Online Inverse Optimal Control for Control-Constrained Discrete-Time Systems on Finite and Infinite Horizons
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 May 2020.
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Citing papers per year: 2023: 1; 2024: 1; 2025: 3.
5 papers.
- 2024A Differential Dynamic Programming Framework for Inverse Reinforcement Learningcited by 1
- 2023Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Gamescited by 0
- 2025Recursive Deep Inverse Reinforcement Learningcited by 0
- 2025PACE: A Framework for Learning and Control in Linear Incomplete-Information Differential Gamescited by 0
- 2025Peer-Aware Cost Estimation in Nonlinear General-Sum Dynamic Games for Mutual Learning and Intent Inferencecited by 0
- 2025Peer-Aware Cost Estimation in Nonlinear General-Sum Dynamic Games for Mutual Learning and Intent Inference23 Apr 2025
- 2025PACE: A Framework for Learning and Control in Linear Incomplete-Information Differential Games23 Apr 2025
- 2025Recursive Deep Inverse Reinforcement Learning17 Apr 2025
- 2024A Differential Dynamic Programming Framework for Inverse Reinforcement Learning29 Jul 2024
- 2023Data-Driven Inverse Reinforcement Learning for Expert-Learner Zero-Sum Games5 Jan 2023