Sarah Dean, Stephen Tu, Nikolai Matni and Benjamin Recht

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2019-41
May 15, 2019

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-41.pdf

We study the constrained linear quadratic regulator with unknown dynamics, addressing the tension between safety and exploration in data-driven control techniques. We present a framework which allows for system identification through persistent excitation, while maintaining safety by guaranteeing the satisfaction of state and input constraints. This framework involves a novel method for synthesizing robust constraint-satisfying feedback controllers, leveraging newly developed tools from system level synthesis. We connect statistical results with cost sub-optimality bounds to give non-asymptotic guarantees on both estimation and controller performance.

Advisor: Benjamin Recht

BibTeX citation:

@mastersthesis{Dean:EECS-2019-41,
    Author = {Dean, Sarah and Tu, Stephen and Matni, Nikolai and Recht, Benjamin},
    Title = {Safely Learning to Control the Linear Quadratic Regulator},
    School = {EECS Department, University of California, Berkeley},
    Year = {2019},
    Month = {May},
    URL = {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-41.html},
    Number = {UCB/EECS-2019-41},
    Abstract = {We study the constrained linear quadratic regulator with unknown dynamics,
addressing the tension between safety and exploration in data-driven control techniques.
We present a framework which allows for system identification through persistent excitation,
while maintaining safety by guaranteeing the satisfaction of state and input constraints.
This framework involves a novel method for synthesizing robust constraint-satisfying feedback controllers, leveraging newly developed tools from 
system level synthesis.
We connect statistical results with cost sub-optimality bounds to give
non-asymptotic guarantees on both estimation and controller performance.}
}

EndNote citation:

%0 Thesis
%A Dean, Sarah
%A Tu, Stephen
%A Matni, Nikolai
%A Recht, Benjamin
%T Safely Learning to Control the Linear Quadratic Regulator
%I EECS Department, University of California, Berkeley
%D 2019
%8 May 15
%@ UCB/EECS-2019-41
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2019/EECS-2019-41.html
%F Dean:EECS-2019-41