Voltage_Force_DATASET1

Citation Author(s):
Ahmet
Atasoy
Submitted by:
Ahmet ATASOY
Last updated:
Wed, 04/09/2025 - 17:56
DOI:
10.21227/qj4p-pe36
Data Format:
**.mat
License:
Creative Commons Attribution
35 Views
Categories:
Machine Learning
Keywords:
Force Control, Recurrent Neural Network Model Training
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Abstract 

  The graph shows the force and voltage data obtained with the SMA Actuator System in four different scenarios. These scenarios were designed to capture the dynamic behavior according to the difference between the cooling times. The first two scenarios with long cooling times were used to examine the steady-state behavior, and the last two scenarios with short cooling times were used to examine the dynamic responses.

Instructions: 

%load data%visualize data%forward model training: V (voltage mV) input -- F (force mN) output%inverse model training: F (force mN) input -- V (voltage mV) output%simuation : inverse model -- forward model connection%validation: inverse model -- plant (hardware system)
 
 % LRNN layer recurrent neural network
 % ________________ % I I% voltage -- I forward model I ---- force% I________________I%% The forward model will predict the force generated from a given voltage input
 % ________________ % I I% force -- I Inverse model I ---- voltage% I________________I%% The inverse model will predict the required voltage to generate the desired force
 % ________________ ________________ % I I I I% force -- I Inverse model I ---- voltage --- voltage -- I forward model I ---- force% (desired) I________________I (estimated) I________________I (estimated) %% This validates that the estimated voltage can produce the correct force through the forward model.
 % ________________ ________________ % I I I I% force -- I Inverse model I ---- voltage --- voltage -- I plant I ---- force% (desired) I________________I (estimated) I________________I (estimated) %% Compare the actual force with the desired force to validate the model's performance.
 % LRNN layer recurrent neural network % _____________% I I% input --w--\ _I_ ___ I% (_+_)----(_f_) -- --w--\ ___ ___% --b--/ (_+_)----(_f_) --output% --b--/ %%_______________________________________________________________________  

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