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Implementation aspects of model predictive control for embedded systems
Publikationstyp
Conference Paper
Date Issued
2012
Sprache
English
Author(s)
Start Page
1205
End Page
1210
Article Number
6315076
Citation
American Control Conference (ACC 2012)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
IEEE
ISBN
978-1-4577-1095-7
978-1-4577-1096-4
We discuss implementation related aspects of model predictive control schemes on embedded platforms. Ex-emplarily, we focus on fast gradient methods and present results from an implementation on a low-cost microcontroller. We show that input quantization in actuators should be exploited in order to determine a suboptimality level of the online optimization that requires a low number of algorithm iterations and might not significantly degrade the performance of the real system. As a case study we consider a Segway-like robot, modeled by a linear time-invariant system with 8 states and 2 inputs subject to box input constraints. The test system runs with a sampling period of 4 ms and uses a horizons up to 20 steps in a hard real-time system with limited CPU time and memory. © 2012 AACC American Automatic Control Council).
Subjects
embedded systems
fast gradient method
LEGO NXT
model predictive control
real-time implementation
DDC Class
004: Computer Sciences
621: Applied Physics