A Family of Iterative Gauss-Newton Shooting Methods for Nonlinear Optimal Control
Abstract¶
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.
Summary¶
Presents a unified family of iterative Gauss-Newton multiple-shooting methods for nonlinear optimal control, covering single and multiple shooting variants within a common framework with analysis of convergence and computational tradeoffs.
Links¶
Knowledge Base
External
Tags¶
Similar Papers¶
On Global and Local Convergence of Iterative Linear Quadratic Optimization Algorithms for Discrete Time Nonlinear Control
Global & Local Convergence of iLQR/DDP