This work provides a decentralized approach to safety by
combining tools from control barrier functions (CBF) and
nonlinear model predictive control (NMPC). It is shown how
leveraging backup safety controllers allows for the robust
implementation of CBF over the NMPC computation horizon,
ensuring safety in nonlinear systems with actuation
constraints. A leader-follower approach to control barrier
functions (LFCBF) enforcement will be introduced as a
strategy to enable a robot leader, in a multi-robot
interactions, to complete its task in minimum time, hence
aggressively maneuvering. An algorithmic implementation of
the proposed solution is provided and safety is verified
via simulation.
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Control Barrier Functions for Complete and Incomplete Information Stochastic Systems
Real-time controllers must satisfy strict safety
requirements. Recently, Control Barrier Functions (CBFs) have
been proposed that guarantee safety by ensuring that a suitablydefined
barrier function remains bounded for all time. The
CBF method, however, has only been developed for deterministic
systems and systems with worst-case disturbances and
uncertainties. In this paper, we develop a CBF framework for
safety of stochastic systems. We consider complete information
systems, in which the controller has access to the exact system
state, as well as incomplete information systems where the
state must be reconstructed from noisy measurements. In the
complete information case, we formulate a notion of barrier
functions that leads to sufficient conditions for safety with
probability 1. In the incomplete information case, we formulate
barrier functions that take an estimate from an extended
Kalman filter as input, and derive bounds on the probability
of safety as a function of the asymptotic error in the filter. We
show that, in both cases, the sufficient conditions for safety can
be mapped to linear constraints on the control input at each
time, enabling the development of tractable optimization-based
controllers that guarantee safety, performance, and stability.
Our approach is evaluated via simulation study on an adaptive
cruise control case study.
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- Award ID(s):
- 1656981
- PAR ID:
- 10131729
- Date Published:
- Journal Name:
- American Control Conference (ACC)
- Page Range / eLocation ID:
- 2928-2935
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/https://doi.org/10.23919/ACC.2019.8814901
