Paper Info
Title
Design Of Reduced Complexity Controllers For Linear Systems Under Constraints Using Data Cluster Analysis
Abstract
A numerical method is proposed to reduce the complexity and computational effort involved in the application of the multiparametric linear programming technique in the design of offline controllers for linear systems subject to constraints. For this purpose, the concept of controlled invariant sets and the K q-flat data cluster analysis algorithm are applied. Specifically, we show how the K q-flat algorithm can be used to establish a smaller number of polyhedral regions associated with a piecewise affine explicit state feedback control law. We also propose a new approach in the design of sub-optimal controllers that further reduce the number of regions. Numerical examples show that a significant reduction in the complexity of the control law can be achieved by the proposed approach.
Year
DOI
Venue
2020
10.1080/00207721.2020.1795948
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Keywords
DocType
Volume
Controlled invariant sets, data cluster analysis, linear systems under constraints, multiparametric linear programming
Journal
51
Issue
ISSN
Citations 
14
0020-7721
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Amanda Danielle O. da S. Dantas100.34
André Felipe Oliveira de Azevedo Dantas200.34
Túlio F. D. Almeida300.34
C. E. T. Dorea42514.67