3 research outputs found
Safe and Stable Adaptive Control for a Class of Dynamic Systems
Adaptive control has focused on online control of dynamic systems in the
presence of parametric uncertainties, with solutions guaranteeing stability and
control performance. Safety, a related property to stability, is becoming
increasingly important as the footprint of autonomous systems grows in society.
One of the popular ways for ensuring safety is through the notion of a control
barrier function (CBF). In this paper, we combine adaptation and CBFs to
develop a real-time controller that guarantees stability and remains safe in
the presence of parametric uncertainties. The class of dynamic systems that we
focus on is linear time-invariant systems whose states are accessible and where
the inputs are subject to a magnitude limit. Conditions of stability, state
convergence to a desired value, and parameter learning are all elucidated. One
of the elements of the proposed adaptive controller that ensures stability and
safety is the use of a CBF-based safety filter that suitably generates safe
reference commands, employs error-based relaxation (EBR) of Nagumo's theorem,
and leads to guarantees of set invariance. To demonstrate the effectiveness of
our approach, we present two numerical examples, an obstacle avoidance case and
a missile flight control case.Comment: 10 pages, 5 figures, IEEE CDC 202