Paper Info
Title
Nonlinear Model Predictive Control considering stochastic and systematic uncertainties with sets of densities
Abstract
In Model Predictive Control, the quality of control is highly dependent upon the model of the system under control. Therefore, a precise deterministic model is desirable. However, in real-world applications, modeling accuracy is typically limited and systems are generally affected by disturbances. Hence, it is important to systematically consider these uncertainties and to model them correctly. In this paper, we present a novel Nonlinear Model Predictive Control method for systems affected by two different types of perturbations that are modeled as being either stochastic or unknown but bounded quantities. We derive a formal generalization of the Nonlinear Model Predictive Control principle for considering both types of uncertainties simultaneously, which is achieved by using sets of probability densities. In doing so, a more robust and reliable control is obtained. The capabilities and benefits of our approach are demonstrated in real-world experiments with miniature walking robots.
Year
DOI
Venue
2010
10.1109/CCA.2010.5611241
Control Applications
Keywords
Field
DocType
nonlinear control systems,perturbation techniques,predictive control,probability,robust control,stochastic systems,uncertain systems,deterministic model,miniature walking robot,nonlinear model predictive control,perturbation,probability density,reliable control,robust control,stochastic uncertainty,systematic uncertainty
Mathematical optimization,Control theory,Computer science,Model predictive control,Stochastic process,Control engineering,Deterministic system,Robust control,Robot,Probability density function,Perturbation (astronomy),Bounded function
Conference
ISSN
ISBN
Citations 
1085-1992
978-1-4244-5363-4
0
PageRank 
References 
Authors
0.34
11
4
Name
Order
Citations
PageRank
Achim Hekler1132.85
Daniel Lyons2111.63
Benjamin Noack316823.73
Uwe D. Hanebeck459971.02