Abstract
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.
Original language | English |
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Journal | IEEE Transactions on Power Electronics |
DOIs | |
Publication status | Accepted/In press - 2022 |
Keywords
- Control systems
- Equivalent circuits
- Finite control-set model predictive control
- fuzzy approximation
- Fuzzy logic
- fuzzy logic system
- modular multilevel converter
- Predictive control
- Predictive models
- Robustness
- Uncertainty
ASJC Scopus subject areas
- Electrical and Electronic Engineering