Efficient Control of Nonlinear NoiseCorrupted Systems Using a Novel Model Predictive Control Framework
Weissel, Florian; Huber, Marco F.; Hanebeck, Uwe D.
Abstract:
Model identification and measurement acquisition is always to some degree uncertain. Therefore, a framework for Nonlinear Model Predictive Control (NMPC) is proposed that explicitly considers the noise influence on nonlinear dynamic systems with continuous state spaces and a finite set of control inputs in order to significantly increase the control quality. Integral parts of NMPC are the prediction of system states over a finite horizon as well as the problem specific modeling of reward functions. For achieving an efficient and also accurate state prediction, the introduced framework uses transition densities approximated by means of axisaligned Gaussian mixtures. The representation power of Gaussian mixtures is also used to model versatile reward functions. Thus, together with the prediction technique a closedform calculation of the optimization problems arising from NMPC is possible. Additionally, an efficient algorithm for calculating an approximate value function of the corresponding optimal control problem employing dynamic programming is presented. Thus, the value function can be calculated offline, which reduces the online computational burden significantly and also permits the use of long optimization horizons. The capabilities of the framework and especially the benefits that can be gained by incorporating the noise in the controller are illustrated by the example of a twowheeled differentialdrive mobile robot following a given path.
Zugehörige Institution(en) am KIT 
Institut für Anthropomatik (IFA)

Publikationstyp 
Proceedingsbeitrag 
Jahr 
2007 
Sprache 
Englisch 
Identifikator 
ISBN: 1424409896
URN: urn:nbn:de:swb:90348317
KITopen ID: 1000034831 
Erschienen in 
Proceedings of the 2007 American Control Conference Marriott Marquis Hotel at Times Square, New York City, USA, July 1113, 2007 
Verlag 
IEEE, Piscataway 
Seiten 
37513756 