Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
- Award ID(s):
- 1931853
- Publication Date:
- NSF-PAR ID:
- 10287786
- Journal Name:
- 2021 American Control Conference (ACC)
- Page Range or eLocation-ID:
- 3882 to 3889
- Sponsoring Org:
- National Science Foundation
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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 »