispensable tools for guaranteeing the integrity, reliability, and safety of products across diverse industries.

Various NDT techniques, including ultrasonic testing, computed tomography, thermography, and acoustic emissions, have long served as cornerstones for inspecting materials and structures. Among these, ultrasonic testing stands out as the most prevalent method, …

Trust-Based Variable Impedance Control And Passivity-Based Switched System Analysis For Human-Robot Cooperative Manipulation, Zhanrui Liao

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Human-robot cooperative manipulation (co-manipulation) is one of the most prominent human-robot collaboration (HRC) tasks, where humans and robots manipulate the same object. Trust in HRC is crucial in determining human acceptance of robots and, hence, robot utilization. A probabilistic dynamic Bayesian network (DBN) trust model that integrates a time-series trust model is presented in this thesis. The trust model is learned using a continuous and normalized Baum-Welch (BW) algorithm, devised to account for the continuous nature of trust evolution and the limitations of the classic parameter learning method. To ensure a good HRC in co-manipulation, a variable impedance control framework …

Controlled Manipulation And Transport By Microswimmers In Stokes Flows, Jake Buzhardt

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Remotely actuated microscale swimming robots have the potential to revolutionize many aspects of biomedicine. However, for the longterm goals of this field of research to be achievable, it is necessary to develop modelling, simulation, and control strategies which effectively and efficiently account for not only the motion of individual swimmers, but also the complex interactions of such swimmers with their environment including other nearby swimmers, boundaries, other cargo and passive particles, and the fluid medium itself. The aim of this thesis is to study these problems in simulation from the perspective of controls and dynamical systems, with a particular focus …

Impacts Of Connected And Automated Vehicles On Energy And Traffic Flow: Optimal Control Design And Verification Through Field Testing, Tyler Ard

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This dissertation assesses eco-driving effectiveness in several key traffic scenarios that include passenger vehicle transportation in highway driving and urban driving that also includes interactions with traffic signals, as well as heavy-duty line-haul truck transportation in highway driving with significant road grade. These studies are accomplished through both traffic microsimulation that propagates individual vehicle interactions to synthesize large-scale traffic patterns that emerge from the eco-driving strategies, and through experimentation in which real prototyped connected and automated vehicles (CAVs) are utilized to directly measure energy benefits from the designed eco-driving control strategies. In particular, vehicle-in-the-loop is leveraged for the CAVs driven …

Physics-Based Machine Learning Methods For Control And Sensing In Fish-Like Robots, Colin Rodwell

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Underwater robots are important for the construction and maintenance of underwater infrastructure, underwater resource extraction, and defense. However, they currently fall far behind biological swimmers such as fish in agility, efficiency, and sensing capabilities. As a result, mimicking the capabilities of biological swimmers has become an area of significant research interest. In this work, we focus specifically on improving the control and sensing capabilities of fish-like robots.

Our control work focuses on using the Chaplygin sleigh, a two-dimensional nonholonomic system which has been used to model fish-like swimming, as part of a curriculum to train a reinforcement learning agent to …

Trust-Based Runtime Verification Of Autonomous Robotic Systems, Maziar Fooladi Mahani

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 Trust plays a crucial role in enabling effective collaboration and decision-making within human-multi-robot teams. In this context, runtime verification techniques and trust inference models have emerged as valuable tools for assessing and quantifying the trustworthiness of individual robots. This dissertation presents a study on trust-based runtime verification and introduces a Bayesian trust inference model for human-multi-robot teams. Firstly, we discuss the concept of runtime verification, which involves monitoring and analyzing the behavior of robots during their operation. We highlight the importance of trust as a key factor in determining the reliability and credibility of robot actions. By integrating trust metrics …

Vibration-Based Fault Diagnostics In Wind Turbine Gearboxes Using Machine Learning, Abdelrahman Amin

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A significantly increased production of wind energy offers a path to achieve the goals of green energy policies in the United States and other countries. However, failures in wind turbines and specifically their gearboxes are higher due to their operation in unpredictable wind conditions that result in downtime and losses. Early detection of faults in wind turbines will greatly increase their reliability and commercial feasibility. Recently, data-driven fault diagnosis techniques based on deep learning have gained significant attention due to their powerful feature learning capabilities. Nonetheless, diagnosing faults in wind turbines operating under varying conditions poses a major challenge. Signal …

Modeling, Control And Estimation Of Reconfigurable Cable Driven Parallel Robots, Adhiti Raman Thothathri

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The motivation for this thesis was to develop a cable-driven parallel robot (CDPR) as part of a two-part robotic device for concrete 3D printing. This research addresses specific research questions in this domain, chiefly, to present advantages offered by the addition of kinematic redundancies to CDPRs. Due to the natural actuation redundancy present in a fully constrained CDPR, the addition of internal mobility offers complex challenges in modeling and control that are not often encountered in literature.

This work presents a systematic analysis of modeling such kinematic redundancies through the application of reciprocal screw theory (RST) and Lie algebra while …

Multiple Heat Exchanger Cooling System For Automotive Applications – Design, Mathematical Modeling, And Experimental Observations, Zaker Syed

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The design of the automotive cooling systems has slowly evolved from engine-driven mechanical to computer-controlled electro-mechanical components. With the addition of computer-controlled variable speed actuators, cooling system architectures have been updated to maximize performance and efficiency. By switching from one large radiator to multiple smaller radiators with individual flow control valves, the heat rejection requirements may be precisely adjusted. The combination of computer regulated thermal management system should reduce power consumption while satisfying temperature control objectives. This research focuses on developing and analyzing a multi-radiator system architecture for implementation in ground transportation applications. The premise is to use a single …

Infusing Kirigami Principles Into Design Of Mechanical Properties, Hesameddin Khosravi

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The emergence of mechanical metamaterials — which derive their properties primarily from the underlying architecture rather than the constituent material — has unleashed a new era of material design and functionalities. To fully materialize the promising potentials of metamaterials, it is crucial to develop versatile, scalable, and easy-to-fabricate methods that can both generate and tailor the underlying periodic architecture. To this end, we propose the use of kirigami — a popular recreational art of cutting and manipulating paper — as a platform to create periodicity and super-stretchability. Kirigami has become a design and fabrication framework for constructing metamaterials, robotic tools, …

Multi-Robot Symbolic Task And Motion Planning Leveraging Human Trust Models: Theory And Applications, Huanfei Zheng

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Multi-robot systems (MRS) can accomplish more complex tasks with two or more robots and have produced a broad set of applications. The presence of a human operator in an MRS can guarantee the safety of the task performing, but the human operators can be subject to heavier stress and cognitive workload in collaboration with the MRS than the single robot. It is significant for the MRS to have the provable correct task and motion planning solution for a complex task. That can reduce the human workload during supervising the task and improve the reliability of human-MRS collaboration. This dissertation relies …