This paper presents an experimental comparison of three different biasing elements utilized to produce out-of-plane actuation for a diaphragm dielectric electro-active polymer (DEAP). A hanging mass, a linear coil spring, and a nonlinear (bistable) mechanism are individually paired with an unloaded DEAP actuator. High voltage (2.5 kV) is applied to the DEAP and the out-of-plane stroke of the DEAP is measured. The actuator stroke is notably different for each bias element. Results show that as the bias element stiffness increases, the actuator stroke decreases. However, the bistable element, when coupled with the DEAP, demonstrated improved actuation in a specific range of DEAP pre-deflection. Not only was the stroke larger for this case, the stroke also did not attenuate at higher voltage frequencies as much as the linear coil spring bias elements. This study demonstrates a promising method for obtaining high performance DEAP actuators in the future.

This paper presents a modeling approach of an actuator system based on a dielectric electro-active polymer (DEAP) circular membrane mechanically loaded with a mass and a linear spring. The motion is generated by the deformation of the membrane caused by the electrostatic compressive force between two compliant electrodes applied on the surface of the polymer. A mass and a linear spring are used to pre-load the membrane, allowing stroke in the out-of-plane direction. The development of mathematical models which accurately describe the nonlinear coupling between electrical and mechanical dynamics is a fundamental step in order to design model-based, high-precision position control algorithms operating in high-frequency regimes (up to 150 Hz). The knowledge of the nonlinear electrical dynamics of the actuator driving circuit can be exploited during the control system design in order to achieve desirable features, such as higher modeling accuracy for high-frequency actuation, self-sensing or control energy minimization. This work proposes a physical model of the DEAP actuator system which couples both electrical and mechanical dynamics occurring during the actuation process. By means of numerous experiments, it is shown that the model can be used to predict both actuator current and displacement, and therefore to increase the overall displacement prediction accuracy with respect to actuator models which neglect electrical behavior.

No abstract

Dielectric electro-active polymer (DEAP) technology holds promise for enabling lightweight, energy efficient, and scalable actuators. The circular DEAP actuator configuration (also known as cone or diaphragm actuator) in particular shows potential in applications such as pumps, valves, micro-positioners and loudspeakers. For a quantitative prediction of the actuator behavior as well as for design optimization tasks, material models which can reproduce the coupled electromechanical behavior inherent to these actuators are necessary. This paper presents a nonlinear viscoelastic model based on an electro-mechanical Ogden free energy expression for the DEAP. The DEAP model is coupled with a spring/mass system to study the dynamic performance of such a representative system from static behavior to 50 Hz. The system is identified and validated by several different experiments.

This paper presents modeling and experimental validation of a small profile, scalable DEAP actuator system. The actuator system consists of a bi-stable mechanism (a negative-rate bias spring, or NBS) coupled with an out-of-plane dielectric electro-active polymer (DEAP). The NBS biases the DEAP allowing actuation when the voltage is cycled and is shown to have a major impact on the overall system performance. Particularly in comparison with conventional linear springs, the NBS-biased actuator exhibits a considerably larger displacement stroke. A first order model of the NBS-DEAP coupled system is developed based on minimization of the system's potential energy. This approach allows for the determination of quasi-static force equilibria in the presence of multiple stable positions. The model is validated with experimental data and provides insight into system trends and related parameter optimization.