Title Implementation of Fast Predictive Controllers on FPGA Platforms based on Parallel Lipschitz Interpolation
Authors Nadales J.M. , MANZANO CRESPO, JOSÉ MARÍA, Barriga A. , Limon D.
External publication No
Means European Control Conf., ECC
Scope Conference Paper
Nature Científica
Web https://www.scopus.com/inward/record.uri?eid=2-s2.0-85124893237&doi=10.23919%2fECC54610.2021.9655204&partnerID=40&md5=6c8c8e8fa190ba049c4efa3f736d2700
Publication date 01/01/2021
ISI 000768455200224
Scopus Id 2-s2.0-85124893237
DOI 10.23919/ECC54610.2021.9655204
Abstract The implementation of nonlinear model predictive controllers for systems operating at high frequencies constitutes a significant challenge, mainly because of the complexity and time consumption of the optimization problem involved. An alternative that has been proposed is the employment of data-driven techniques to offline learn the control law, and then to implement it on a target embedded platform. Following this trend, in this paper we propose the implementation of predictive controllers on FPGA platforms making use of a parallel version of the machine learning technique known as Lipschitz interpolation. By doing this, computation time can be enormously accelerated. The results are compared to those obtained when the sequential algorithm runs on standard CPU platforms, and when the system is controlled by solving the optimization problem online, in terms of the error made and computing time. This method is validated in a case study where the nonlinear model predictive controller is employed to control a self-balancing two-wheel robot. © 2021 EUCA.
Keywords Balancing; Controllers; Field programmable gate arrays (FPGA); Interpolation; Learning systems; Optimization; Data driven technique; High frequency HF; Learn+; Lipschitz; Model predictive controllers;
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