Surrogate model development and feedforward control implementation for PZT bimorph actuators employed for robobee

2017 ◽  
Author(s):  
Nikolas Bravo ◽  
Ralph C. Smith ◽  
John Crews
Keyword(s):  
Surrogate Model ◽  
Feedforward Control ◽  
Model Development ◽  
Bimorph Actuators ◽  
Control Implementation

Surrogate Model Development for Naturally Ventilated Office Buildings

2020 ◽  
Author(s):  
Marcelo Salles Olinger ◽  
Ana Paula Melo ◽  
Letícia Oliveira Neves ◽  
Roberto Lamberts
Keyword(s):  
Surrogate Model ◽  
Model Development ◽  
Office Buildings

Stochastic Bayesian optimization for predicting borderline knock

2021 ◽  
pp. 146808742110652
Author(s):  
Jian Tang ◽  
Anuj Pal ◽  
Wen Dai ◽  
Chad Archer ◽  
James Yi ◽  
...  
Keyword(s):  
Fuel Economy ◽  
Learning Algorithm ◽  
Feedforward Control ◽  
Model Development ◽  
Kriging Model ◽  
Optimization Method ◽  
Surrogate Models ◽  
Bayesian Optimization ◽  
Control Parameters ◽  
Operation Conditions

Engine knock is an undesirable combustion that could damage the engine mechanically. On the other hand, it is often desired to operate the engine close to its borderline knock limit to optimize combustion efficiency. Traditionally, borderline knock limit is detected by sweeping tests of related control parameters for the worst knock, which is expensive and time consuming, and also, the detected borderline knock limit is often used as a feedforward control without considering its stochastic characteristics without compensating current engine operational condition and type of fuel used. In this paper, stochastic Bayesian optimization method is used to obtain a tradeoff between stochastic knock intensity and fuel economy. The log-nominal distribution of knock intensity signal is converted to Gaussian one using a proposed map to satisfy the assumption for Kriging model development. Both deterministic and stochastic Kriging surrogate models are developed based on test data using the Bayesian iterative optimization process. This study focuses on optimizing two competing objectives, knock intensity and indicated specific fuel consumption using two control parameters: spark and intake valve timings. Test results at two different operation conditions show that the proposed learning algorithm not only reduces required time and cost for predicting knock borderline but also provides control parameters, based on trained surrogate models and the corresponding Pareto front, with the best fuel economy possible.

Model Development for PZT Bimorph Actuation Employed for Micro-Air Vehicles

2016 ◽  
Author(s):  
John Crews ◽  
Nikolas Bravo ◽  
Ralph Smith
Keyword(s):  
Model Development ◽  
Micro Air Vehicles ◽  
Energy Model ◽  
Comprehensive Model ◽  
Actuator Dynamics ◽  
Rate Dependent ◽  
Dependent Behavior ◽  
Bimorph Actuators ◽  
Homogenized Energy Model ◽  
Air Vehicles

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.

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

2017 ◽  
Author(s):  
Nikolas Bravo ◽  
Ralph C. Smith ◽  
John Crews
Keyword(s):  
Surrogate Model ◽  
Model Development ◽  
Micro Air Vehicles ◽  
Dynamic Mode Decomposition ◽  
Data Driven ◽  
Dynamic Mode ◽  
High Fidelity ◽  
Actuator Dynamics ◽  
Rate Dependent ◽  
Homogenized Energy Model

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.

Data Science Approaches for Electrochemical Engineers: An Introduction through Surrogate Model Development for Lithium-Ion Batteries

2018 ◽  
Vol 165 (2) ◽  
pp. A1-A15 ◽  
Author(s):  
Neal Dawson-Elli ◽  
Seong Beom Lee ◽  
Manan Pathak ◽  
Kishalay Mitra ◽  
Venkat R. Subramanian
Keyword(s):  
Lithium Ion Batteries ◽  
Surrogate Model ◽  
Data Science ◽  
Model Development ◽  
Lithium Ion

Smart Sampling Algorithm for Surrogate Model Development

2017 ◽  
Vol 96 ◽  
pp. 103-114 ◽  
Author(s):  
Sushant Suhas Garud ◽  
I.A. Karimi ◽  
Markus Kraft
Keyword(s):  
Surrogate Model ◽  
Model Development ◽  
Sampling Algorithm

Surrogate Model Development for Fuels for Advanced Combustion Engines

2011 ◽  
Vol 25 (4) ◽  
pp. 1474-1484 ◽  
Author(s):  
K. Anand ◽  
Y. Ra ◽  
R. D. Reitz ◽  
B. Bunting
Keyword(s):  
Surrogate Model ◽  
Model Development ◽  
Combustion Engines ◽  
Advanced Combustion
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