TY - GEN
T1 - Deep Robust Control of a Mechatronic System With Parametric Uncertainties
AU - Baimukashev, Daulet
AU - Rzagaliyev, Yerzhan
AU - Rubagotti, Matteo
AU - Varol, Huseyin Atakan
N1 - Funding Information:
This work was supported by the Nazarbayev University under Faculty Development Competitive Research Grant 240919FD3915.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - This paper proposes a method for controller approximation via neural network in the presence of parametric perturbations. The neural network is based on long short-term memory blocks and is trained to approximate a numerical optimal control law, solved for different parameter values. Using this approach, the obtained approximate control law learns to generate the control inputs based on different optimal control solutions for different parameters: as compared to training the neural network only based on the optimal control law defined for the nominal parameters, the overall system performance greatly improves when parameter variations are present, and does not degrade when the nominal parameters are used for testing. The proposed approach is validated experimentally on an inverted pendulum with dual-axis reaction wheels.
AB - This paper proposes a method for controller approximation via neural network in the presence of parametric perturbations. The neural network is based on long short-term memory blocks and is trained to approximate a numerical optimal control law, solved for different parameter values. Using this approach, the obtained approximate control law learns to generate the control inputs based on different optimal control solutions for different parameters: as compared to training the neural network only based on the optimal control law defined for the nominal parameters, the overall system performance greatly improves when parameter variations are present, and does not degrade when the nominal parameters are used for testing. The proposed approach is validated experimentally on an inverted pendulum with dual-axis reaction wheels.
UR - http://www.scopus.com/inward/record.url?scp=85137718409&partnerID=8YFLogxK
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U2 - 10.1109/AIM52237.2022.9863362
DO - 10.1109/AIM52237.2022.9863362
M3 - Conference contribution
AN - SCOPUS:85137718409
T3 - IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM
SP - 1031
EP - 1038
BT - 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2022
Y2 - 11 July 2022 through 15 July 2022
ER -