TY - JOUR
T1 - A Fuzzy Approximation for FCS-MPC in Power Converters
AU - Liu, Xing
AU - Qiu, Lin
AU - Fang, Youtong
AU - Wang, Kui
AU - Li, Yongdong
AU - Rodriguez, Jose
N1 - Publisher Copyright:
IEEE
PY - 2022
Y1 - 2022
N2 - Standard model predictive control is an optimization-based control strategy that can handle multiple control objectives and system nonlinear constraints. However, it typically suffers from the limitation of the uncertainties in practical systems, such as external unknown disturbances and parametric uncertainties. Motivated by aforementioned limitation, in this article, a novel robust model predictive control framework, endowed with the merits of fuzzy logic system and finite control-set model predictive control solution, is proposed. The main objective of this article is to enhance the system robustness, while guaranteeing adaptability to different conditions. More specifically, a fuzzy approximation point of view, which has a good potential to approximate the unknown nonlinear functions, is deployed and incorporated into the proposed design, which allows one to explicitly take the system nonlinear dynamics and uncertainties into account. The novelty of the proposed methodology relies on the fact that any prior knowledge and explicit information of system model parameters are not required, thereby resulting in considerable enhancement of robustness. Furthermore, the input-to-state stability of the approximation error system is proven through Lyapunov analysis, and it demonstrates that the estimated errors are uniformly ultimately bounded. Finally, the interest of the proposal is experimentally confirmed for modular multilevel converter.
AB - Standard model predictive control is an optimization-based control strategy that can handle multiple control objectives and system nonlinear constraints. However, it typically suffers from the limitation of the uncertainties in practical systems, such as external unknown disturbances and parametric uncertainties. Motivated by aforementioned limitation, in this article, a novel robust model predictive control framework, endowed with the merits of fuzzy logic system and finite control-set model predictive control solution, is proposed. The main objective of this article is to enhance the system robustness, while guaranteeing adaptability to different conditions. More specifically, a fuzzy approximation point of view, which has a good potential to approximate the unknown nonlinear functions, is deployed and incorporated into the proposed design, which allows one to explicitly take the system nonlinear dynamics and uncertainties into account. The novelty of the proposed methodology relies on the fact that any prior knowledge and explicit information of system model parameters are not required, thereby resulting in considerable enhancement of robustness. Furthermore, the input-to-state stability of the approximation error system is proven through Lyapunov analysis, and it demonstrates that the estimated errors are uniformly ultimately bounded. Finally, the interest of the proposal is experimentally confirmed for modular multilevel converter.
KW - Control systems
KW - Equivalent circuits
KW - Finite control-set model predictive control
KW - fuzzy approximation
KW - Fuzzy logic
KW - fuzzy logic system
KW - modular multilevel converter
KW - Predictive control
KW - Predictive models
KW - Robustness
KW - Uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85126272496&partnerID=8YFLogxK
U2 - 10.1109/TPEL.2022.3157847
DO - 10.1109/TPEL.2022.3157847
M3 - Article
AN - SCOPUS:85126272496
SN - 0885-8993
JO - IEEE Transactions on Power Electronics
JF - IEEE Transactions on Power Electronics
ER -