Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Safe event-triggered Gaussian process
learning for barrier-constrained control
A. Lederer, A. Begzadić, S. Hirche,
J. Cortés, S. Herbert
IEEE Transactions on Automatic Control, submitted
Abstract
While control barrier functions (CBFs) are employed in addressing
safety, control synthesis methods based on them generally rely on
accurate system dynamics. This is a critical limitation, since the
dynamics of complex systems are often not fully known. Supervised
machine learning techniques hold great promise for alleviating this
weakness by inferring models from data. We propose a novel approach
for safe event-triggered learning of Gaussian process models in
CBF-based continuous-time control for unknown control-affine
systems. By applying a finite excitation at triggering times, our
approach ensures a sufficient information gain to maintain the
feasibility of the CBF-based safety condition with high
probability. Our approach probabilistically guarantees safety based on
a suitable GP prior and rules out Zeno behavior in the triggering
scheme. The effectiveness of the proposed approach and theory is
demonstrated in simulations.
pdf
Mechanical and Aerospace Engineering,
University of California, San Diego
9500 Gilman Dr,
La Jolla, California, 92093-0411
Ph: 1-858-822-7930
Fax: 1-858-822-3107
cortes at ucsd.edu
Skype id:
jorgilliyo