.png){kind=icon}
.png){kind=icon}
Return
Online Stochastic Optimization for Unknown Linear Systems: Data-Driven Controller Synthesis and Analysis
.png){kind=icon}
PRE
AI
DOI：10.1109/TAC.2023.3323581.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This article proposes a data-driven control framework to regulate an unknown stochastic linear dynamical system to the solution of a stochastic convex optimization problem. Despite the centrality of this problem, most of the available methods critically rely on a precise knowledge of the system dynamics, thus requiring offline system identification. To solve the control problem, we first show that the steady-state gain of the transfer function of a linear system can be computed directly from historical data generated by the open-loop system, thus overcoming the need to first identify the full system dynamics. We leverage this data-driven representation of the steady-state gain to design a controller, which is inspired by stochastic gradient descent methods, to regulate the system to the solution of the prescribed optimization problem. A distinguishing feature of our method is that it does not require any knowledge of the system dynamics or of the possibly time-varying disturbances affecting them (or their distributions). Our technical analysis combines concepts from behavioral system theory, stochastic optimization with decision-dependent distributions, and Lyapunov stability. We illustrate the applicability of the framework in a case study for mobility-on-demand ride service scheduling in Manhattan.
Keywords:
Optimization
Stochastic processes
Control systems
Trajectory
Steady-state
Power system dynamics
Linear systems
Control design
data-driven control
learning systems
optimization methods
stochastic optimization
shared transport
Journal
IF:
7
Papers:
1.3W
Citations:
6.7W
View more organizations