In this paper, the issue of model uncertainty in safety-critical control is addressed with a data-driven approach. For this purpose, we utilize the structure of an input-output linearization controller based on a nominal model along with a Control Barrier Function and Control Lyapunov Function based Quadratic Program (CBF-CLF-QP). Specifically, we propose a novel reinforcement learning framework which learns the model uncertainty present in the CBF and CLF constraints, as well as other control-affine dynamic constraints in the quadratic program. The trained policy is combined with the nominal model based CBF-CLF-QP, resulting in the Reinforcement Learning based CBF-CLF-QP (RL-CBF-CLF-QP), which addressesmore »
Multi-Rate Control Design Leveraging Control Barrier Functions and Model Predictive Control Policies
- Award ID(s):
- 1932091
- Publication Date:
- NSF-PAR ID:
- 10195971
- Journal Name:
- IEEE Control Systems Letters
- Volume:
- 5
- Issue:
- 3
- Page Range or eLocation-ID:
- 1007 to 1012
- ISSN:
- 2475-1456
- Sponsoring Org:
- National Science Foundation