Convergence Properties of Fast quasi-LPV Model Predictive Control

Publikationstyp
Conference Paper
Publikationsdatum
2021-12
Sprache
English
Author
Hespe, Christian orcid-logo
Werner, Herbert 
Institut
Regelungstechnik E-14 
TORE-URI
http://hdl.handle.net/11420/12053
Enthalten in
Proceedings of the IEEE Conference on Decision & Control 
Volume
2021-December
Start Page
3869
End Page
3874
Citation
60th IEEE Conference on Decision and Control (CDC 2021)
Contribution to Conference
60th IEEE Conference on Decision and Control, CDC 2021 
Publisher DOI
10.1109/CDC45484.2021.9683612
Scopus ID
2-s2.0-85126055235
In this paper, we study the convergence properties of an iterative algorithm for fast nonlinear model predictive control (MPC) of quasi-linear parameter-varying systems without inequality constraints. Compared to previous works considering this algorithm, we contribute conditions under which the iterations are guaranteed to converge. Furthermore, we show that the algorithm converges to suboptimal solutions and propose an optimality-preserving variant with moderately increased computational complexity. Finally, we compare both variants in terms of quality of solution and computational performance with a state-of-the-art solver for nonlinear MPC in two simulation benchmarks.