Efficient Nonlinear Model Predictive Control via quasi-LPV representation

Publikationstyp
Conference Paper
Date Issued
2016-12-27
Sprache
English
Author(s)
Cisneros, Pablo S. G.  
Voss, Sophia  
Werner, Herbert  
Institut
Regelungstechnik E-14  
TORE-URI
http://hdl.handle.net/11420/4904
Journal
Proceedings of the IEEE Conference on Decision & Control  
Volume
2016
Start Page
3216
End Page
3221
Article Number
7798752
Citation
Proceedings of the IEEE Conference on Decision & Control (): 7798752 3216-3221 (2016)
Contribution to Conference
55th IEEE Conference on Decision and Control, CDC 2016  
Publisher DOI
10.1109/CDC.2016.7798752
Scopus ID
2-s2.0-85010716356
Publisher
IEEE
Nonlinear Model Predictive Control often suffers from excessive computational complexity, which becomes critical when fast plants are to be controlled. This papers presents an approach to NMPC that exploits the quasi-LPV framework. For quasi-LPV systems, the scheduling variables are determined by the state variables and/or inputs. By calculating an estimate of the state variables during prediction, the prediction model can be adapted to the estimated state evolution in each step. Stability of the proposed algorithm is enforced by the offline solution of an optimization problem with Linear Matrix Inequality (LMI) constraints. Furthermore, an iterative approach is presented with which the NMPC optimization problem can be handled by solving a series of Quadratic Programs (QP) or Second Order Cone Programs (SOCP) in each time step, which leads to computational efficiency. The algorithm is tested in simulation to highlight convergence of the prediction and stability of the closed-loop under contraints.
DDC Class
620: Ingenieurwissenschaften