When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.
Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.
As the field of robotics extends its reach from traditional manufacturing automation to domains like healthcare, field exploration, and collaborative human-robot manipulation, the significance of soft robots becomes increasingly evident. Soft robots possess the unique ability to undergo substantial deformations and navigate through unstructured and constrained environments. Importantly, they achieve this without inducing detrimental internal pressures and stress concentrations.
This emergence of soft robots represents a new frontier at the intersection of multiple disciplines, encompassing engineering, materials science, mechanics, physics, chemistry, biology, and robotics. The convergence of these fields is driving innovation and redefining the boundaries of what robots can achieve, opening up new avenues for exploration and practical applications.
Among the materials for soft robots, Dielectric Elastomer (DE) enabled actuators present themselves as promising candidates for utilization in soft robotics applications, primarily due to their outstanding performance characteristics. Nonetheless, owing to the intricate nonlinear nature of Dielectric Elastomer Actuators (DEAs), which encompass complexities such as hysteresis with numerous dependencies, the task of modeling and controlling DEAs becomes inherently demanding.
This dissertation aims to develop suitable modeling and control strategies for the DEAs with the purpose of using them in soft robot applications.
Firstly, a comprehensive set of experimental tests has been carried out to analyze the input-output characteristics of DEAs across varying input amplitudes, frequencies, and mechanical loads. The experimental results have undergone meticulous scrutiny, yielding significant insights. The experimentation reveals the following observations: (1) The input-output responses of DEAs are marked by their intricate and complex nature; (2) The observed responses exhibit notable dependency on factors such as input frequencies, input amplitudes, and external mechanical loads.
Focusing on conical DEAs and planar DEAs, this dissertation introduces two distinct models derived from the fundamental principles of physics. Drawing inspiration from the concept of free energy within viscoelastic materials, these proposed models are designed to encapsulate the intricate behaviors exhibited by DEAs, encompassing their complex dependencies. Notably, these models are adept at capturing the interplay of multiple factors influencing DEA behaviors. The accuracy and efficacy of the proposed models are substantiated through the scrutiny of experimental results.
The models outlined above demand an extensive array of actuator-specific details, necessitating the development of separate models for distinct actuators, including detailed geometry specifications. To circumvent this limitation, a more versatile approach is presented: a data-driven model. This innovative model employs an array of nonlinear blocks, encompassing features such as creep and hysteresis, thereby enabling the representation of intricate DEA behaviors without relying on geometry-specific information. By employing this approach, the model sidesteps the requirement for actuator-specific details and can effectively capture the multifaceted behaviors of DEAs.
The nonlinear effects exhibited in DEAs can lead to adverse consequences that undermine their performance, resulting in inaccuracies and oscillations. To counteract these detrimental effects, a strategic approach is employed: the application of feedforward inverse compensation methods for controller design. Anchored in this approach, a model founded on PI hysteresis blocks is deployed to effectively characterize the nonlinear impact within DEAs. The direct inverse compensation technique is then harnessed to deduce the inverse of the PI model. Building upon this foundation, a robust adaptive controller is meticulously formulated. By leveraging this comprehensive methodology, the aim is to mitigate the adverse impacts stemming from nonlinearities in DEAs, ultimately enhancing their control performance and ameliorating the challenges posed by dynamic behaviors.