Stochastic Nonlinear Model Predictive Control Based on Progressive Density Simplification

Provided by: Karlsruhe Institute of Technology
Topic: Mobility
Format: PDF
Increasing demand for nonlinear model predictive control with the ability to handle highly noise-corrupted systems has recently given rise to stochastic control approaches. Besides providing high-quality results within a noisy environment, these approaches have one problem in common, namely a high computational demand and, as a consequence, generally a short prediction horizon. In this paper, the authors propose to reduce the computational complexity of prediction and value function evaluation within the control horizon by simplifying the system progressively down to the deterministic case.

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