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Title: Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
Authors:
; ;
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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  1. 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 »the problem of model uncertainty in the safety constraints. The performance of the proposed method is validated by testing it on an underactuated nonlinear bipedal robot walking on randomly spaced stepping stones with one step preview, obtaining stable and safe walking under model uncertainty.« less