Data-driven Estimation of the Algebraic Riccati Equation for the Discrete-Time Inverse Linear Quadratic Regulator Problem
<|reference_start|>Data-driven Estimation of the Algebraic Riccati Equation for the Discrete-Time Inverse Linear Quadratic Regulator Problem: In this paper, we propose a method for estimating the algebraic Riccati equation (ARE) with respect to an unknown discrete-time system from the system state and input observation. The inverse optimal control (IOC) problem asks, ``What objective function is optimized by a given control system?'' The inverse linear quadratic regulator (ILQR) problem is an IOC problem that assumes a linear system and quadratic objective function. The ILQR problem can be solved by solving a linear matrix inequality that contains the ARE. However, the system model is required to obtain the ARE, and it is often unknown in fields in which the IOC problem occurs, for example, biological system analysis. Our method directly estimates the ARE from the observation data without identifying the system. This feature enables us to economize the observation data using prior information about the objective function. We provide a data condition that is sufficient for our method to estimate the ARE. We conducted a numerical experiment to demonstrate that our method can estimate the ARE with less data than system identification if the prior information is sufficient.<|reference_end|>
@article{sugiura2024data-driven,
    title={Data-driven Estimation of the Algebraic Riccati Equation for the
  Discrete-Time Inverse Linear Quadratic Regulator Problem},
    author={Shuhei Sugiura, Ryo Ariizumi, Masaya Tanemura, Toru Asai, Shun-ichi
  Azuma},
    journal={arXiv preprint arXiv:2402.06180},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06180},
    primaryClass={math.OC cs.SY eess.SY}
}