Fast Constrained Generalized Predictive Control with ADMM Embedded in an FPGA

Constrained model predictive control (MPC) usually requires the computation of a quadratic programming problem (QP) at each sampling instant. This is computationally expensive and becomes a limitation to embed and use MPC in plants with fast sampling rates. Several special solvers for MPC problems h...

Full description

Saved in:
Bibliographic Details
Published in:Revista IEEE América Latina 2020-02, Vol.18 (2), p.422-429
Main Authors: Peccin, Vinicius Berndsen, Lima, Daniel Martins, Flesch, Rodolfo Cesar Costa, Normey-Rico, Julio Elias
Format: Article
Language:English
Subjects:
Alternated direction method of multipliers
Computation
Computational modeling
Convex functions
embedded MPC
Embedded systems
fast GPC
Field programmable gate arrays
FPGA application
Hardware
Matrix decomposition
MIMO communication
Parallel processing
Predictive control
Quadratic programming
Sampling
Solvers
Citations: Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Constrained model predictive control (MPC) usually requires the computation of a quadratic programming problem (QP) at each sampling instant. This is computationally expensive and becomes a limitation to embed and use MPC in plants with fast sampling rates. Several special solvers for MPC problems have been proposed in the last years, but most of them focus on state-space formulations, which are very popular in academia. This paper proposes a solution based on alternated direction method of multipliers, tailored for embedded systems and applied to generalized predictive control (GPC), which is a very popular formulation in industry. Implementations issues of parallel computation are discussed in order to accelerate the time required for the operations. The implementation in an FPGA proved to be quite fast, with the observed worst case execution time of 11,54 µs for the presented example. These results contribute to embed GPC applications in processes that are typically controlled by classical controllers because of their fast dynamics.
ISSN:1548-0992
1548-0992
DOI:10.1109/TLA.2020.9085299