TY - GEN
T1 - Risk-Bounded Control with Kalman Filtering and Stochastic Barrier Functions
AU - Yaghoubi, Shakiba
AU - Fainekos, Georgios
AU - Yamaguchi, Tomoya
AU - Prokhorov, Danil
AU - Hoxha, Bardh
N1 - Funding Information:
S. Yaghoubi, and G. Fainekos are with SCAI, Arizona State University, Tempe, AZ, USA. Email: <first name.last name>@asu.edu T. Yamaguchi, D. Prokhorov, and B. Hoxha are with the Toyota Research Institute of North America, Ann Arbor, MI, USA. Email: <first name.last name>@toyota.com This research was partially funded by NSF awards OIA 1936997 and CNS 1932068, and DARPA AMP N6600120C4020.
Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - In this paper, we study Stochastic Control Barrier Functions (SCBFs) to enable the design of probabilistic safe real-time controllers in presence of uncertainties and based on noisy measurements. Our goal is to design controllers that bound the probability of a system failure in finite-time to a given desired value. To that end, we first estimate the system states from the noisy measurements using an Extended Kalman filter, and compute confidence intervals on the filtering errors. Then, we account for filtering errors and derive sufficient conditions on the control input based on the estimated states to bound the probability that the real states of the system enter an unsafe region within a finite time interval. We show that these sufficient conditions are linear constraints on the control input, and, hence, they can be used in tractable optimization problems to achieve safety, in addition to other properties like reachability, and stability. Our approach is evaluated using a simulation of a lane-changing scenario on a highway with dense traffic.
AB - In this paper, we study Stochastic Control Barrier Functions (SCBFs) to enable the design of probabilistic safe real-time controllers in presence of uncertainties and based on noisy measurements. Our goal is to design controllers that bound the probability of a system failure in finite-time to a given desired value. To that end, we first estimate the system states from the noisy measurements using an Extended Kalman filter, and compute confidence intervals on the filtering errors. Then, we account for filtering errors and derive sufficient conditions on the control input based on the estimated states to bound the probability that the real states of the system enter an unsafe region within a finite time interval. We show that these sufficient conditions are linear constraints on the control input, and, hence, they can be used in tractable optimization problems to achieve safety, in addition to other properties like reachability, and stability. Our approach is evaluated using a simulation of a lane-changing scenario on a highway with dense traffic.
KW - Barrier Function
KW - Kalman Filter
KW - Robotics
KW - Uncertainty
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U2 - 10.1109/CDC45484.2021.9683756
DO - 10.1109/CDC45484.2021.9683756
M3 - Conference contribution
AN - SCOPUS:85126019350
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 5213
EP - 5219
BT - 60th IEEE Conference on Decision and Control, CDC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 60th IEEE Conference on Decision and Control, CDC 2021
Y2 - 13 December 2021 through 17 December 2021
ER -