Data-Driven Model Development and Feedback Control Design for PZT Bimorph Actuators

Author(s):  
Nikolas Bravo ◽  
Ralph C. Smith ◽  
John Crews

In the paper, we discuss the development of a high-fidelity and surrogate model for a PZT bimorph used as an actuator for micro-air vehicles including Robobee. The models must quantify the nonlinear, hysteretic, and rate-dependent behavior inherent to PZT in dynamic operating regimes. The actuator dynamics are initially modeled using the homogenized energy model (HEM) framework. This provides a comprehensive high-fidelity model, which can be inverted and implemented in real time for certain control regimes. To improve efficiency, we additionally discuss the development of data-driven models and focus on the implementation of a surrogate model based on a dynamic mode decomposition (DMD). Finally, we detail the design and implementation of a PI controller on the surrogate and high-fidelity models.

Author(s):  
John Crews ◽  
Nikolas Bravo ◽  
Ralph Smith

In the paper, we discuss the development of a model for PZT bimorph actuators used to power micro-air vehicles including Robobee. Due to highly dynamic drive regimes required for the actuators, models must quantify the nonlinear, hysteretic, and rate-dependent behavior inherent to PZT in these regimes. We employ the homogenized energy model (HEM) framework to model the actuator dynamics and numerically we illustrate the capability of the model to characterize the inherent hysteresis. This provides a comprehensive model, which can be inverted and implemented for certain control regimes.


2020 ◽  
Vol 105 (3) ◽  
pp. 699-713 ◽  
Author(s):  
Hadrien Calmet ◽  
Daniel Pastrana ◽  
Oriol Lehmkuhl ◽  
Takahisa Yamamoto ◽  
Yoshiki Kobayashi ◽  
...  

Author(s):  
Nicola Demo ◽  
Giulio Ortali ◽  
Gianluca Gustin ◽  
Gianluigi Rozza ◽  
Gianpiero Lavini

Abstract This contribution describes the implementation of a data-driven shape optimization pipeline in a naval architecture application. We adopt reduced order models in order to improve the efficiency of the overall optimization, keeping a modular and equation-free nature to target the industrial demand. We applied the above mentioned pipeline to a realistic cruise ship in order to reduce the total drag. We begin by defining the design space, generated by deforming an initial shape in a parametric way using free form deformation. The evaluation of the performance of each new hull is determined by simulating the flux via finite volume discretization of a two-phase (water and air) fluid. Since the fluid dynamics model can result very expensive—especially dealing with complex industrial geometries—we propose also a dynamic mode decomposition enhancement to reduce the computational cost of a single numerical simulation. The real-time computation is finally achieved by means of proper orthogonal decomposition with Gaussian process regression technique. Thanks to the quick approximation, a genetic optimization algorithm becomes feasible to converge towards the optimal shape.


2015 ◽  
Vol 25 (6) ◽  
pp. 1307-1346 ◽  
Author(s):  
Matthew O. Williams ◽  
Ioannis G. Kevrekidis ◽  
Clarence W. Rowley

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