Interior Point Differential Dynamic Programming

Andrei Pavlov*, Iman Shames, Chris Manzie

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)


This brief introduces a novel differential dynamic programming (DDP) algorithm for solving discrete-time finite-horizon optimal control problems with inequality constraints. Two variants, namely feasible- and infeasible-IPDDP algorithms, are developed using a primal-dual interior-point methodology, and their local quadratic convergence properties are characterized. We show that the stationary points of the algorithms are the perturbed KKT points, and thus can be moved arbitrarily close to a locally optimal solution. Being free from the burden of the active-set methods, it can handle nonlinear state and input inequality constraints without a discernible increase in its computational complexity relative to the unconstrained case. The performance of the proposed algorithms is demonstrated using numerical experiments on three different problems: control-limited inverted pendulum, car-parking, and unicycle motion control and obstacle avoidance.

Original languageEnglish
Pages (from-to)2720-2727
Number of pages8
JournalIEEE Transactions on Control Systems Technology
Issue number6
Publication statusPublished - 1 Nov 2021
Externally publishedYes


Dive into the research topics of 'Interior Point Differential Dynamic Programming'. Together they form a unique fingerprint.

Cite this