文章基本信息

标题：Sequential ℓ 1 Quadratic Programming for Nonlinear Model Predictive Control
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作者：Dimitri Boiroux ; John Bagterp Jørgensen
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：474-479
DOI：10.1016/j.ifacol.2019.06.107
语种：English
出版社：Elsevier
摘要：AbstractIn this paper, we present and describe a computationally efficient sequential l1 quadratic programming (Sl1QP) algorithm for Nonlinear Model Predictive Control (NMPC). We use a tailored trust region sequential quadratic programming for the solution of the optimal control problem (OCP) involved in the NMPC algorithm. We use a multiple shooting approach for numerical integration and sensitivity computation. A second order correction ensures a faster convergence of the SQP algorithm. We exploit the structure of the OCP by using an efficient primal-dual interior point algorithm based on Riccati factorizations and a block diagonal BFGS update of the Hessian matrix. The complexity scales linearly with the prediction horizon length. We numerically evaluate and compare the performance of our algorithm on a numerical example.
关键词：KeywordsNonlinear model predictive controltrust region algorithmsequential quadratic programming