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Curt S. Kothera
Researcher at Virginia Tech
Publications - 74
Citations - 1621
Curt S. Kothera is an academic researcher from Virginia Tech. The author has contributed to research in topics: Pneumatic artificial muscles & Actuator. The author has an hindex of 20, co-authored 73 publications receiving 1422 citations. Previous affiliations of Curt S. Kothera include University of Maryland, College Park & AMIT.
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Journal ArticleDOI
Design and Fabrication of a Passive 1D Morphing Aircraft Skin
TL;DR: In this article, an elastomer-fiber-composite surface layer that is supported by a flexible honeycomb structure was developed for a wing span morphing UAV.
Journal ArticleDOI
Experimental Characterization and Static Modeling of McKibben Actuators
TL;DR: In this article, a series of experiments aimed at understanding the static behavior of the actuators was conducted, and the results for three different lengths (4 in, 6 in, and 8 in), three diameters (1/8 in, 1/4 in., and 3/16 in.), and one wall thickness (1 /16 in.) at pressures ranging from 10 psi to 60 psi illustrate the key design trends seen in McKibben actuator geometry.
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Transport modeling in ionomeric polymer transducers and its relationship to electromechanical coupling
TL;DR: In this article, a computational model of transport and electromechanical transduction for ionomeric polymer transducers is developed based upon a coupled chemoelectrical multifield formulation and computes the spatiotemporal volumetric charge density profile to an applied potential at the boundaries.
Proceedings ArticleDOI
Dynamic modeling of Mckibben pneumatic artificial muscles for antagonistic actuation
TL;DR: Comparing with experimental results, simulations revealed that the proposed model gave good performance in estimating motions of the antagonistic actuation as well as the pressure variance of the Mckibben muscles.
Journal ArticleDOI
Nonlinear Control of Robotic Manipulators Driven by Pneumatic Artificial Muscles
TL;DR: In this paper, the performance of three advanced control strategies (sliding mode control, adaptive sliding mode control and adaptive neural network (ANN) control) was investigated to enable smooth and accurate motion tracking of a single degree-of-freedom pneumatically actuated manipulator.