Showing papers by "Vaughn College of Aeronautics and Technology published in 2023"

Variationally consistent modeling of a sensor-actuator based on shape-morphing from electro-chemical–mechanical interactions

Abstract: This paper concerns the computational modeling of a class of carbon fiber composites, known as shape-morphing and strain-sensing composites. The actuating and sensing performance of such (smart) materials is achieved by the interplay between electrochemistry and mechanics, in particular the ability of carbon fibers to (de)intercalate Li-ions repeatedly. We focus on the actuation and sensing properties of a beam in conjunction with the appropriate “through-the-thickness” properties. Thus, the electro-chemo-mechanical analysis is essentially two-dimensional, and it is possible to rely heavily on the results in Carlstedt et al. (2020). More specifically, the cross-sectional design is composed of two electrodes, consisting of (partly) lithiated carbon fibers embedded in structural battery electrolyte (SBE), on either side of a separator. As a result, the modeling is hierarchical in the sense that (macroscale) beam action is combined with electro-chemo-mechanical interaction along the beam. The setup is able to work as sensor or actuator depending on the choice of control (and response) variables. Although quite idealized, this design allows for a qualitative investigation. In this paper we demonstrate the capability of the developed framework to simulate both the actuator and sensor modes. As proof of concept, we show that both modes of functionality can be captured using the developed framework. For the actuator mode, the predicted deformation is found to be in close agreement with experimental data. Further, the sensor-mode is found to agree with experimental data available in the literature.

Control-oriented meta-learning

Abstract: Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory tracking performance, provided that any uncertain dynamics terms are linearly parameterizable with known nonlinear features. However, it is often difficult to specify such features a priori, such as for aerodynamic disturbances on rotorcraft or interaction forces between a manipulator arm and various objects. In this paper, we turn to data-driven modeling with neural networks to learn, offline from past data, an adaptive controller with an internal parametric model of these nonlinear features. Our key insight is that we can better prepare the controller for deployment with control-oriented meta-learning of features in closed-loop simulation, rather than regression-oriented meta-learning of features to fit input-output data. Specifically, we meta-learn the adaptive controller with closed-loop tracking simulation as the base-learner and the average tracking error as the meta-objective. With both fully actuated and underactuated nonlinear planar rotorcraft subject to wind, we demonstrate that our adaptive controller outperforms other controllers trained with regression-oriented meta-learning when deployed in closed-loop for trajectory tracking control.