# Optimal-Horizon Model-Predictive Control with Differential Dynamic Programming

arXiv: https://arxiv.org/abs/2111.09207 · HTML: https://mlsanity.com/paper/2111.09207/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2021.

## Built on by (most cited first, 1 of 1)

- [Solving Minimum-Cost Reach Avoid using Reinforcement Learning](https://mlsanity.com/paper/2410.22600/) (2024)

## Abstract (arXiv)

We present an algorithm, based on the Differential Dynamic Programming framework, to handle trajectory optimization problems in which the horizon is determined online rather than fixed a priori. This algorithm exhibits exact one-step convergence for linear, quadratic, time-invariant problems and is fast enough for real-time nonlinear model-predictive control. We show derivations for the nonlinear algorithm in the discrete-time case, and apply this algorithm to a variety of nonlinear problems. Finally, we show the efficacy of the optimal-horizon model-predictive control scheme compared to a standard MPC controller, on an obstacle-avoidance problem with planar robots.