TY - GEN
T1 - Optimal Control Methods for Fuzzy Optimal Control Problem
AU - Soemarsono, Annisa R.
AU - Mardlijah,
AU - Yazid, Edwar
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - We have made several adjustmentsto fulfill the necessary requirements for optimal control problems based on fuzzy concepts for the LQR method by implementing the horizontal membership function (HMF) concept and also utilizing the definition of differentiation with a granular approach. We then construct an illustrative example of fuzzy optimal control problems for PMP and LQR methods by determining fuzzy control in the description of a Gaussian membership function. The fuzzy control, which is declared as a membership function, is expected to facilitate the optimal control process applied to the fuzzification process of a fuzzy model, such as the Takagi-Sugeno (or Takagi-Sugeno-Kang) fuzzy model.Fuzzy models of the Takagi-Sugeno (or Takagi-Sugeno-Kang) type aim to represent systems or plants that are non-linear. Therefore, we have PMP for optimal control problems with a fuzzy approach and LQR for optimal control problems with a fuzzy approach. Both optimal control methods involve many variational calculus (variational problems) that require an approach relevant with the idea of Euler-Lagrange. Hereafter, we find a solution to the fuzzy optimal control problems for PMP and LQR analytically. We obtain optimal fuzzy state and optimal fuzzy control. Finally, we apply Simulink (Matlab) to show graphics of the optimal fuzzy state and optimal fuzzy control.
AB - We have made several adjustmentsto fulfill the necessary requirements for optimal control problems based on fuzzy concepts for the LQR method by implementing the horizontal membership function (HMF) concept and also utilizing the definition of differentiation with a granular approach. We then construct an illustrative example of fuzzy optimal control problems for PMP and LQR methods by determining fuzzy control in the description of a Gaussian membership function. The fuzzy control, which is declared as a membership function, is expected to facilitate the optimal control process applied to the fuzzification process of a fuzzy model, such as the Takagi-Sugeno (or Takagi-Sugeno-Kang) fuzzy model.Fuzzy models of the Takagi-Sugeno (or Takagi-Sugeno-Kang) type aim to represent systems or plants that are non-linear. Therefore, we have PMP for optimal control problems with a fuzzy approach and LQR for optimal control problems with a fuzzy approach. Both optimal control methods involve many variational calculus (variational problems) that require an approach relevant with the idea of Euler-Lagrange. Hereafter, we find a solution to the fuzzy optimal control problems for PMP and LQR analytically. We obtain optimal fuzzy state and optimal fuzzy control. Finally, we apply Simulink (Matlab) to show graphics of the optimal fuzzy state and optimal fuzzy control.
KW - fuzzy
KW - granular
KW - membership function
KW - optimal control
UR - http://www.scopus.com/inward/record.url?scp=85186490938&partnerID=8YFLogxK
U2 - 10.1109/ICAMIMIA60881.2023.10427679
DO - 10.1109/ICAMIMIA60881.2023.10427679
M3 - Conference contribution
AN - SCOPUS:85186490938
T3 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
SP - 407
EP - 412
BT - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023
Y2 - 14 November 2023 through 15 November 2023
ER -
