TY - GEN
T1 - Adaptive Robust Quadratic Programs using Control Lyapunov and Barrier Functions
AU - Zhao, Pan
AU - Mao, Yanbing
AU - Tao, Chuyuan
AU - Hovakimyan, Naira
AU - Wang, Xiaofeng
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/14
Y1 - 2020/12/14
N2 - This paper presents adaptive robust quadratic program (QP) based control using control Lyapunov and barrier functions for nonlinear systems subject to time-varying and state-dependent uncertainties. An adaptive estimation law is proposed to estimate the pointwise value of the uncertainties with pre-computable estimation error bounds. The estimated uncertainty and the error bounds are then used to formulate a robust QP, which ensures that the actual uncertain system will not violate the safety constraints defined by the control barrier function. Additionally, the accuracy of the uncertainty estimation can be systematically improved by reducing the estimation sampling time, leading subsequently to reduced conservatism of the formulated robust QP. The proposed approach is validated in simulations on an adaptive cruise control problem and through comparisons with existing approaches.
AB - This paper presents adaptive robust quadratic program (QP) based control using control Lyapunov and barrier functions for nonlinear systems subject to time-varying and state-dependent uncertainties. An adaptive estimation law is proposed to estimate the pointwise value of the uncertainties with pre-computable estimation error bounds. The estimated uncertainty and the error bounds are then used to formulate a robust QP, which ensures that the actual uncertain system will not violate the safety constraints defined by the control barrier function. Additionally, the accuracy of the uncertainty estimation can be systematically improved by reducing the estimation sampling time, leading subsequently to reduced conservatism of the formulated robust QP. The proposed approach is validated in simulations on an adaptive cruise control problem and through comparisons with existing approaches.
UR - http://www.scopus.com/inward/record.url?scp=85098828410&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098828410&partnerID=8YFLogxK
U2 - 10.1109/CDC42340.2020.9303829
DO - 10.1109/CDC42340.2020.9303829
M3 - Conference contribution
AN - SCOPUS:85098828410
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 3353
EP - 3358
BT - 2020 59th IEEE Conference on Decision and Control, CDC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 59th IEEE Conference on Decision and Control, CDC 2020
Y2 - 14 December 2020 through 18 December 2020
ER -