Please use this identifier to cite or link to this item: http://hdl.handle.net/10216/105547
Author(s): L.T. Paiva
Fontes, Fernando A C C
Title: Sampled¿data model predictive control using adaptive time¿mesh refinement algorithms
Issue Date: 2017-09
Abstract: We address sampled¿data nonlinear Model Predictive Control (MPC) schemes, in particular we address methods to efficiently and accurately solve the underlying continuous-time optimal control problems (OCP). In nonlinear OCPs, the number of discretization points is a major factor affecting the computational time. Also, the location of these points is a major factor affecting the accuracy of the solutions. We propose the use of an algorithm that iteratively finds the adequate time¿mesh to satisfy some pre¿defined error estimate on the obtained trajectories. The proposed adaptive time¿mesh refinement algorithm provides local mesh resolution considering a time¿dependent stopping criterion, enabling an higher accuracy in the initial parts of the receding horizon, which are more relevant to MPC. The results show the advantage of the proposed adaptive mesh strategy, which leads to results obtained approximately as fast as the ones given by a coarse equidistant¿spaced mesh and as accurate as the ones given by a fine equidistant¿spaced mesh. © Springer International Publishing Switzerland 2017.
Call Number: 147871
URI: http://hdl.handle.net/10216/105547
Source: Lecture Notes in Electrical Engineering
Document Type: Artigo em Livro de Atas de Conferência Internacional
Rights: openAccess
Appears in Collections:FEUP - Artigo em Livro de Atas de Conferência Internacional

Files in This Item:
File Description SizeFormat 
147871.pdfMPC AMR-controlo16.pdf897.94 kBAdobe PDFThumbnail
View/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.