Open AccessFast algorithms for constrained generalised predictive control with on‐line optimisation
Author(s) -
Peccin Vinícius Berndsen,
Lima Daniel Martins,
Flesch Rodolfo César Costa,
NormeyRico Julio Elias
Publication year - 2021
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/cth2.12060
Subject(s) - model predictive control , computer science , algorithm , minimisation (clinical trials) , grid , quadratic equation , context (archaeology) , mathematical optimization , mathematics , control (management) , artificial intelligence , paleontology , statistics , geometry , biology
This paper proposes fast algorithms for constrained generalised predictive control based on two first‐order methods, namely accelerated dual gradient projection method and fast alternating minimisation algorithm. Theoretical bounds on the number of iterations, which play an important role in the context of real‐time model predictive controllers, are provided for both algorithms. Also, some implementation issues in parallel architectures are discussed. The methods are firstly validated by simulation and their results are compared with the ones presented by commercial solvers. A three‐phase grid‐connected LCL‐filtered inverter was used as a case study. The algorithms were evaluated in an FPGA with the quadratic program computed in microseconds.