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

29 Nov 2017  ·  Markus Giftthaler, Michael Neunert, Markus Stäuble, Jonas Buchli, Moritz Diehl ·

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 straight-forward 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.

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Systems and Control Robotics Optimization and Control


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