TY - GEN
T1 - Training Neural Network Controllers Using Control Barrier Functions in the Presence of Disturbances
AU - Yaghoubi, Shakiba
AU - Fainekos, Georgios
AU - Sankaranarayanan, Sriram
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/20
Y1 - 2020/9/20
N2 - Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems. These feedback control methods typically compute the next control input by solving an online Quadratic Program (QP). Solving QPs in real-time can be a computationally expensive process for resource-constrained systems. In the presence of disturbances, finding CBF-based safe control inputs can get even more time consuming as finding the worst-case of the disturbance requires solving a nonlinear program in general. In this work, we propose to use imitation learning to learn Neural Network based feedback controllers which will satisfy the CBF constraints. In the process, we also develop a new class of High Order CBF for systems under external disturbances. We demonstrate the framework on a unicycle model subject to external disturbances, e.g., wind or currents.
AB - Control Barrier Functions (CBF) have been recently utilized in the design of provably safe feedback control laws for nonlinear systems. These feedback control methods typically compute the next control input by solving an online Quadratic Program (QP). Solving QPs in real-time can be a computationally expensive process for resource-constrained systems. In the presence of disturbances, finding CBF-based safe control inputs can get even more time consuming as finding the worst-case of the disturbance requires solving a nonlinear program in general. In this work, we propose to use imitation learning to learn Neural Network based feedback controllers which will satisfy the CBF constraints. In the process, we also develop a new class of High Order CBF for systems under external disturbances. We demonstrate the framework on a unicycle model subject to external disturbances, e.g., wind or currents.
KW - Barrier Function
KW - Disturbance
KW - Imitation Learning
KW - Neural Network Controller
UR - http://www.scopus.com/inward/record.url?scp=85099599359&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099599359&partnerID=8YFLogxK
U2 - 10.1109/ITSC45102.2020.9294485
DO - 10.1109/ITSC45102.2020.9294485
M3 - Conference contribution
AN - SCOPUS:85099599359
T3 - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
BT - 2020 IEEE 23rd International Conference on Intelligent Transportation Systems, ITSC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 23rd IEEE International Conference on Intelligent Transportation Systems, ITSC 2020
Y2 - 20 September 2020 through 23 September 2020
ER -