.png){kind=icon}
.png){kind=icon}
Return
Data-Driven Control Design With LMIs and Dynamic Programming
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3241926.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The goal of this paper is to study model-free data-driven control evaluation and design strategies for discrete-time linear time-invariant systems, where the system model is unknown. In particular, our main contribution is twofold: 1) new state-input exploration and data collection schemes from experiences; 2) new data-driven linear matrix inequalities and dynamic programming methods for stabilization and optimal control problems. The proposed exploration and data collection schemes theoretically guarantee to acquire sufficient information from the system's state-input trajectories that can solve the underlying control design problems. We prove that under mild assumptions, as more and more data is accumulated, the collected data can solve the problems with higher probability along with the proposed algorithms.
Keywords:
Data collection
Symmetric matrices
Linear systems
Control design
Optimal control
Mathematical models
Dynamic programming
Data-driven design
reinforcement learning
optimal control
linear matrix inequality (LMI)
dynamic programming
linear time-invariant (LTI) system
