# Data-Driven Computation of Robust Invariant Sets and Gain-Scheduled Controllers for Linear Parameter-Varying Systems

arXiv: https://arxiv.org/abs/2309.01814 · HTML: https://mlsanity.com/paper/2309.01814/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Sep 2023.

## Built on by (most cited first, 1 of 1)

- [Data-Driven Synthesis of Probabilistic Controlled Invariant Sets for Linear MDPs](https://mlsanity.com/paper/2604.02727/) (2026)

## Abstract (arXiv)

We present a direct data-driven approach to synthesize robust control invariant (RCI) sets and their associated gain-scheduled feedback control laws for linear parameter-varying (LPV) systems subjected to bounded disturbances. A data-set consisting of a single state-input-scheduling trajectory is gathered from the system, which is directly utilized to compute polytopic RCI set and controllers by solving a semidefinite program. The proposed method does not require an intermediate LPV model identification step. Through a numerical example, we show that the proposed approach can generate RCI sets with a relatively small number of data samples when the data satisfies certain excitation conditions.