Memory-Based Global Iterative Linear Quadratic Control

We propose a method for designing global nonlinear controllers based on the application of memory-based learning schemes for the purpose of aggregating multiple solutions produced by optimal control algorithms based on differential dynamic programming. The method leverages the fact that these optimal control algorithms produce not only nominal state and control trajectories, but entire full-state feedback (FSF) controllers, and the combined controller effectively switches between these multiple FSF controllers. Empirical verification demonstrates that it can be very effective in solving difficult benchmark control problems at high control rates.