Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Data-driven synthesis of optimization-based
controllers for regulation of unknown linear
systems
G. Bianchin, M. Vaquero, J. Cortés, E. Dall'Anese
Proceedings of the IEEE Conference on Decision and Control, Austin, Texas, 2021,
pp. 5776-5781
Abstract
This paper proposes a data-driven framework to solve
time-varying optimization problems associated with
unknown linear dynamical systems. Making online control
decisions to steer a system to the solution trajectory
of a time-varying optimization problem is a central goal
in many modern engineering applications. Yet, the
available methods critically rely on a precise knowledge
of the system dynamics, thus requiring ad-hoc system
identification and model refinement phases. In this
work, we leverage tools from behavioral theory to show
that the steady-state transfer function of a system can
be computed from control experiments without knowledge
or estimation of the system model. Such direct
computation allows us to avoid the explicit model
identification phase, and is significantly more
tractable than the direct model-based computation. We
leverage the data-driven representation to design a
controller inspired from a gradient-descent method that
drives the system to the solution of an unconstrained
optimization problem, without any knowledge of
time-varying disturbances affecting the model
equation. Results are tailored to cost functions that
are smooth and satisfy the Polyak-Lojasiewicz
inequality. Simulation results illustrate the technical
findings.
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