A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control

This paper introduces a family of iterative algorithms for unconstrained nonlinear optimal control. We generalize the well-known iLQR algorithm to different multiple shooting variants, combining advantages like straightforward initialization and a closed-loop forward integration. All algorithms have...

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Bibliographic Details
Published in2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) pp. 1 - 9
Main Authors Giftthaler, Markus, Neunert, Michael, Stauble, Markus, Buchli, Jonas, Diehl, Moritz
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
Convergence
Differential Dynamic Programming
Heuristic algorithms
Multiple Shooting
Nonlinear Model Predictive Control
Numerical Optimal Control
Optimal control
Prediction algorithms
Quadrupedal Robots
Robots
System dynamics
Trajectory
Trajectory Optimization
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Summary:This paper introduces a family of iterative algorithms for unconstrained nonlinear optimal control. We generalize the well-known iLQR algorithm to different multiple shooting variants, combining advantages like straightforward initialization and a closed-loop forward integration. All algorithms have similar computational complexity, i.e. linear complexity in the time horizon, and can be derived in the same computational framework. We compare the full-step variants of our algorithms and present several simulation examples, including a high-dimensional underactuated robot subject to contact switches. Simulation results show that our multiple shooting algorithms can achieve faster convergence, better local contraction rates and much shorter runtimes than classical iLQR, which makes them a superior choice for nonlinear model predictive control applications.
ISSN:2153-0866
DOI:10.1109/IROS.2018.8593840