文章基本信息

标题：Fast Generalized Predictive Control Based on Accelerated Dual Gradient Projection Method
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作者：Vinícius Berndsen Peccin ; Daniel Martins Lima ; Rodolfo César Costa Flesch 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2019
卷号：52
期号：1
页码：480-485
DOI：10.1016/j.ifacol.2019.06.108
语种：English
出版社：Elsevier
摘要：AbstractIn general, model predictive control (MPC) requires the computation of a quadratic programming problem (QP) at each sampling instant. This computation can be considered costly from the computational point of view and become a limitation for the use of MPC in plants with fast sampling rates. In order to circumvent this limitation and allow it to be used on a larger variety of systems, special solvers which efficiently compute the control signal can be used and implemented using high-speed hardware. Several works were proposed for this type of solution, but most of them focus on state-space formulations for MPC, which are very popular in academia. This paper proposes a solution based on the accelerated dual gradient projection method, applied to generalized predictive control, which is a very popular formulation in industry. The method is firstly validated using MATLAB® and its results are compared with the ones presented byquadprogsolver. A small size system is also evaluated in an FPGA with the QP computed in microseconds.
关键词：Keywordsembedded optimal controlFPGAGPADmodel predictive control