Presentation + Paper
13 March 2019 Modelling and control of self-sensing ionic electroactive polymer actuator
Author Affiliations +
Abstract
Electroactive polymer (EAP) is a kind of smart material that exhibits a large deformation on the application of a small potential across the electrodes. On the contrary, the materials can also exhibit sensorial behavior by generating a small electrical potential on the application of mechanical force. These characteristics make these materials to be a promising candidate for applications involving actuators with self-sensing ability. In this work, we report on the development of integrated sensing and actuation of ionic polymer–metal composite. Integrated sensing is accomplished by crafting discrete sensing and actuation sections over a single device by patterning the surface of the electrodes. A control scheme and estimation technique are implemented for self-sensing feedback control that uses the electrode resistance change during deformation. The artificial neural network is used to handle the hysteresis during modelling the relation between electrode resistance change and actual tip displacement. While the need for stable control to overcome nonlinearity and inherent back relaxation behavior of the material is accomplished by using a robust sliding mode controller. The developed model and controller are experimentally verified and found to be capable of predicting and controlling the actuators with excellent tracking accuracy without the need for a separate position sensor and makes the device to perform as a self-sensing actuator.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sunjai Nakshatharan S., Andres Punning, Urmas Johanson, and Alvo Aabloo "Modelling and control of self-sensing ionic electroactive polymer actuator", Proc. SPIE 10966, Electroactive Polymer Actuators and Devices (EAPAD) XXI, 109661C (13 March 2019); https://doi.org/10.1117/12.2514202
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CITATIONS
Cited by 1 scholarly publication.
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KEYWORDS
Electrodes

Actuators

Sensors

Resistance

Neural networks

Control systems

Data modeling

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