Abstract

This paper introduces a family of iterative al- gorithms 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 al- gorithms 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.

Paper Link

M. Giftthaler et al., A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control, IROS 2018

PDF file

Code Dependancy

• Matlab

How to Operate It

• ipython LQR.py
  • My code is to caculate simple dynamics problem which is introduces in their paper.

Result