Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Online learning of data-driven controllers for unknown switched linear
  systems   Motivated by the goal of learning controllers for complex systems whose
dynamics change over time, we consider the problem of designing control laws
for systems that switch among a finite set of unknown discrete-time linear
subsystems under unknown switching signals. To this end, we propose a method
that uses data to directly design a control mechanism without any explicit
identification step. Our approach is online, meaning that the data are
collected over time while the system is evolving in closed-loop, and are
directly used to iteratively update the controller. A major benefit of the
proposed online implementation is therefore the ability of the controller to
automatically adjust to changes in the operating mode of the system. We show
that the proposed control mechanism guarantees stability of the closed-loop
switched linear system provided that the switching is slow enough.
Effectiveness of the proposed design technique is illustrated for two aerospace
applications.

12
The categories of this paper are: nucl-th
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int