Probabilistic prediction methods for nonlinear systems with application to stochastic model predictive control
Landgraf D, Völz A, Berkel F, Schmidt K, Specker T, Graichen K (2023)
Publication Type: Journal article
Publication year: 2023
Journal
Book Volume: 56
Pages Range: 100905
Article Number: 100905
DOI: 10.1016/j.arcontrol.2023.100905
Abstract
The performance of modern control methods, such as model predictive control, depends significantly on the accuracy of the system model. In practice, however, stochastic uncertainties are commonly present, resulting from inaccuracies in the modeling or external disturbances, which can have a negative impact on the control performance. This article reviews the literature on methods for predicting probabilistic uncertainties for nonlinear systems. Since a precise prediction of probability density functions comes along with a high computational effort in the nonlinear case, the focus of this article is on approximating methods, which are of particular relevance in control engineering practice. The methods are classified with respect to their approximation type and with respect to the assumptions about the input and output distribution. Furthermore, the application of these prediction methods to stochastic model predictive control is discussed including a literature review for nonlinear systems. Finally, the most important probabilistic prediction methods are evaluated numerically. For this purpose, the estimation accuracies of the methods are investigated first and the performance of a stochastic model predictive controller with different prediction methods is examined subsequently using multiple nonlinear systems, including the dynamics of an autonomous vehicle.
Authors with CRIS profile
Daniel Landgraf
Chair of Automatic Control
Andreas Völz
Chair of Automatic Control
Knut Graichen
Chair of Automatic Control
Involved external institutions
Germany (DE)How to cite
APA:
Landgraf, D., Völz, A., Berkel, F., Schmidt, K., Specker, T., & Graichen, K. (2023). Probabilistic prediction methods for nonlinear systems with application to stochastic model predictive control. Annual Reviews in Control, 56, 100905. https://doi.org/10.1016/j.arcontrol.2023.100905
MLA:
Landgraf, Daniel, et al. "Probabilistic prediction methods for nonlinear systems with application to stochastic model predictive control." Annual Reviews in Control 56 (2023): 100905.
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