Preprint
Real-Time Optimal Control via Transformer Networks and Bernstein Polynomials
ArXiv.org
Cornell University
11/19/2025
DOI: 10.48550/arxiv.2511.15588
Abstract
In this paper, we propose a Transformer-based framework for approximating solutions to infinite-dimensional optimization problems: calculus of variations problems and optimal control problems. Our approach leverages offline training on data generated by solving a sample of infinite- dimensional optimization problems using composite Bernstein collocation. Once trained, the Transformer efficiently generates near-optimal, feasible trajectories, making it well-suited for real-time applications. In motion planning for autonomous vehicles, for instance, these trajectories can serve to warm- start optimal motion planners or undergo rigorous evaluation to ensure safety. We demonstrate the effectiveness of this method through numerical results on a classical control problem and an online obstacle avoidance task. This data-driven approach offers a promising solution for real-time optimal control of nonlinear, nonconvex systems.
Details
- Title: Subtitle
- Real-Time Optimal Control via Transformer Networks and Bernstein Polynomials
- Creators
- Gage MacLinVenanzio CichellaAndrew PattersonIrene Gregory
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2511.15588
- ISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, New York
- Language
- English
- Date posted
- 11/19/2025
- Academic Unit
- Mechanical Engineering
- Record Identifier
- 9985033874302771
Metrics
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