A distributed model predictive control strategy for back-to-back converters

Luca Tarisciotti, Giovanni Lo Calzo, Alberto Gaeta, Pericle Zanchetta, Felipe Valencia, Doris Saez

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


In recent years, model predictive control (MPC) has been successfully used for the control of power electronics converters with different topologies and for different applications. MPC offers many advantages over more traditional control techniques such as the ability to avoid cascaded control loops, easy inclusion of constraint, and fast transient response. On the other hand, the controller computational burden increases exponentially with the system complexity and may result in an unfeasible realization on modern digital control boards. This paper proposes a novel distributed MPC (DMPC), which is able to achieve the same performance of the classical MPC while reducing the computational requirements of its implementation. The proposed control approach is tested on a ac/ac converter in a back-to-back configuration used for power flow management. Simulation results are provided and validated through experimental testing in several operating conditions.

Original languageEnglish
Article number7403993
Pages (from-to)5867-5878
Number of pages12
JournalIEEE Transactions on Industrial Electronics
Issue number9
Publication statusPublished - 1 Sep 2016


  • Back-to-back converters
  • nonlinear control systems
  • predictive control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering


Dive into the research topics of 'A distributed model predictive control strategy for back-to-back converters'. Together they form a unique fingerprint.

Cite this