Abstract
Determining appropriate weighting factors is a key issue in finite control set model predictive control (FCS-MPC). The sequential model predictive control (SMPC) transforms the continuous weighting factors into fixed discrete optimization sequence and number of voltage vectors. In order to make these two parameters dynamic, this paper proposes a statistics-based dynamic sequential model predictive control scheme (Statistics-Based SMPC) for induction motor (IM) drives. This scheme focuses on the statistical characteristics of the cost function values, and uses the entropy weight method to dynamically determine the weight of the control targets, so that the optimization sequence can be dynamically changed with different working conditions. Another advantage of this scheme is that it is not limited by the number of control targets. Therefore, it has the potential to extend the cascade structure MPC without weighting factors to multiple control targets. Matlab/Simulink simulation verifies the effectiveness of the proposed method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
