Mixed-level iteration schemes for nonlinear model predictive control - Frasch, Janick; Wirsching, Leonard; Sager, Sebastian; Bock, Hans Georg (IFAC-PapersOnLine, 2012)
In general, numerical schemes for nonlinear model predictive control (NMPC) require the (approximate) solution of a nonlinear program in each sample for feedback generation. Thus, the application of NMPC to processes that need fast feedback poses a major computational challenge. Recently, new multi-level iteration schemes have been proposed, extending the well-known idea of real-time iterations. These algorithms take into account different time scales inherent in the dynamic model by updating the data of the feedback-generating quadratic program (QP), i.e., Hessians and Jacobians, gradients, and constraint residuals, on different levels. In this contribution we consider new mixed-level updates of the QP data, which interval-wise apply different update levels. In particular, we apply higher-level updates more frequently on the first intervals of the control horizon, given their importance in the context of model predictive control in general. Targeting at modern computers with multi-core processing units, we describe an efficient parallel implementation of the mixed-level iteration approach and apply it to a benchmark problem from automotive engineering.
Available online 21 April 2016
Gesehen am 29.01.2018