Entwurf und Regelung von multistabilen dielektrischen Elastomerwandler-Systemen auf Basis hybrider Modellierung
This dissertation addresses the modeling, analysis, synthesis, and control of bistable dielectric elastomer (DE) transducers, a promising class of smart materials. These materials respond to electrical stimuli with a change in shape, enabling their use as actuators, sensors, or generators in a wide range of applications, including soft-material systems. By combining hyperelastic DE transducers with mechanical structural elements, DE systems with tailored electromechanical properties and even multistable behavior can be realized. A essential challenge lies in modeling the strongly nonlinear, dynamically coupled mechanisms. While physics-based continuum models offer high accuracy, they are often too complex for control and design purposes. Classical lumped-parameter models are computationally efficient but capture real effects such as friction, manufacturing tolerances, or aging only to a limited extent. In this work, a hybrid modeling approach is developed that combines physics-based continuum models derived from Hamilton’s principle with data-driven artificial neural networks. Furthermore, an extended friction model and optimization methods are employed to parameterize the models and accurately represent actually occurring nonlinear effects. The proposed modeling approach is applied to two systems: a novel bistable dielectric elastomer generator and a specially designed bistable DE actuator. For both DE-systems, lumped-parameter models are developed. The generator employs a mechanical structure that, through equibiaxial stretching under uniaxial loading, enables a larger capacitance change and thus higher energy harvesting. Magnetically realized springs create mechanical bistability with reduced and adjustable stiffness. The dynamic model of the bistable DE actuator is additionally extended with data-driven components. Both DE-systems exhibit multiple stable equilibrium positions, which are deliberately exploited in both generator and actuator operation to highlight advantages and potential applications. For the bistable DE actuator, a model predictive control (MPC) scheme is developed, including both classical MPC and an adaptive variant with online parameter estimation (Dual-MPC). A capacitive sensing concept with a state observer is integrated to enable precise position control without the need for complex external sensors. The results demonstrate that combining physics-based and data-driven modeling enables realistic yet computationally efficient descriptions of complex DE systems. The developed control strategies allow robust and precise position control of nonlinear, bistable DE transducers and show potential for broader use in advanced mechatronic systems.
Networked Digital Library of Theses and Dissertations