Research paper
Dual-model LBMPC · Separating learning from robust constraint checks
One model learns improved performance while a nominal model with uncertainty bounds checks robust constraints. The author manuscript discusses quadrotor experiments involving ground effect and deliberately incorrect model updates.
Reviewed 2026-11-05 · Source and bibliography checked
- Original title
- Provably safe and robust learning-based model predictive control
- Publisher
- Elsevier BV
- Authors
- Anil Aswani, Humberto Gonzalez, S. Shankar Sastry, Claire Tomlin
- DOI
- 10.1016/j.automatica.2013.02.003
- Publication date
- 2013-05
- Source language
- en
- Edition
- Automatica; 2013; DOI 10.1016/j.automatica.2013.02.003
- Access
- Publisher metadata and open author manuscript
- Publication status
- Published
Related resources and engineering notes
Source overview
One model learns improved performance while a nominal model with uncertainty bounds checks robust constraints. The author manuscript discusses quadrotor experiments involving ground effect and deliberately incorrect model updates.
Use and review
Abstract and quadrotor application section checked. Robustness relies on uncertainty bounds, models and constraint assumptions; it does not cover every learning error or physical robot failure.
Sources & editions
Provably safe and robust learning-based model predictive control ↗
Elsevier BV · Automatica; 2013; DOI 10.1016/j.automatica.2013.02.003
Automatica bibliography; arXiv:1107.2487v2 abstract and High Performance Quadrotor Helicopter Flight section, https://arxiv.org/abs/1107.2487v2.