Robust Differential Dynamic Programming

Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes, pp. 1714–1721

Abstract

Differential Dynamic Programming is an optimal control technique often used for trajectory generation. Many variations of this algorithm have been developed in the literature, including algorithms for stochastic dynamics or state and input constraints. In this contribution, we develop a robust version of Differential Dynamic Programming that uses generalized plants and multiplier relaxations for uncertainties. To this end, we study a version of the Bellman principle and use convex relaxations to account for uncertainties in the dynamic program. The resulting algorithm can be seen as a robust trajectory generation tool for nonlinear systems.

Authors 3

1. Dennis Gramlich Aachen Chair of Intelligent Control Systems

   RWTH Aachen University

   Affiliation as printed

   RWTH-Aachen,Chair of Intelligent Control Systems,Aachen,Germany,D-52074

2. Carsten W. Scherer

   University of Stuttgart

   Affiliation as printed

   University of Stuttgart,Chair of Mathematical Systems Theory,Stuttgart,Germany,70174

3. Christian Ebenbauer Aachen Chair of Intelligent Control Systems

   RWTH Aachen University

   Affiliation as printed

   RWTH-Aachen,Chair of Intelligent Control Systems,Aachen,Germany,D-52074