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