|Publisher version (open access)||1 MB||Adobe Acrobat PDF||View/Open
Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/920467
- Predictive control formulation for achieving a reduced finite control set in flying capacitor converters
Aguilera, Ricardo P.;
Quevedo, Daniel E.
- The University of Newcastle. Faculty of Engineering & Built Environment, School of Electrical Engineering and Computer Science
- Multilevel Converters (MCs) have emerged as a promising alternative to traditional two level converters. These topologies present a better output voltage quality due to the reduction of the voltage steps by increasing the voltage number levels. Within the MC family, flying capacitor converters present a special attraction due to the easy way to increase output voltage levels by adding cells. Recently model predictive control algorithms have reached a special interest in MCs applications. In particular, finite control set predictive control algorithms applied to flying capacitor converters have shown that it is possible to achieve a good performance in the control of capacitor voltages and output current. For that purpose, at each sample time the controller explores all the switching states and determines the optimal one to be applied. However, the number of switching states grow exponentially in relation to the number of cells. This increases the time that the algorithm takes to find the optimal switching state. In this paper we present an off-line strategy to reduce the number of switching states to be explored in a finite control set predictive algorithm by using only those which produce that the system state point towards to the reference. Moreover, a sampling period design is presented to guarantee that the system state remains inside of a positive invariant set.
- European Control Conference 2009 (ECC'09). ECC'09: European Control Conference 2009 Proceedings (Budapest, Hungary 23-26 August, 2009) p. 3955-3960
- European Union Control Association (EUCA)
flying capacitor converters;
predictive control algorithms;
- Resource Type
- conference paper