skip to main content

Title: Control of passivation and compensation in Mg-doped GaN by defect quasi Fermi level control
Authors:
 ;  ;  ;  ;  ;  ;  ;  
Award ID(s):
1653383
Publication Date:
NSF-PAR ID:
10131588
Journal Name:
Journal of Applied Physics
Volume:
127
Issue:
4
Page Range or eLocation-ID:
Article No. 045702
ISSN:
0021-8979
Publisher:
American Institute of Physics
Sponsoring Org:
National Science Foundation
More Like this
  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 addresses 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.