Abstract
Model predictive control (MPC) is the result of the latest advances in power electronics and modem control. It is regarded as one of the best techniques when it comes to handling of nonlinearities in the intrinsic model of induction motor (IM). Conventional MPC utilizes weighting factors in the objective function that are tuned after rigorous experimental work which can be improved by utilizing the more mature intelligent optimization techniques like NSGA-II etc. In this study, the weighting factor optimization for the conventional MPC control of IM based on NSGA-II with TOPSIS decision-making criteria is studied. A control algorithm is designed, and an experimental test setup is made to obtain the results of this intelligent MPC which are compared with conventional MPC based on some performance indices like torque and flux ripple, switching frequency loss etc.
| Original language | English |
|---|---|
| Article number | 8928488 |
| Pages (from-to) | 177595-177606 |
| Number of pages | 12 |
| Journal | IEEE Access |
| Volume | 7 |
| DOIs | |
| State | Published - 2019 |
Bibliographical note
Publisher Copyright:© 2013 IEEE.
Keywords
- DTC
- FOC
- TOPSIS
- dynamic induction motor model
- finite set control model non-dominated sorting genetic algorithm
- predictive control
ASJC Scopus subject areas
- General Computer Science
- General Materials Science
- General Engineering