# Online Inverse Optimal Control for Control-Constrained Discrete-Time Systems on Finite and Infinite Horizons

arXiv: https://arxiv.org/abs/2005.06153 · HTML: https://mlsanity.com/paper/2005.06153/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2020.

## Built on by (most cited first, 5 of 5)

- [Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Games](https://mlsanity.com/paper/2301.01997/) (2023)
- [A Differential Dynamic Programming Framework for Inverse Reinforcement Learning](https://mlsanity.com/paper/2407.19902/) (2024)
- [Recursive Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/2504.13241/) (2025)
- [PACE: A Framework for Learning and Control in Linear Incomplete-Information Differential Games](https://mlsanity.com/paper/2504.17128/) (2025)
- [Peer-Aware Cost Estimation in Nonlinear General-Sum Dynamic Games for Mutual Learning and Intent Inference](https://mlsanity.com/paper/2504.17129/) (2025)

## Abstract (arXiv)

In this paper, we consider the problem of computing parameters of an objective function for a discrete-time optimal control problem from state and control trajectories with active control constraints. We propose a novel method of inverse optimal control that has a computationally efficient online form in which pairs of states and controls from given state and control trajectories are processed sequentially without being stored or processed in batches. We establish conditions guaranteeing the uniqueness of the objective-function parameters computed by our proposed method from trajectories with active control constraints. We illustrate our proposed method in simulation.