TY - JOUR
T1 - High-Speed Finite Control Set Model Predictive Control for Power Electronics
AU - Stellato, Bartolomeo
AU - Geyer, Tobias
AU - Goulart, Paul J.
N1 - Funding Information:
This work was supported by the People Program (Marie Curie Actions) of the European Union Seventh Framework Program (FP7/2007-2013) under REA Grant 607957 (TEMPO).
Publisher Copyright:
© 2016 IEEE.
PY - 2017/5
Y1 - 2017/5
N2 - Common approaches for direct model predictive control (MPC) for current reference tracking in power electronics suffer from the high computational complexity encountered when solving integer optimal control problems over long prediction horizons. We propose an efficient alternative method based on approximate dynamic programming, greatly reducing the computational burden and enabling sampling times below 25 μs. Our approach is based on the offline estimation of an infinite horizon value function, which is then utilized as the tail cost of an MPC problem. This allows us to reduce the controller horizon to a very small number of stages, while simultaneously improving the overall controller performance. Our proposed algorithm was implemented on a small size FPGA and validated on a variable-speed drive system with a three-level voltage-source converter. Time measurements showed that our algorithm requires only 5.76 μs for horizon N=1 and 17.27 μs for N=2, in both cases outperforming state-of-the-art approaches with much longer horizons in terms of currents distortion and switching frequency. To the authors' knowledge, this is the first time direct MPC for current control has been implemented on an FPGA solving the integer optimization problem in real time and achieving comparable performance to formulations with long prediction horizons.
AB - Common approaches for direct model predictive control (MPC) for current reference tracking in power electronics suffer from the high computational complexity encountered when solving integer optimal control problems over long prediction horizons. We propose an efficient alternative method based on approximate dynamic programming, greatly reducing the computational burden and enabling sampling times below 25 μs. Our approach is based on the offline estimation of an infinite horizon value function, which is then utilized as the tail cost of an MPC problem. This allows us to reduce the controller horizon to a very small number of stages, while simultaneously improving the overall controller performance. Our proposed algorithm was implemented on a small size FPGA and validated on a variable-speed drive system with a three-level voltage-source converter. Time measurements showed that our algorithm requires only 5.76 μs for horizon N=1 and 17.27 μs for N=2, in both cases outperforming state-of-the-art approaches with much longer horizons in terms of currents distortion and switching frequency. To the authors' knowledge, this is the first time direct MPC for current control has been implemented on an FPGA solving the integer optimization problem in real time and achieving comparable performance to formulations with long prediction horizons.
KW - Approximate dynamic programming (ADP)
KW - Value function approximation
KW - drive systems
KW - finite control set
KW - model predictive control (MPC)
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U2 - 10.1109/TPEL.2016.2584678
DO - 10.1109/TPEL.2016.2584678
M3 - Article
AN - SCOPUS:85012154521
SN - 0885-8993
VL - 32
SP - 4007
EP - 4020
JO - IEEE Transactions on Power Electronics
JF - IEEE Transactions on Power Electronics
IS - 5
M1 - 7499836
ER -